Presenter: Marina Sirota, UCSF
Recent advances in genome typing and sequencing technologies have enabled quick generation of a vast amount of molecular data at very low cost. The mining and computational analysis of this type of data can help shape new diagnostic and therapeutic strategies in biomedicine. In this talk, I will discuss how such technological advances in combination with data science and integrative analysis can be applied to drug discovery in the context of drug target identification, computational drug repurposing, and population stratification approaches.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Short presentation for a special lecture on Medicine Graduation Course in Hospital de Clínicas (https://www.hcpa.edu.br/), as part of a one-day special discipline on Machine Learning and Healthcare. The goal was introducing the importance of Deep Learning for Healthcare as well as showing some of the recent impact.
Drug discovery and development is a long and expensive process over time has notoriously bucked Moore's law that it now has its own law called Eroom's Law named after it (the opposite of Moore). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of drug failures. Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible becomes all the more important to accelerate drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains. Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories: 1. Classification 2. Regression 3. Read-across. The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them. We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Short presentation for a special lecture on Medicine Graduation Course in Hospital de Clínicas (https://www.hcpa.edu.br/), as part of a one-day special discipline on Machine Learning and Healthcare. The goal was introducing the importance of Deep Learning for Healthcare as well as showing some of the recent impact.
Drug discovery and development is a long and expensive process over time has notoriously bucked Moore's law that it now has its own law called Eroom's Law named after it (the opposite of Moore). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of drug failures. Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible becomes all the more important to accelerate drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains. Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories: 1. Classification 2. Regression 3. Read-across. The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them. We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Big Data to avoid weather related flight delaysAkshatGiri3
This topic generally belongs to weather forecasting, how we will implement Big Data computing for future weather prediction so that weather Related Flight Delays get minimized.
Women Who Code-HSV Event:
'An Introduction to Machine Learning and Genomics'. Dr. Lasseigne will introduce the R programming language and the foundational concepts of machine learning with real-world examples including applications in the field of genomics with an emphasis on complex human disease research.
Brittany Lasseigne, PhD, is a postdoctoral fellow in the lab of Dr. Richard Myers at the HudsonAlpha Institute for Biotechnology and a 2016-2017 Prevent Cancer Foundation Fellow. Dr. Lasseigne received a BS in biological engineering from the James Worth Bagley College of Engineering at Mississippi State University and a PhD in biotechnology science and engineering from The University of Alabama in Huntsville. As a graduate student, she studied the role of epigenetics and copy number variation in cancer, identifying novel diagnostic biomarkers and prognostic signatures associated with kidney cancer. In her current position, Dr. Lasseigne’s research focus is the application of genetics and genomics to complex human diseases. Her recent work includes the identification of gene variants linked to ALS, characterization of gene expression patterns in schizophrenia and bipolar disorder, and development of non-invasive biomarker assays. Dr. Lasseigne is currently focused on integrating genomic data across cancers with functional annotations and patient information to explore novel mechanisms in cancer etiology and progression, identify therapeutic targets, and understand genomic changes associated with patient survival. Based upon those analyses, she is creating tools to share with the scientific community.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
AI in Clinical Trials: From Big Sky to Practical ApplicationVeeva Systems
See presentation slides from SCOPE Summit 2020.
Artificial Intelligence (AI) has made its way into the realm of clinical trials and is reshaping how studies are conducted. This presentation looks at the practical ways AI and process automation are being used effectively today to optimize trial design and execution. See this presentation for a look into how technology is revolutionizing the clinical operations landscape – from the smallest biotech to big pharma.
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
Deep learning in healthcare: Oppotunities and challenges with Electronic Medi...Thien Q. Tran
Interested in deep learning for healthcare has grown strongly recent years besides with the successes in other domains such as Computer Vision, Natural Language Processing, Speech Recognition and so forth. This talk will try to give a brief look into the recent effort of research in deep learning for healthcare. Especially, this talk focuses on the opportunities and challenges in using electronic health records (EHR) data, which is one of the most important data sources in healthcare domain.
Computational Drug Discovery: Machine Learning for Making Sense of Big Data i...Chanin Nantasenamat
In this lecture, I provide an overview on how computers can be instrumental in drug discovery efforts. Topics covered includes: big data as a result of omics effort; bioinformatics; cheminformatics; biological space; chemical space; how computers particularly machine learning (and data science) can be applied in the context of drug discovery.
A video of this lecture is also provided on the "Data Professor" YouTube channel available at http://bit.ly/dataprofessor
If you are fascinated about data science, it would mean the world to me if you would consider subscribing to this channel (by clicking the link below):
http://bit.ly/dataprofessor
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
I have framed this talk to encourage Pharmacy students to embrace computing in general, and data science and artificial intelligence techniques in particular. The reason is that data-driven science has overtaken traditional lab science; chemistry and biology that underlie pharmacy have become data-driven sciences, and a significant majority of the new jobs in pharma industries demand data analysis skills. Increasingly, traditional bioinformatics approaches are being complemented or replaced by machine learning or deep learning algorithms, especially for cases that have large data sets. I will provide a few examples (e.g., drug discovery, finding adverse drug reactions and broadly pharmacovigilance, and selecting patients for clinical trials) to demonstrate how big data and/or AI are indispensable to pharma research and industry today.
Digital RNAseq for Gene Expression Profiling: Digital RNAseq Webinar Part 2QIAGEN
Traditional RNA sequencing (RNA-Seq) is a powerful tool for expression profiling, but is hindered by PCR amplification bias and inaccuracy at low expressing genes. QIAseq RNA is a flexible and precise tool developed for mitigating these complications, allowing digital gene expression analysis. In this webinar we will cover, in depth, the sample requirements, experimental design, NGS platform specific challenges, and workflow for gene enrichment, library prep and sequencing. The applications of QIASeq RNA Panels in cancer research, stem cell differentiation and elucidating the effects small molecules on signaling pathways will be highlighted.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Big Data to avoid weather related flight delaysAkshatGiri3
This topic generally belongs to weather forecasting, how we will implement Big Data computing for future weather prediction so that weather Related Flight Delays get minimized.
Women Who Code-HSV Event:
'An Introduction to Machine Learning and Genomics'. Dr. Lasseigne will introduce the R programming language and the foundational concepts of machine learning with real-world examples including applications in the field of genomics with an emphasis on complex human disease research.
Brittany Lasseigne, PhD, is a postdoctoral fellow in the lab of Dr. Richard Myers at the HudsonAlpha Institute for Biotechnology and a 2016-2017 Prevent Cancer Foundation Fellow. Dr. Lasseigne received a BS in biological engineering from the James Worth Bagley College of Engineering at Mississippi State University and a PhD in biotechnology science and engineering from The University of Alabama in Huntsville. As a graduate student, she studied the role of epigenetics and copy number variation in cancer, identifying novel diagnostic biomarkers and prognostic signatures associated with kidney cancer. In her current position, Dr. Lasseigne’s research focus is the application of genetics and genomics to complex human diseases. Her recent work includes the identification of gene variants linked to ALS, characterization of gene expression patterns in schizophrenia and bipolar disorder, and development of non-invasive biomarker assays. Dr. Lasseigne is currently focused on integrating genomic data across cancers with functional annotations and patient information to explore novel mechanisms in cancer etiology and progression, identify therapeutic targets, and understand genomic changes associated with patient survival. Based upon those analyses, she is creating tools to share with the scientific community.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
AI in Clinical Trials: From Big Sky to Practical ApplicationVeeva Systems
See presentation slides from SCOPE Summit 2020.
Artificial Intelligence (AI) has made its way into the realm of clinical trials and is reshaping how studies are conducted. This presentation looks at the practical ways AI and process automation are being used effectively today to optimize trial design and execution. See this presentation for a look into how technology is revolutionizing the clinical operations landscape – from the smallest biotech to big pharma.
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
Deep learning in healthcare: Oppotunities and challenges with Electronic Medi...Thien Q. Tran
Interested in deep learning for healthcare has grown strongly recent years besides with the successes in other domains such as Computer Vision, Natural Language Processing, Speech Recognition and so forth. This talk will try to give a brief look into the recent effort of research in deep learning for healthcare. Especially, this talk focuses on the opportunities and challenges in using electronic health records (EHR) data, which is one of the most important data sources in healthcare domain.
Computational Drug Discovery: Machine Learning for Making Sense of Big Data i...Chanin Nantasenamat
In this lecture, I provide an overview on how computers can be instrumental in drug discovery efforts. Topics covered includes: big data as a result of omics effort; bioinformatics; cheminformatics; biological space; chemical space; how computers particularly machine learning (and data science) can be applied in the context of drug discovery.
A video of this lecture is also provided on the "Data Professor" YouTube channel available at http://bit.ly/dataprofessor
If you are fascinated about data science, it would mean the world to me if you would consider subscribing to this channel (by clicking the link below):
http://bit.ly/dataprofessor
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
I have framed this talk to encourage Pharmacy students to embrace computing in general, and data science and artificial intelligence techniques in particular. The reason is that data-driven science has overtaken traditional lab science; chemistry and biology that underlie pharmacy have become data-driven sciences, and a significant majority of the new jobs in pharma industries demand data analysis skills. Increasingly, traditional bioinformatics approaches are being complemented or replaced by machine learning or deep learning algorithms, especially for cases that have large data sets. I will provide a few examples (e.g., drug discovery, finding adverse drug reactions and broadly pharmacovigilance, and selecting patients for clinical trials) to demonstrate how big data and/or AI are indispensable to pharma research and industry today.
Digital RNAseq for Gene Expression Profiling: Digital RNAseq Webinar Part 2QIAGEN
Traditional RNA sequencing (RNA-Seq) is a powerful tool for expression profiling, but is hindered by PCR amplification bias and inaccuracy at low expressing genes. QIAseq RNA is a flexible and precise tool developed for mitigating these complications, allowing digital gene expression analysis. In this webinar we will cover, in depth, the sample requirements, experimental design, NGS platform specific challenges, and workflow for gene enrichment, library prep and sequencing. The applications of QIASeq RNA Panels in cancer research, stem cell differentiation and elucidating the effects small molecules on signaling pathways will be highlighted.
Sensor Data Wrangling: From Metal to CloudWrangleConf
Presenter: Sameera Poduri, Jawbone
Sensors are getting cheaper and are now deployed all around us, and the data streams from them can reveal patterns in behavior and a rich understanding of context to enable truly intelligent services. But where and how should we process this data -- close to the sensor hardware to minimize latency and communication costs while maintaining user privacy, or in the cloud, where combining data from multiple devices over long time periods can lead to richer context and deeper insights? In this talk, I will draw three examples from my experience working on robotics, mobile devices, and wearables to contrast the design of sensor processing algorithms at different points in this spectrum between device and cloud.
Condense Fact from the Vapor of Nuance WrangleConf
Presenter: Michael Conover, LinkedIn
Interesting, impactful problems are rarely easy to define, model, or evaluate - in fact, the very newness of a problem often suggests a scarcity of data and structured thinking about viable approaches. In this talk, we'll work through some of the challenges faced by LinkedIn's machine learning research teams in building and shipping intelligent systems that make sense of the world's economy. From creating novel training data and productionizing models with complex structure to evangelizing and evaluating the results of unsupervised algorithms, this talk will examine real-world case studies describing some of the hardest and most interesting challenges faced by one of the world's largest technology companies.
By Moritz Sudhof, Kanjoya
Data science is revolutionizing HR. With NLP and machine learning, organizations can now understand their employees automatically and in real-time. With NLP and machine learning, organizations no longer need armies of consultants and months of labor to determine what their employees want and need, to identify training initiatives that will maximize performance and leadership, and to surface and address critical issues before high performers leave. In this talk, Moritz explores how data science is used in organizations today and possibilities for the space in the future.
Wrangle 2016: Malware Tracking at ScaleWrangleConf
By Michael Bentley, Lookout
Historically, mobile-device malware detection has required security researchers to write a heuristic, then scan binaries for a match. Rinse, recycle, and repeat until the entire malware family can be detected. This approach has been effective, but it does not scale to Lookout’s challenge of analyzing more than 30 million applications. In this session, Michael explains how Lookout took an entirely different approach: using graph data modeling techniques. One significant outcome of this approach is a new data model that has the powerful ability to track variants of malware that are under active development. This model also allows Lookout to extract more metadata about malware families through the discovery of relationships that were previously unknown.
Wrangle 2016: Driving Healthcare Operations with Small DataWrangleConf
By Sandy Ryza, Clover Health
How do you get people with chronic heart conditions to take their medication? Or diagnose complications as early as possible? Healthcare operations--the set of actions that organizations like insurers take to interact with their members--sit in some sort of nebulous shadow realm between social science, medicine, and corporate bureaucracy. In this talk, Sandy will throw some additional nouns that seem more at home in the modern web era, like "machine learning" and "A/B testing," into the mix. He'll also walk attendees through an example of now Clover Health builds and tests models for predicting which of diabetic members are likely to develop complications.
Presenter: Cameron Turner, The Data Guild
We live at a time when data science, as sexy as it is, is too often still considered exotic and disconnected from the main line goals of organizations. How can we turn that model around to data science being a core engine of value and force for good in the world? I'll discuss our experiences at The Data Guild working with some of the largest and most innovative companies and nonprofits in the world and our approach to building a purposeful community around data product development.
Wrangle 2016 - Digital Vulnerability: Characterizing Risks and Contemplating ...WrangleConf
By Chris Diehl, The Data Guild
Our modern world represents an inextricable blend of the cyber and physical domains. Networked devices with sensing and computational capabilities continue to expand the extent of the cyber-physical interface, increasing risks across scales from the individual to the societal level. No longer do we fully understand the extent of what is being collected and assimilated about us. For marginalized populations globally, the consequences of this can be severe. What is the nature of the risk at the level of the individual? How can the data science community respond to improve the current reality? Chris will present some initial thoughts to frame the concerns and outline a way forward.
The Unreasonable Effectiveness of Product SenseWrangleConf
Presenter: Shubha Nabar, Salesforce
Statistics, machine learning and programming are commonly referenced tools of the data science trade. A less often talked about, but entirely indispensable skill for building great data products, is knowing what to build and why. In this talk, I will dispel some commonly prevalent myths related to building data products and touch on the increasing convergence between the skills of an effective data scientist and skills that have traditionally fallen in the realm of product management.
Wrangle 2016: Staying Hippocratic with High Stakes DataWrangleConf
By Abe Gong, Aspire Health
Abe has spent the last year building data systems to forecast personal medical calamities: hospitalization, debilitation, and death. This talk will share perspective from this experience, with two main goals:
Demystify the process of working with highly regulated medical data and legacy healthcare IT
Continue last year’s conversation about ethical algorithms and the potential harms of data work
Ultimately, all data is high-stakes data. Abe's hope is that discussing data science in a life-and-death medical context can further a community conversation about how to do no harm—and more more good—with data.
Wrangle 2016: (Lightning Talk) FizzBuzz in TensorFlowWrangleConf
By Joel Grus, AI2
FizzBuzz is a ubiquitous, nearly trivial problem used to weed out developer job applicants. Recently, Joel wrote a joking-not-joking blog post about a fictional interviewee who solves it using neural networks. After the blog post went viral, he spent a lot of time thinking about FizzBuzz as a machine-learning problem. It turns out, it's surprisingly interesting and subtle! Here, Joel talks about how and why.
A/B Testing at Pinterest: Building a Culture of Experimentation WrangleConf
Presenter: Andrea Burbank, Pinterest
A successful experimentation program consists of much more than mere randomization and measurement. How do you help stakeholders understand the right things to measure, avoid common pitfalls, and learn to rely on A/B tests as the best way to measure a new system or feature? In this talk, Andrea will explain how building a culture of experimentation and the right tools to support it is just as important as the statistics behind the comparisons themselves - and potentially much trickier to get right.
Biomedical big data and research clinical application for obesityHyung Jin Choi
1. What is Biomedical Big Data?
2. Biomedical Big Data
1) Genetic Data
2) Electrical Health Records
3) National Healthcare Data
4) Medical Images
5) Sensor/Mobile Data
6) Data Integration
3. Biomedical Big Data + Artificial Intelligence
4. Research/Clinical Application for Obesity
Translational Genomics towards Personalized medicine - Medhavi Vashisth.pptMedhavi27
Every individual is unique, and so is his/her body's affinity and reaction towards diseases and their treatment methods. The science of personalized takes into account biology of one individual at a time and relates it with established databases for devising or optimizing suitable treatment strategies.
BioVariance - Pediatric Pharmacogenomics in Drug DiscoveryJosef Scheiber
This slideset gives an overview of pharmacogenomic and pediatric dosing knowledge and various influence factors. Finally it shows an example on how to use this kind of Data within predictive approaches.
2015 04-13 Pharma Nutrition 2015 Philadelphia Alain van GoolAlain van Gool
Keynote lecture at the Pharma-Nutrition 2015 conference, outline global paradigm shifts and activities in pharma, personalized healthcare and pharmanutrition combination therapies.
iCAAD London 2019 - Antonio Metastasio - PERSONALISED MEDICINE IN THE TREATM...iCAADEvents
Personalised medicine is considered the next frontier of health care. The role of genetic testing in psychiatry and in addictions medicine, however, has been recently critically reviewed. Are genetic tests helpful in assessing and managing these conditions?
"Hacking the Software for Life" - Brad Perkins (Chief Medical Officer, Human ...Hyper Wellbeing
"Hacking the Software for Life" - Brad Perkins (Chief Medical Officer, Human Logevity, Inc.)
Delivered at the inaugural Hyper Wellbeing Summit, 14th November 2016, Mountain View, California.
For more information including details of subsequent events, please visit http://hyperwellbeing.com
The summit was created to foster a community around an emerging industry - Wellness as a Service (WaaS). Consumer technologies, in particular wearables and mobile, are powering a consumer revolution. A revolution to turn health and wellness into platform delivered services. A revolution enabling consumer data-driven disease risk reduction. A revolution extending health care past sick care towards consumer-led lifelong health, wellness and lifestyle optimization.
WaaS newsletter sign-up http://eepurl.com/b71fdr
@hyperwellbeing
Precision Medicine: Opportunities and Challenges for Clinical TrialsMedpace
The momentum and muscle behind "finding the right drug for the right patient at the right dose" has further escalated with President Barack Obama’s announcement of a $215 million dollar Precision Medicine Initiative earlier this year. In this webinar, Dr. Frank Smith will explore advances in precision medicine and how it is affecting clinical research. As a pediatric hematologist/oncologist, he will use his extensive clinical and research background as a backdrop for the discussion.
Topics will include:
The evolution of "personalized medicine" to "precision medicine"
How state-of-the-art molecular biology is creating new diagnostic and prognostic strategies
How these new strategies are helping inform the design of clinical trials
Case study: How precision medicine is improving clinical trials in hematology and oncology
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
4. Moore’s Law – Biology and
Computation
Cost Per Genome
Cost of Computational Resources
5. Can we use data integration
to…
Biomarker
Discovery
… to find
better
diagnostic
markers?
Disease
Mechanism
… understand
disease
better?
Therapeutics
… find new
uses for
existing
drugs?
6. Motivation
• Problem:
– Takes roughly 15 years and over
$800 million to develop and bring
a novel drug to market
– 90% of drugs fail in early
development
• Solution: Drug Repurposing
– Lower cost
– Reduce risk of failure
7. Problem Statement
Can we use public data to
systematically predict relationships
between drugs and diseases?
Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ. Discovery and Validation of
Drug Indications Using Compendia of Public Gene Expression Data. Science Translational Medicine. Aug 2011.
8. Problem Statement
Can we use public data to
systematically predict relationships
between drugs and diseases?
Diseases
Drugs
Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ. Discovery and Validation of
Drug Indications Using Compendia of Public Gene Expression Data. Science Translational Medicine. Aug 2011.
9. What is Gene Expression
Profiling?
• Global snapshot of cellular function and
activity
– Genome sequence – what might be going on
– Expression – what is actually going on
• 25,000 genes 1,000,000 proteins
• We can measure a few thousand proteins,
but gene expression is a global proxy
How Can We Measure Expression?
10. Microarrays
• Thousands of probes are hybridized to a solid
surface
• Takes advantage of complementary DNA
sequences
• Process:
– RNA is extracted from the sample
– Fluorescent labeling
– Hybridization and wash
– Scanning and signal processing
– Normalization and analysis!
11. Data Sources
• Collection of expression
data from cultured human
cells
• 453 experiments of 164
drugs
• Covers broad range of
effects
– FDA approved drugs
– Non drug bioactive small
molecules
• Publicly available
gene expression
repository
– Platforms – 11,745
– Samples – 961,202
– Series -39,679
• There are numerous
experiments dealing
with over 200
diseasesBarrett et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009.
Lamb et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.
Science. 2006.
12. Disease Gene Expression Data
(GEO)
Butte AJ, Chen R. AMIA,
2006.
Download all GDS
Experiments GEO
Identify Disease
Associated Experiments
Identify Normal vs.
Disease Experiments
176 datasets, 3113
arrays, 100 diseases
Dudley J, Butte AJ. PSB,
2008.
Dudley JT, Tibshirani R, Deshpande T, Butte AJ. Disease signatures are robust across tissues and experiments. Mol
14. Drug Gene Expression Profile
Treated
Sample
Untreated
Sample
Drug Gene Expression Profile
15. Up-regulated Down-regulated
Hypothesis
Gene Expression Profiles
Disease Drug BDisease Drug A Disease Drug C
Genes
Genes
Genes
Treatment Adverse Reaction
?
????
Lamb et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.
22. Crohn’s Disease
• An inflammatory disease of the intestines
that has an autoimmune component
• Affects 500,000 people in North America
• No known pharmaceutical cure
• Current solutions:
– Reduce inflammation with anti-
inflammatory drugs and
corticosteroids (prednisone)
– Bad side effects
– Surgical solutions
25. Topiramate – An Anti-Seizure
Drug
• Suppresses the rapid and excessive
firing of neurons that start a seizure
• Enhances GABA-activation
• Used to treat epilepsy, bipolar disorder
• Antidepressant
• Investigated as potential
treatment for obesity and
type II diabetes
26. Topiramate and Crohn’s
Genes that are
up-regulated by the drug are
down-regulated in the disease
Genes that are
down-regulated by the drug ar
up-regulated in the disease
27. Animal Model for Crohn’s
• TNBS (trinitrobenzene sulfonic acid) +
ethanol induced rats:
– Excellent and reproducible experimental model
for Inflammatory Bowel Disease (Crohn’s and
Ulcerative Colitis)
– Toxin-based model
Normal TNBS Induced
28. Pilot Validation Study Design
• Pilot Study – 18 rats
– Healthy (control)
– TNBS-Induced Untreated
– TNBS-Induced Treated
• 80 mg/kg topiramate, injected daily
• Colon tissue macroscopic damage score
Reetesh Pai, Mohan Shenoy and Pankaj Jay
36. Ongoing work
• Extending the drug datasets to use structural
data
• Incorporating meta-analysis methods
• Application to cancer (lung cancer, liver
cancer, medulloblastoma)
• More focused cell line selection
• Looking at dosage response and combination
therapy prediction
• Leveraging EMR and clinical trial dataChen B, Sirota M, Fan-Minogue H, Hadley D, Butte AJ. Relating Hepatocellular Carcinoma Tumor Samples and Cell
Lines Using Gene Expression Data in Translational Research. BMC Medical Genomics, 2015.
Wu M, Sirota M, Butte AJ, Chen B. Characteristics of drug combination therapy in oncology by analyzing clinical trial
data on clinicaltrials.gov. Pac Symp Biocomput. 2015.
37. Can we use data integration
to…
Biomarker
Discovery
… to find
better
diagnostic
markers?
Disease
Mechanism
… understand
disease
better?
Therapeutics
… find new
uses for
existing
drugs?
39. Acknowledgements
Atul Butte
Joel Dudley Annie P. Chiang
Alex Morgan Pankaj Jay Pasricha
Mohan Shenoy Minnie Sarwal
Reetesh Pai Julien Sage
Silke Roedder Alejandro Sweet-
Cordero
Bin Chen Hanna Paik
Dexter Hadley
Good morning my name is Marina Sirota. I’m currently a lead research scientist in the division of systems medicine at Stanford university. Previously I worked at Pfizer under David Cox in a genetics group working on applying next gen sequencing technologies to discover novel drug targets and develop population stratification techniques for clinical trials. Today I will tell you a bit about translational bioinformatics, systems medicine and how it might impact transplantation practice in the near future
Since then we have come a looong way. Thousands of people have been sequenced and millions of individuals have been genotyped. These are all resources that have been created and most importantly they are open to the public.
People are also starting to use sequencing in creative ways – immunome, metagenome, epigenetics cell-free DNA.
The observation made in 1965 by Gordon Moore, co-founder of Intel, that the number of transistors per square inch on integrated circuits had doubled every year since the integrated circuit was invented. Moore predicted that this trend would continue for the foreseeable future.
Drug repurposing is finding a novel indication for a known FDA approved drugs
Computational work can be instrumental in making this feasible
Examples: Viagra - was initially studied for use in hypertension
So far I have focused on the genetics piece or the DNA, I would like to talk a little bit about RNA or the middle piece of the central dogma and ways we can measure it using gene expression profiling
Expression profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment Used to generate hypothesis, mechanism of action
Post translational modification, alternative splicing
Microarrays are one technology to measure gene expression. They were first developed in 1995, nearly 20 years ago.
Have GEO data, but the format doesn’t make it easy to ask relevant questions
We have built an infrastructure to enable this sort of analysis
This approach is especially likely to yield good results for diseases with a strong gene dis-regulation component such as autoimmune disease or cancer
If the up-regulated disease genes appear near the top (up-regulated) of the rank-ordered drug gene expression list and the down-regulated disease genes fall near the bottom (down-regulated) of the rank-ordered drug gene expression list we can conclude that the drug and the disease expression profiles are similar
if the up-regulated disease genes fall near the bottom of the rank-ordered drug gene expression list and the down-regulated disease genes are near the top of the rank-ordered drug gene expression - therapeutic
Randomization by picking a signature at random and recomputing drug disease scores 100 times FDR
100 diseases 164 drugs
16000 drug-disease pairs
53 diseases significant predictions
Not everything is treatable
Hieararchical clustering
Brain cancers
Other Cancers
Lung Injury
UC and crohn’s
histone deacetylase (HDAC) inhibitors (in red)
Drugs known to affect different parts of the same pathway also cluster together:
phosphatidylinositol-3-kinase (PI3K) inhibitors LY−294002 and wortmannin (in green)
heat shock protein 90 (HSP90) inhibitors (in orange)
Chose Crohn’s but have others
Known drug
Two that are better
One is FDA approved so go for this one
Looked for an animal model of Crohn’s and found one
Define macroscopic damage score
Scale 0-6 what they mean
Define axes
Earlier this year President Obama launched a $215 million investment in Precision Medicine Initiative will pioneer a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.