We have created a large Neo4j database that integrates the results from text mining, experimental data and biological background knowledge. The utility of this graph is two fold:
- Identify promising compounds to be tested as a starting point for drug development.
- Better understand the results of large scale compound testing in cellular assays using imaging technology.
Currently the database contains 25 million article abstracts, data for 2 million compounds and 60000 genes – overall 29 million nodes and 270 million relationships.
We show some details about how the graph was built and show examples how combining text mining with experimental results leads to new insights and to better understanding and design in biological experiments.
Research by Mahendra Kumar Trivedi - Evaluation of the Impact of Biofield Tre...john henrry
Research on Trivedi Effect - In the present study, the influence of biofield treatment on physical and thermal properties of Casein Enzyme Hydrolysate (CEH) and Casein Yeast Peptone (CYP) were investigated. The control and treated samples were characterized by Fourier transform infrared (FT-IR) spectroscopy, differential scanning calorimetry (DSC), Thermo Gravimetric Analysis (TGA), particle size and surface area analysis.to read more visit http://www.academicroom.com/article/evaluation-impact-biofield-treatment-physical-and-thermal-properties-casein-enzyme-hydrolysate-and-casein-yeas-t-peptone
Research by Mahendra Kumar Trivedi - Evaluation of the Impact of Biofield Tre...Abby Keif
http://works.bepress.com/mahendra_trivedi/54/ - Research on Trivedi Effect - In the present study, the influence of biofield treatment on physical and thermal properties of Casein Enzyme Hydrolysate (CEH) and Casein Yeast Peptone (CYP) were investigated. The control and treated samples were characterized by Fourier transform infrared (FT-IR) spectroscopy, differential scanning calorimetry (DSC), Thermo Gravimetric Analysis (TGA), particle size and surface area analysis.
Research by Mahendra Kumar Trivedi - Evaluation of the Impact of Biofield Tre...john henrry
Research on Trivedi Effect - In the present study, the influence of biofield treatment on physical and thermal properties of Casein Enzyme Hydrolysate (CEH) and Casein Yeast Peptone (CYP) were investigated. The control and treated samples were characterized by Fourier transform infrared (FT-IR) spectroscopy, differential scanning calorimetry (DSC), Thermo Gravimetric Analysis (TGA), particle size and surface area analysis.to read more visit http://www.academicroom.com/article/evaluation-impact-biofield-treatment-physical-and-thermal-properties-casein-enzyme-hydrolysate-and-casein-yeas-t-peptone
Research by Mahendra Kumar Trivedi - Evaluation of the Impact of Biofield Tre...Abby Keif
http://works.bepress.com/mahendra_trivedi/54/ - Research on Trivedi Effect - In the present study, the influence of biofield treatment on physical and thermal properties of Casein Enzyme Hydrolysate (CEH) and Casein Yeast Peptone (CYP) were investigated. The control and treated samples were characterized by Fourier transform infrared (FT-IR) spectroscopy, differential scanning calorimetry (DSC), Thermo Gravimetric Analysis (TGA), particle size and surface area analysis.
Evaluation of the Impact of Biofield Treatment on Physical and Thermal Proper...wilhelm mendel
In the present study, the influence of biofield treatment on physical and thermal properties of Casein Enzyme Hydrolysate (CEH) and Casein Yeast Peptone (CYP) were investigated. The control and treated samples were characterized by Fourier transform infrared (FT-IR) spectroscopy, differential scanning calorimetry (DSC), Thermo Gravimetric Analysis (TGA), particle size and surface area analysis. The FTIR results revealed that biofield treatment has caused reduction of amide group (amide-I and amide-II) stretching vibration peak that is associated with strong intermolecular hydrogen bonding in treated CEH as compared to control. However, no significant changes were observed in FTIR spectrum of treated CYP. The TGA analysis of treated CEH showed a substantial improvement in thermal stability which was confirmed by increase in maximum thermal decomposition temperature (217°C) as compared to control (209°C). Similarly, the treated CYP also showed enhanced thermal stability as compared to control. DSC showed increase in melting temperature of treated CYP as compared to control. However the melting peak was absent in DSC of treated CEH which was probably due to rigid chain of the protein. The surface area of treated CEH was increased by 83% as compared to control. However, a decrease (7.3%) in surface area was observed in treated CYP. The particle size analysis of treated CEH showed a significant increase in average particle size (d50) and d99 value (maximum particle size below which 99% of particles are present) as compared to control sample. Similarly, the treated CYP also showed a substantial increase in d50 and d99 values which was probably due to the agglomeration of the particles which led to formation of bigger microparticles. The result showed that the biofield treated CEH and CYP could be used as a matrix for pharmaceutical applications.
Aspartame, a dietary sweetener, concentration dependently induces neurotoxici...Samson Ogbole
Aspartame, an artificial sweetener widely used in many foods and beverages, has shown controversies about the toxicity of its metabolite. Hence it is believed to be unsafe for human use. Garcinia kola, a herb grown in Nigeria with a characteristic astringent and bitter taste, is used in ethnomedicine in the treatment of laryngitis, cough, liver disease and dementia. It is known for its anti-inflammatory, antimicrobial and antiviral properties. This study was therefore designed to investigate the protective role of the essential oil of Garcinia kola (EOGK) on the toxicity of aspartame (ASP) on the brain of male Wistar rat
https://www.aasraw.com/products/azd-9291/
AZD-9291 powder is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) that binds to certain mutant forms of EGFR (T790M, L858R, and exon 19 deletion) that predominate in non-small cell lung cancer (NSCLC) tumours following treatment with first-line EGFR-TKIs.
The ADMET SIG meeting at SLAS2014, January 21 in San Diego, featured a presentation by SIG Chair David M. Stresser, Ph.D., of Corning® GentestSM Contract Research Services. View his presentation, Time-Dependent Inhibition of Cytochrome P450: A Deep Dive Into Methods for Abbreviated Testing, here.
Benchtop NMR of Adulterants in Sexual Enhancement and Weight-Loss Supplements...John Edwards
NMR utilization as screening tool for illegal adulteration of herbal supplements with pharmaceutical doses of viagra, tadalafil and their various analogs.
Presented at SMASH 2014, Atlanta, September 2014
Sulfoximine as rising stars in modern drug discovery PrashantChavan93
Sulfoximine first semester Credit presentation
Prashant Chavan (GPAT,NIPER Qualified)
M.S. (Pharm) in Medicinal Chemistry
National Institute of Pharmaceutical Education and Research Mohali, Punjab-160062 (India)
Mail ID- 20mcm_prashant@niper.ac.in
At the 7th World Congress of Diabetes Prevention and Its Complications, ISIC sponsored a session entitled, Good things in life: Can coffee help in diabetes prevention? Speakers at the conference session included Dr. Nathan Matusheski - Associate Principal Scientist, Mondelēz International.
See presentation for details
The Five Graphs of Government: How Federal Agencies can Utilize Graph TechnologyNeo4j
In this session from Neo4j Government Graphday, Philip Rathle discusses how federal agencies and contractors can utilize graphs to power their applications.
Accelerating Scientific Research Through Machine Learning and GraphNeo4j
Miroculus is a molecular diagnostics company that leverages the potential of microRNAs as biomarkers and has created the most easy-to-use and automated platform for their detection. MicroRNAs are small non-coding RNA molecules, whose primary role is to regulate the expression of our genes. Their discovery in circulation of body fluids such as blood plasma/serum, urine and saliva has been followed up by a multitude of studies, providing evidence that detection of specific microRNA molecules can give clues about a person’s health status and may therefore be used as biomarkers for various conditions.
Loom is an up-to-date snapshot of the scientific literature landscape focused on microRNAs that we built to expedite our own research. As of today, there is no compelling way to access much of the microRNA research. By using Loom's easy-to-use, interactive UI, the researcher is able to quickly locate the relevant sentences across many publications relating specific microRNAs with her disease or gene of interest. With this tool, our objective is to provide a visually compelling and complete overview of how microRNAs relate to specific diseases and genes.
At the backend, Loom is comprised of 4 microservices. The first one is a listener that fetches new publications daily that are available in the NCBI databases: PubMed for abstracts and PMC for full-text, open-access publications. Then, a natural language processor scans the publication, breaking them down into their constituent sentences and detecting mentions of microRNAs, genes and diseases.
Within each sentence, a machine learning scorer evaluates the strength and type of relationship on a scale from 0 to 1 and outputs the results in a graph database. The resulting graph database is then queried in real-time by the UI to retrieve the sentences and relationships the user is interested in.
Launched in 2009, Cisco’s Hierarchy Management Platform aimed at consolidating and improving master data management by creating a one-stop shop for Enterprise hierarchies. Fast forward seven years and the mission has expanded to something even more intriguing: utilizing cross-hierarchy relationships to simplify and automate Cisco’s functional processes. Enabled by Neo4j, these relationships (and graphical visualizations of these relationships) are fundamentally changing how Cisco conducts operations globally.
This discussion is intended for technical and non-technical audiences, focusing primarily on Enterprise hierarchy strategy, hierarchy data capabilities, and unlocking actionable business insights.
Evaluation of the Impact of Biofield Treatment on Physical and Thermal Proper...wilhelm mendel
In the present study, the influence of biofield treatment on physical and thermal properties of Casein Enzyme Hydrolysate (CEH) and Casein Yeast Peptone (CYP) were investigated. The control and treated samples were characterized by Fourier transform infrared (FT-IR) spectroscopy, differential scanning calorimetry (DSC), Thermo Gravimetric Analysis (TGA), particle size and surface area analysis. The FTIR results revealed that biofield treatment has caused reduction of amide group (amide-I and amide-II) stretching vibration peak that is associated with strong intermolecular hydrogen bonding in treated CEH as compared to control. However, no significant changes were observed in FTIR spectrum of treated CYP. The TGA analysis of treated CEH showed a substantial improvement in thermal stability which was confirmed by increase in maximum thermal decomposition temperature (217°C) as compared to control (209°C). Similarly, the treated CYP also showed enhanced thermal stability as compared to control. DSC showed increase in melting temperature of treated CYP as compared to control. However the melting peak was absent in DSC of treated CEH which was probably due to rigid chain of the protein. The surface area of treated CEH was increased by 83% as compared to control. However, a decrease (7.3%) in surface area was observed in treated CYP. The particle size analysis of treated CEH showed a significant increase in average particle size (d50) and d99 value (maximum particle size below which 99% of particles are present) as compared to control sample. Similarly, the treated CYP also showed a substantial increase in d50 and d99 values which was probably due to the agglomeration of the particles which led to formation of bigger microparticles. The result showed that the biofield treated CEH and CYP could be used as a matrix for pharmaceutical applications.
Aspartame, a dietary sweetener, concentration dependently induces neurotoxici...Samson Ogbole
Aspartame, an artificial sweetener widely used in many foods and beverages, has shown controversies about the toxicity of its metabolite. Hence it is believed to be unsafe for human use. Garcinia kola, a herb grown in Nigeria with a characteristic astringent and bitter taste, is used in ethnomedicine in the treatment of laryngitis, cough, liver disease and dementia. It is known for its anti-inflammatory, antimicrobial and antiviral properties. This study was therefore designed to investigate the protective role of the essential oil of Garcinia kola (EOGK) on the toxicity of aspartame (ASP) on the brain of male Wistar rat
https://www.aasraw.com/products/azd-9291/
AZD-9291 powder is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) that binds to certain mutant forms of EGFR (T790M, L858R, and exon 19 deletion) that predominate in non-small cell lung cancer (NSCLC) tumours following treatment with first-line EGFR-TKIs.
The ADMET SIG meeting at SLAS2014, January 21 in San Diego, featured a presentation by SIG Chair David M. Stresser, Ph.D., of Corning® GentestSM Contract Research Services. View his presentation, Time-Dependent Inhibition of Cytochrome P450: A Deep Dive Into Methods for Abbreviated Testing, here.
Benchtop NMR of Adulterants in Sexual Enhancement and Weight-Loss Supplements...John Edwards
NMR utilization as screening tool for illegal adulteration of herbal supplements with pharmaceutical doses of viagra, tadalafil and their various analogs.
Presented at SMASH 2014, Atlanta, September 2014
Sulfoximine as rising stars in modern drug discovery PrashantChavan93
Sulfoximine first semester Credit presentation
Prashant Chavan (GPAT,NIPER Qualified)
M.S. (Pharm) in Medicinal Chemistry
National Institute of Pharmaceutical Education and Research Mohali, Punjab-160062 (India)
Mail ID- 20mcm_prashant@niper.ac.in
At the 7th World Congress of Diabetes Prevention and Its Complications, ISIC sponsored a session entitled, Good things in life: Can coffee help in diabetes prevention? Speakers at the conference session included Dr. Nathan Matusheski - Associate Principal Scientist, Mondelēz International.
See presentation for details
The Five Graphs of Government: How Federal Agencies can Utilize Graph TechnologyNeo4j
In this session from Neo4j Government Graphday, Philip Rathle discusses how federal agencies and contractors can utilize graphs to power their applications.
Accelerating Scientific Research Through Machine Learning and GraphNeo4j
Miroculus is a molecular diagnostics company that leverages the potential of microRNAs as biomarkers and has created the most easy-to-use and automated platform for their detection. MicroRNAs are small non-coding RNA molecules, whose primary role is to regulate the expression of our genes. Their discovery in circulation of body fluids such as blood plasma/serum, urine and saliva has been followed up by a multitude of studies, providing evidence that detection of specific microRNA molecules can give clues about a person’s health status and may therefore be used as biomarkers for various conditions.
Loom is an up-to-date snapshot of the scientific literature landscape focused on microRNAs that we built to expedite our own research. As of today, there is no compelling way to access much of the microRNA research. By using Loom's easy-to-use, interactive UI, the researcher is able to quickly locate the relevant sentences across many publications relating specific microRNAs with her disease or gene of interest. With this tool, our objective is to provide a visually compelling and complete overview of how microRNAs relate to specific diseases and genes.
At the backend, Loom is comprised of 4 microservices. The first one is a listener that fetches new publications daily that are available in the NCBI databases: PubMed for abstracts and PMC for full-text, open-access publications. Then, a natural language processor scans the publication, breaking them down into their constituent sentences and detecting mentions of microRNAs, genes and diseases.
Within each sentence, a machine learning scorer evaluates the strength and type of relationship on a scale from 0 to 1 and outputs the results in a graph database. The resulting graph database is then queried in real-time by the UI to retrieve the sentences and relationships the user is interested in.
Launched in 2009, Cisco’s Hierarchy Management Platform aimed at consolidating and improving master data management by creating a one-stop shop for Enterprise hierarchies. Fast forward seven years and the mission has expanded to something even more intriguing: utilizing cross-hierarchy relationships to simplify and automate Cisco’s functional processes. Enabled by Neo4j, these relationships (and graphical visualizations of these relationships) are fundamentally changing how Cisco conducts operations globally.
This discussion is intended for technical and non-technical audiences, focusing primarily on Enterprise hierarchy strategy, hierarchy data capabilities, and unlocking actionable business insights.
Cloud system configurations and their dependencies can quickly grow into the thousands of virtual machine, network and storage components. Once software is included, the number of components can easily rise into six figures.
Frequent releases using continuous integration and deployment tools makes a repository of these components and relationships absolutely critical to cloud system integrity and quality of service no matter what cloud management tools you use.
Systems configurations are more naturally represented using a graph database than the relational representations used by traditional IT management products.
Our talk will explore how we use Neo4J to create a live, active, self-updating repository service, containing nearly all virtual hardware, network and software components and their dependencies, enabling continuous deployment in any cloud environment at scale.
Panama Papers and Beyond: Unveiling Secrecy with GraphsNeo4j
The media start-up International Consortium of Investigative Journalists has been breaking the secrecy surrounding tax havens for the past four years, but graph databases helped take their investigative power to the next level. Learn how their reporters used graphs to unveil patterns of crime and corruption in exposés like the Panama Papers and how millions of people have now become investigators by using the Neo4j-powered Offshore Leaks Database, one of the largest repositories of offshore companies in the world.
Knowledge Architecture: Graphing Your KnowledgeNeo4j
Ask any project manager and they will tell you the importance of reviewing lessons learned prior to starting a new project. The lesson learned databases are filled with nuggets of valuable information to help project teams increase the likelihood of project success. Why then do most lesson learned databases go unused by project teams? In my experience, they are difficult to search through and require hours of time to review the result set.
Recently I had a project engineer ask me if we could search our lessons learned using a list of 22 key terms the team was interested in. Our current keyword search engine would require him to enter each term individually, select the link, and save the document for review. Also, there was no way to search only the database, the query would search our entire corpus, close to 20 million URLs. This would not do. I asked our search team if they would run a special query against the lesson database only, using the terms provided. They returned a spreadsheet with a link to each document containing the terms. The engineer had his work cut out for him: over 1100 documents were on the list;.
I started thinking there had to be a better way. I had been experimenting with topic modeling, in particular to assist our users in connecting seemingly disparate documents through an easier visualization mechanism. Something better than a list of links on multiple pages. I gathered my toolbox: R/RStudio, for the topic modeling and exploring the data; Neo4j, for modeling and visualizing the topics; and Linkurious, a web front end for our users to search and visualize the graph database.
An Introduction to Container Organization with Docker Swarm, Kubernetes, Meso...Neo4j
Interest in Docker has increased significantly since its inception. According to a report compiled by a leading cloud-scale monitoring company, Datadog, two-thirds of the companies that try Docker adopt it, and the adopters have increased their container count by five times over a period of nine months. Neo4j has also embraced Docker by supporting official images and also offering specific images of its own.
While the interest in container technology is growing rapidly, so is the need to deploy containers over a cluster of machines to allow scalability and fault-tolerance. This highlights the need for orchestration which refers to the idea of automating the manual process of deploying, configuring and scaling the containers in an automated manner.
In this talk, we provide a hands-on introduction to the three most popular Docker orchestration tools: Kubernetes, Docker Swarm and Mesos. This talk offers a conceptual understanding of each of these technologies along with an insight into the concepts learned through a series of three demos. The demos will illustrate how to deploy and automatically scale a Neo4j container using each of the three orchestration platforms.
We realize that the scope of the topic in terms of the orchestration tools is too broad. The rationale behind choosing the three specific tools is based on the following two reasons: First is their potential use in our cluster at Cincinnati Children’s Hospital (CCHMC). Secondly, they also fall under the leading orchestration tools.
چند هفته ایست که ساخت طبقه دوم بزرگراه صدر به پایان رسیده است. از نگاه فنی و مدیریت اجرایی الحق جای تبریک و تحسین دارد. به جرات می توان گفت که نمونه چنین پروژه بزرگ شهری قبلا در سابقه پروژه های عمرانی شهر تهران انجام نگردیده است. قطعا مهمترین معیار انتخاب چنین سناریویی برای اجرا رفع گلوگاه های ترافیکی منطقه مربوطه بوده است و حتما ذیل چنین طرح مهمی تائید چندین مهندس مشاور ترافیک و حمل و نقل هم وجود دارد. در طول اجرای طرح و پس از افتتاح آن دیدگاه های مختلفی پیرامون اجرای این پروژه از نگاه متخصصین امر ارائه گردیده است. مجریان طرح با ذکر دلایلی و مستنداتی این سناریو را بهترین گزینه قلمداد کرده و پیش بینی می کنند که اثرات مثبتی در جریان حرکت خودروها در منطقه داشته باشد. از سوی دیگر گروهی از متخصصین حمل و نقل ترافیک هم اصولا با مقوله بزرگراه سازی مخالف بوده و در تاثیر چنین پروژه هایی بر روان سازی ترافیک تردید جدی دارند.
حال که این پروژه به بهره برداری رسیده است تب دوطبقه سازی بالا گرفته و پیش بینی می گردد که مسیرهای دیگری نیز از جمله بزرگراه بعثت , آزادگان و همت هم کاندیدای بعدی باشند. البته دیگر شهرها هم نسبت به این موضوع واکنش مثبت نشان داده و صحبت از اجرای پروژه های مشابه در شهرهای متبوع خود می نمایند.
حال سوال این است که از نگاه مدیریت شهری و توسعه پایدار در خصوص اجرای پروژه های توسعه معابر چه رویکردی را بایستی در پیش گرفت؟ نقش حمل و نقل عمومی و توسعه حمل و نقل ریلی شهری نظیر مترو در این بین چه می شود؟ در انتخاب بهترین سناریو مطالعات هزینه منفعت از نگاه شهری و شاید هم ملی چه جایگاهی دارد؟
خوب است دوستانی که در حوزه حمل و نقل و ترافیک دارای تجربه و مطالعاتی هم هستند در این خصوص مشارکت داشته باشند. باید بررسی شود که عملکرد بزرگراه دوطبقه صدر پس از افتتاح چگونه است و آیا صرف میلیارها تومان هزینه در مقایسه با دستاوردهای آن منطقی بوده است؟
Kdo zná minulost, vidí budoucnost, Tao = fáze kulminace a maxima, Proč neberu antidepresiva, bydlení: Udržujte pořádek, Energie dní: Týden od 10.10. 2016
Bioanalytical support plays a vital role during the lead optimization stages. The major goal of the bioanalysis is to assess the over-all ADME characteristics of the NCEs and biologics. Bioanalytical tools can play a significant role and impact the progress in drug discovery and development. Dramatic increases in investments in new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and ADC, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME and PK properties.https://www.medicilon.com/blog/featured-stories/dmpk-bioanalysis/
Annovis Bio is a clinical-stage, drug platform company addressing neurodegeneration, such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and Alzheimer’s in Down Syndrome (AD-DS). Annovis is believed to be the only company developing a drug for AD, PD and AD-DS that inhibits
more than one neurotoxic protein and improves the information highway of the nerve cell, known as axonal transport. When this information flow is impaired, the nerve cell gets sick and dies. The company expects its treatment to improve memory loss and dementia associated with AD and AD-DS, as well as body and brain function in PD. Annovis has an ongoing
Phase 2a study in AD patients and a second Phase 2a study in early PD and early AD patients.
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...Neo4j
Romain CAMPOURCY – Architecte Solution, Sopra Steria
Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...Neo4j
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Shirley Bacso, Data Architect, Ingka Digital
“Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxNeo4j
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanNeo4j
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
Follow us on: Pinterest
Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
HOT NEW PRODUCT! BIG SALES FAST SHIPPING NOW FROM CHINA!! EU KU DB BK substit...GL Anaacs
Contact us if you are interested:
Email / Skype : kefaya1771@gmail.com
Threema: PXHY5PDH
New BATCH Ku !!! MUCH IN DEMAND FAST SALE EVERY BATCH HAPPY GOOD EFFECT BIG BATCH !
Contact me on Threema or skype to start big business!!
Hot-sale products:
NEW HOT EUTYLONE WHITE CRYSTAL!!
5cl-adba precursor (semi finished )
5cl-adba raw materials
ADBB precursor (semi finished )
ADBB raw materials
APVP powder
5fadb/4f-adb
Jwh018 / Jwh210
Eutylone crystal
Protonitazene (hydrochloride) CAS: 119276-01-6
Flubrotizolam CAS: 57801-95-3
Metonitazene CAS: 14680-51-4
Payment terms: Western Union,MoneyGram,Bitcoin or USDT.
Deliver Time: Usually 7-15days
Shipping method: FedEx, TNT, DHL,UPS etc.Our deliveries are 100% safe, fast, reliable and discreet.
Samples will be sent for your evaluation!If you are interested in, please contact me, let's talk details.
We specializes in exporting high quality Research chemical, medical intermediate, Pharmaceutical chemicals and so on. Products are exported to USA, Canada, France, Korea, Japan,Russia, Southeast Asia and other countries.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Triangles of Neck and Clinical Correlation by Dr. RIG.pptx
Connecting the Dots in Early Drug Discovery
1. Novartis Institutes for BioMedical Research (NIBR)
Connecting the dots in
early drug discovery
Stephan Reiling
Senior Scientist, Novartis Institutes for
BioMedical Research
2. Connecting the dots in early
drug discovery
Stephan Reiling
In-Silico Lead Discovery Group
Novartis Institutes for BioMedical Research (NIBR) Cambridge
GraphConnect 2016, San Francisco
Novartis Institutes for
BioMedical Research
(NIBR)
3. Novartis Institutes for BioMedical Research (NIBR)
Why (might you be interested in this talk)
• The talk shows how a lot of heterogeneous data can be integrated into one big
graph
– Greater than the sum of its parts
• Text mining and pattern detection can lead to valuable insights
– Nobody can read 25 million scientific papers
• Data mining this graph can give novel biological insights
– Connecting the dots
Public3
4. Novartis Institutes for BioMedical Research (NIBR)
Why (did we build the graph)
Public4
Treatment effects in cellular phenotypic assays
Compound
treatment
5. Novartis Institutes for BioMedical Research (NIBR)
• What we have (the dots)
– almost 1 Billion data points of
compound activity data on protein
targets
(~99% of which can be summarized as “not active”)
– More and more results of phenotypic
assays
• What we lack (the connections)
– A good way to use biological
knowledge or background information
to make a connection
– A storage for “biological knowledge”
that can be “queried”
Public5
Why
Compound
Gene
Disease
(Phenotype)
6. Novartis Institutes for BioMedical Research (NIBR)
How (did we build the graph)
Public6
Text mining for chemicals, diseases, proteins
In continuation of our investigation on novel stearoyl-CoA desaturase (SCD) 1 inhibitors, we have already reported on the structural modification of the
benzoylpiperidines that led to a series of novel and highly potent spiropiperidine-based SCD1 inhibitors. In this report, we would like to extend the scope of our previous
investigation and disclose details of the synthesis, SAR, ADME, PK, and pharmacological evaluation of the spiropiperidines with high potency for SCD1 inhibition. Our
current efforts have culminated in the identification of 5-fluoro-1'-{6-[5-(pyridin-3-ylmethyl)-1,3,4-oxadiazol-2-yl]pyridazin-3-yl}-3,4-dihydrospiro[chromene-2,4'-
piperidine] (10e), which demonstrated a very strong potency for liver SCD1inhibition (ID(50)=0.6 mg/kg). This highly efficacious inhibition is presumed to be the result
of a combination of strong enzymatic inhibitory activity (IC(50) (mouse)=2 nM) and good oral bioavailability (F >95%). Pharmacological evaluation of 10e has
demonstrated potent, dose-dependent reduction of the plasma desaturation index in C57BL/6J mice on a high carbohydrate diet after a 7-day oral administration (q.d.).
In addition, it did not cause any noticeable skin abnormalities up to the highest dose (10 mg/kg).
7. Novartis Institutes for BioMedical Research (NIBR)
How (did we build the graph)
Public7
Text mining for chemicals, diseases, proteins
In continuation of our investigation on novel stearoyl-CoA desaturase (SCD) 1 inhibitors, we have already reported on the structural modification of the
benzoylpiperidines that led to a series of novel and highly potent spiropiperidine-based SCD1 inhibitors. In this report, we would like to extend the scope of our previous
investigation and disclose details of the synthesis, SAR, ADME, PK, and pharmacological evaluation of the spiropiperidines with high potency for SCD1 inhibition. Our
current efforts have culminated in the identification of 5-fluoro-1'-{6-[5-(pyridin-3-ylmethyl)-1,3,4-oxadiazol-2-yl]pyridazin-3-yl}-3,4-dihydrospiro[chromene-2,4'-
piperidine] (10e), which demonstrated a very strong potency for liver SCD1inhibition (ID(50)=0.6 mg/kg). This highly efficacious inhibition is presumed to be the result
of a combination of strong enzymatic inhibitory activity (IC(50) (mouse)=2 nM) and good oral bioavailability (F >95%). Pharmacological evaluation of 10e has
demonstrated potent, dose-dependent reduction of the plasma desaturation index in C57BL/6J mice on a high carbohydrate diet after a 7-day oral administration (q.d.).
In addition, it did not cause any noticeable skin abnormalities up to the highest dose (10 mg/kg).
Hit Type Recognized text Smiles
T1 GeneOrProtein stearoyl-CoA desaturase
T2 Mechanism inhibitors
T3 G benzoylpiperidines
T4 D spiropiperidine
O=C(NC(Cc1c[nH]c2ccccc12)C(=O)N3CCC4(CC3)CCc5ccccc45)NC6C
N7CCC6CC7
T5 GeneOrProtein SCD1
T6 Mechanism inhibitors
T7 GeneOrProtein SCD1
T8 M
5-fluoro-1'-{6-[5-(pyridin-3-ylmethyl)-1,3,4-oxadiazol-2-
yl]pyridazin-3-yl}-3,4-dihydrospiro[chromene-2,4'-
piperidine]
FC1=C2CCC3(OC2=CC=C1)CCN(CC3)C=3N=NC(=CC3)C=3OC(=NN3)
CC=3C=NC=CC3
T9 GeneOrProtein SCD1
T10 G carbohydrate
T11 Disease skin abnormalities
8. Novartis Institutes for BioMedical Research (NIBR)
How (did we build the graph)
• ~25,000,000 article abstracts
• 5,600 journals
• 1946 – current
Public8
National Institutes of Health (NIH) PubMed http://www.ncbi.nlm.nih.gov/pubmed
http://www.ncbi.nlm.nih.gov/pubmed/?term=20801551
• Tagged with “MeSH terms”
(MeSH: Medical Subject Heading)
9. Novartis Institutes for BioMedical Research (NIBR)
How
Public9
Structure of the MeSH term hierarchy (partial)
Yellow: Diseases
Blue: Processes and Mechanisms
Green: Anatomy
Red: Chemicals and Drugs
Grey: Organisms
12. Novartis Institutes for BioMedical Research (NIBR)
How
Public12
Association rule mining of co-occurrences
Article 1
• Compound A
• Gene 1
• Gene 2
Article 2
• Compound A
• Compound B
• Gene 1
Article 3
• Compound A
• Mesh term X
• Gene 1
Article 4
• Compound C
• Gene 1
• Identification of entities (compounds, mesh terms,
genes, diseases,…) from pubmed annotations or
textmining
• The a-priori algorithm from association rule mining
is used to identify frequently co-mentioned entities
(aka market basket analysis)
• Associations above a certain association strength
(lift) and number of articles in which they are co-
mentioned (support) are stored
• The association strength is scaled to 0-1 and
stored as the uncertainty of the association
(high lift = low uncertainty)
• Articles are stored as well, including the entities
that are mentioned in it
• This only captures the fact that something is
frequently co-mentioned with something else, not
any causality (similar to correlation)
13. Novartis Institutes for BioMedical Research (NIBR)
What (can you do with this)
Public13
Example: disease – compound – target from text mining
Every relationship in the graph has a property “uncertainty” in
the range of 0-1
This allows to query for connections with the highest confidence
Tafamidis (INN, or Fx-
1006A, trade
name Vyndaqel) is a drug
for the amelioration
of transthyretin-related
hereditary amyloidosis (also
familial amyloid
polyneuropathy, or FAP),
a rare but deadly
neurodegenerative disease.
Canavan disease is caused by a
defective ASPA gene which is
responsible for the production of
the enzyme aspartoacylase.
Decreased aspartoacylase activity
prevents the normal breakdown of
N-acetyl aspartate, wherein the
accumulation of N-acetylaspartate,
or lack of its further metabolism
interferes with growth of the myelin
sheath of the nerve fibers of the
brain.
From Wikipedia: From Wikipedia:
Color code: Disease, Gene, Compound
MATCH p =
(cpd:Compound) -[:is_associated]-> (g:Gene) -[:is_associated]-> (d:Disease) <-[:is_associated]- (cpd)
RETURN p, reduce(u=0.0, r in relationships(p) | u+r.uncertainty) as unc
ORDER BY unc
14. Novartis Institutes for BioMedical Research (NIBR)
What (can you do with this)
Public14
So why not just load Wikipedia?
Disease Uncertainty
Canavan Disease 0.1
Pelizaeus-Merzbacher Disease 0.364
Alexander Disease 0.432
Diffuse Axonal Injury 0.432
Brain Diseases, Metabolic 0.451
MATCH p = (cpd:Compound {name: 'N-acetylaspartate'}) -[r:is_associated]-> (m:Disease)
RETURN m.name as Disease, r.uncertainty as Uncertainty
ORDER BY r.uncertainty LIMIT 5
15. Novartis Institutes for BioMedical Research (NIBR)
What (can you do with this)
Public15
Now this is getting more interesting (for us)
MATCH p = (cpd:Compound {name: 'N-acetylaspartate'})
-[r:is_associated]-> (m:CellularComponent)
return m.name as CellularComponent, r.uncertainty as Uncertainty
ORDER BY r.uncertainty LIMIT 5
CellularComponent Uncertainty
Axons 0.582
Myelin Sheath 0.611
Extracellular Fluid 0.772
MATCH p = (cpd:Compound {name: 'N-acetylaspartate'})
-[r:is_associated]-> (m:BiologicalProcess)
RETURN m.name as BiologicalProcess, r.uncertainty as Uncertainty
ORDER BY r.uncertainty LIMIT 5
BiologicalProcess Uncertainty
Energy Metabolism 0.476
Dominance, Cerebral 0.532
Functional Laterality 0.586
Cerebrovascular Circulation 0.653
Lipid Metabolism 0.72
N-acetylaspartate association with
cellular components
N-acetylaspartate association with
biological processes
16. Novartis Institutes for BioMedical Research (NIBR)
Data sources:
1. MeSH Hierarchy
2. Pubmed articles, (pubmed_id, title,
abstract, Lucene full text searches
enabled)
3. Pubmed Associations
4. Comparative Toxicogenomics Database
(CTD)
5. Compound Target Scores*
6. Public compound annotations
7. Entity relations from sentences
8. Protein-protein interactions data set from
CCSB
9. MetaCore gene - gene interactions
(binds, activates, regulates expression, …)
10. Similarity relations for all the compounds in
the graph*
(~2M compounds)
11. Gene ontology
12. Protein annotations
13. Pathways / gene sets
Objects:
• 25,430,635 articles
• 1,951,819 compounds
• 257,000 Mesh and SCR
terms
• 59,859 Genes
• 24,769 GO terms
• 10,570 Diseases
Public16
How (did we build the graph)
Relationships:
91 different relationships
Compound - is_active – Gene
• X – is_associated – X
• Gene – binding – Gene
• Gene – ubiquitinates – Gene
• Compound – affects_ubiquitination – Gene
• Article – mentions – (compound, gene, mesh)
209,031,615 mentions
50,334,440 is_similar
6,951,257 literature_association
762,002 is_active
Other data sources integrated
(*: NIBR internal data)
See Acknowledgments / References slide
30 Million nodes 480 Million relationships
18. Novartis Institutes for BioMedical Research (NIBR)
How (did we build the graph)
Public18
Overall build process
MongoDB PostgreSQL
Pubmed
xml files
Internal data sources
MeSH hierarchies
ctdbase Pubchem
ChEMBL ChEBI
CCSB MetaStore
Information
extraction
Compound similarities
Gene sets
Protein annotations
Gene ontologies
CSV file
staging
Titles
Abstracts
• Information extraction
(entity recognition,
relationship detection,
association rule mining is
done on linux cluster)
• Neo4J “endpoint” focused
on graph mining
• MongoDB and PostgreSQL
are also used for
datamining purposes
Neo4J
19. Novartis Institutes for BioMedical Research (NIBR)
What (can you do with this)
Public19
Example: Analysis of compound activities
A
B
C
D
E
F
G
H
Active compounds Inactive compounds
20. Novartis Institutes for BioMedical Research (NIBR)
What
Public20
Example: Analysis of compound activities
A
B
C
D
E
F
G
H
2
5
1
4
3
6
Active compounds Inactive compounds
1. Find genes directly affected by
the compounds
21. Novartis Institutes for BioMedical Research (NIBR)
What
Public21
Example: Analysis of compound activities
A
B
C
D
E
F
G
H
2
8
5
1
4
9
3
6
7
10
Active compounds Inactive compounds
1. Find genes directly affected by
the compounds
2. Find all genes that are indirectly
affected with some confidence
(below a given uncertainyt)
22. Novartis Institutes for BioMedical Research (NIBR)
What
Public22
Example: Analysis of compound activities
A
B
C
D
E
F
G
H
2
8
5
1
4
9
3
6
7
10
Active compounds Inactive compounds
1. Find genes directly affected by
the compounds
2. Find all genes that are indirectly
affected with some confidence
(below a given uncertainty)
3. Assign nodes that can not be
reached a large distance
4. Identify nodes that
• can not be reached by
most of the inactive
compound
• or are “closer” to the
actives than the inactives
23. Novartis Institutes for BioMedical Research (NIBR)
What
Public23
Example: Analysis of compound activities
MATCH (cpd:Compound)
where any( nvs in cpd.cpd_id
where nvs in [‘cpd1’,’cpd2’,…])
WITH cpd
MATCH p = (cpd) -[r*1..2]-> (m)
WITH cpd, p, m, reduce(u=0.0,
r in relationships(p) | u+r.uncertainty
) as uncertainty
WHERE uncertainty < 0.9
RETURN
cpd.cpd_id as Compound_ID,
m.id as ID,
uncertainty as Distance
ORDER BY uncertainty
Query reachable nodes
Compound_ID Active C582554 C495901 C495900
1 0 1.00 1.00 1.00
2 1 0.78 0.89 0.88
3 1 1.00 1.00 1.00
4 0 1.00 1.00 1.00
5 0 1.00 0.78 0.67
6 0 1.00 1.00 1.00
7 0 1.00 1.00 1.00
8 0 0.88 0.88 0.90
9 0 1.00 0.88 0.82
10 1 1.00 1.00 1.00
11 0 1.00 1.00 1.00
12 0 1.00 0.80 0.83
13 0 1.00 1.00 1.00
14 1 1.00 1.00 1.00
15 1 0.82 1.00 1.00
16 1 0.78 0.89 0.88
17 1 0.80 1.00 1.00
18 1 0.80 1.00 1.00
19 1 0.78 0.89 0.88
20 1 0.80 1.00 1.00
Matrix of compound – node “distances” Result of recursive partitioning
(decision tree)
Sum of relationship uncertainty is used as
distance from compound to node
Distance to unreachable node is set to 1.0
( and one surrogate split with equivalent
performance: 2 nodes of interest )
24. Novartis Institutes for BioMedical Research (NIBR)
What
Public24
Example: Analysis of compound activities
Green:
relationships derived from
in-house data
Grey:
relationships found from
textmining
Compound
1
Compound
2
Compound
3
Compound
4
Compound
5
Compound
6
Compound
7
Compound
8
Compound
9
Compound
10
Compound
11
Compound
12
Compound
13
Only showing the active
compounds and their
connections to the
identified nodes.
25. Novartis Institutes for BioMedical Research (NIBR)
Public25
Compound
1
Compound
2
Compound
3
Compound
4
Compound
5
Compound
6
Compound
7
Compound
8
Compound
9
Compound
10
Compound
11
Compound
12
Compound
13
MATCH p = (g1:Gene) -[r*1..2 {datasource: 'metacore'}]-> (g2:Gene)
WHERE g2.gene_symbol in ['FOXO','MTOR']
and g1.gene_symbol in ['PRKAB1', 'PRKAA1','PRKAA2']
RETURN p, reduce(u=0.0, r in relationships(p) | u+r.uncertainty) as unc
ORDER BY unc LIMIT 20
26. Novartis Institutes for BioMedical Research (NIBR)
Public26
MATCH p = (g1:Gene) <-[:mentions]- (a:Article) -[:mentions]-> (g2:Gene)
WHERE g2.gene_symbol in ['FOXO','MTOR']
and g1.gene_symbol in ['PRKAB1', 'PRKAA1','PRKAA2']
RETURN p
MATCH p = (g1:Gene) -[r*1..2 {datasource: 'metacore'}]-> (g2:Gene)
WHERE g2.gene_symbol in ['FOXO','MTOR']
and g1.gene_symbol in ['PRKAB1', 'PRKAA1','PRKAA2']
RETURN p, reduce(u=0.0, r in relationships(p) | u+r.uncertainty) as unc
ORDER BY unc LIMIT 20
28. Novartis Institutes for BioMedical Research (NIBR)
Where (is this going)
• More tweaks to what we have
– Improvements to text mining
– Analysis of verbs (actions) / information extraction
– Monitor change over time (what is new “emerging knowledge”)
• Full text analysis
– Enable analysis and inclusion of internal documents
• Incorporate additional data sources
– Gene Expression data (tissue expression and perturbations)
– Mutations
– Proteomics
• Refining the “uncertainty” measure
– How best to compare uncertainties from different data sources
• Expand user base
• Automated updates
Public28
29. Novartis Institutes for BioMedical Research (NIBR)
• ISLD group
– John Davies
– Miguel Camargo
– Eugen Lounkine
– Elisabet Gregori-Puigjane
– Mark Bray
– Pierre Farmer
– Ansgar Schuffenhauer
• Text mining group
– Therese Vachon
– Pierre Parrisot
– Andrea Splendiani
– Fatima Oezdemir-Zaech
– Frederic Sutter
• Protein information:
– Pfam: R.D. Finn, et. al. The Pfam protein families database: towards a more sustainable future, Nucleic Acids
Research (2016) Database Issue 44:D279-D285
http://pfam.xfam.org/
– Uniprot: The UniProt Consortium, UniProt: a hub for protein information, Nucleic Acids Res. 43: D204-D212 (2015)
http://www.uniprot.org/
• Comparative Toxicogenomics database:
– Davis AP et. al. The Comparative Toxicogenomics Database's 10th year anniversary: update 2015. Nucleic Acids Res.
2015 Jan;43 (Database issue): D914-20.
Curated chemical–gene data were retrieved from the Comparative Toxicogenomics Database (CTD), MDI Biological
Laboratory, Salisbury Cove, Maine, and NC State University, Raleigh, North Carolina. World Wide Web (URL:
http://ctdbase.org/). [May 2016].
• MetaCore
– Thomson Reuters LifeSciences
http://thomsonreuters.com/en/products-services/pharma-life-sciences/pharmaceutical-research/metacore.html
• Protein-Protein interaction data set:
– Center for Cancer Systems Biology (CCSB) at the Dana Farber Cancer Institute
http://ccsb.dfci.harvard.edu/
• Gene Ontology
– The Gene Ontology Consortium. Gene Ontology Consortium: going forward. (2015) Nucl Acids Res 43 Database issue
D1049–D1056.
http://geneontology.org/
• Pathways
– Reactome pathway database:
A. Fabregat et. al., The Reactome pathway Knowledgebase, Nucl. Acids Res. (04 January 2016) 44 (D1): D481-D487
D. Croft et. al., The Reactome pathway knowledgebase, Nucl. Acids Res. (1 January 2014) 42 (D1): D472-D477
http://reactome.org/
Public29
Acknowledgments / References
Source References
• CPC
– Sylvain Cottens
– Doug Auld
• DMP
– Jeremy Jenkins
– Ben Cornett
– Florian Nigsch
• NX
– Stephen Litster