The document discusses how biological network topology can provide new insights into biological information. It notes that networks can model various biological interactions and relationships. The author argues that network topology may have a similar ground-breaking impact on understanding biology as genetic sequences. An example methodology is described for analyzing network topology through graphlet degree vectors and network alignment techniques to find conserved subnetworks. Results demonstrate that similar network topologies often correlate with shared biological functions, protein complexes and disease associations. Current work is exploring network analyses of G-protein coupled receptors and genetic interaction maps.
Presentation for Network Biology SIG 2013 by Gang Su, University of Michigan, USA. “CoolMap Cytoscape App: Flexible Multi-scale Heatmap-Driven Molecular Network Exploration”
Presentation for Network Biology SIG 2013 by Gang Su, University of Michigan, USA. “CoolMap Cytoscape App: Flexible Multi-scale Heatmap-Driven Molecular Network Exploration”
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Presentation for NetBio SIG 2013 by Martina Kutmon, PhD Researcher in the BiGCaT Bioinformatics Dept at the University of Maastricht in the Netherlands. “Building Biological Regulatory Networks in Cytoscape Using CyTargetLinker”
Network Biology: A paradigm for modeling biological complex systemsGanesh Bagler
These slides are part of the two lectures delivered at the as part of the 'National Workshop on Network Modelling and Graph Theory' (Dec 14-16, 2017) at Department of Mathematics, Dibrugarh University, Assam, India.
(1) Network Biology: A paradigm for integrative modeling of biological complex systems -- 14 Dec 2017, 3:30pm
(2) Applications of network modeling in biomedicine -- 15 Dec 2017, 9:00pm
Sponsored by UGC under SAP DRS (II)
(1) Workshop link: https://www.dibru.ac.in/upcoming-events/2981-national-workshop-on-network-modelling-and-graph-theory
(2) The Workshop Flyer: https://www.dibru.ac.in/images/uploaded_files/2017/Nov/National_Workshop_on_Network_Modelling_and_Graph_Theory.pdf
An information-theoretic, all-scales approach to comparing networksJim Bagrow
My presentation at NetSci 2018 on Portrait Divergence, a new approach to comparing networks that is simple, general-purpose, and easy to interpret.
The preprint: https://arxiv.org/abs/1804.03665
The code: https://github.com/bagrow/portrait-divergence
Introduction to graph databases and Neo4j for the bachelors student in Life sciences. Hands-on workshop for Neo4j and Cypher query language. The source of material for the hands-on training is: https://neo4j.com/graphacademy/online-training/introduction-to-neo4j/
Visualization and Analysis of Dynamic Networks Alexander Pico
DynNetwork development was taken up initially by Sabina Sara Pfister back in GSoC 2012. She laid out a strong foundation for dynamic network visualization in Cytoscape and my job was to extend the plugin’s functionality to help users analyse time changing networks. The two of us were mentored by Jason Montojo. We had developed a decent tool over the course of two GSoC programs to aid dynamic network analysis and our efforts culminated in DynNetwork getting accepted for an oral presentation at the International Network for Social Network Analysis (INSNA), Sunbelt 2014 which was held in St. Petersburg, FL in February.
Community Finding with Applications on Phylogenetic Networks [Extended Abstract]Luís Rita
[Master Thesis Extended Abstract]
With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics.
The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com).
Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 21st
Abstract. Novelty is an inherent part of innovations and discoveries. Such processes may be considered as the appearance of new ideas or as the emergence of atypical connections between existing ones. The importance of such connections hints for investigation of innovations through network or graph representation in the space of ideas. In such representation, a graph node corresponds to the relevant notion (idea), whereas an edge between two nodes means that the corresponding notions have been used in a common context. The question addressed in this research is the possibility to identify the edges between existing concepts where the innovations may emerge. To this end, a well-documented scientific knowledge landscape has been used. Namely, we downloaded 1.2M arXiv.org manuscripts dated starting from April 2007 and until September 2019; and extracted relevant concepts for them using ScienceWISE.info platform. Combining approaches developed in complex networks science and graph embedding the predictability of edges (links) on the scientific knowledge landscape where the innovations may appear is investigated. We argue that the conclusions drawn from this analysis may be used not only to the scientific knowledge analysis but are rather generic and may be applied to any domain that involves creativity within.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Presentation for NetBio SIG 2013 by Martina Kutmon, PhD Researcher in the BiGCaT Bioinformatics Dept at the University of Maastricht in the Netherlands. “Building Biological Regulatory Networks in Cytoscape Using CyTargetLinker”
Network Biology: A paradigm for modeling biological complex systemsGanesh Bagler
These slides are part of the two lectures delivered at the as part of the 'National Workshop on Network Modelling and Graph Theory' (Dec 14-16, 2017) at Department of Mathematics, Dibrugarh University, Assam, India.
(1) Network Biology: A paradigm for integrative modeling of biological complex systems -- 14 Dec 2017, 3:30pm
(2) Applications of network modeling in biomedicine -- 15 Dec 2017, 9:00pm
Sponsored by UGC under SAP DRS (II)
(1) Workshop link: https://www.dibru.ac.in/upcoming-events/2981-national-workshop-on-network-modelling-and-graph-theory
(2) The Workshop Flyer: https://www.dibru.ac.in/images/uploaded_files/2017/Nov/National_Workshop_on_Network_Modelling_and_Graph_Theory.pdf
An information-theoretic, all-scales approach to comparing networksJim Bagrow
My presentation at NetSci 2018 on Portrait Divergence, a new approach to comparing networks that is simple, general-purpose, and easy to interpret.
The preprint: https://arxiv.org/abs/1804.03665
The code: https://github.com/bagrow/portrait-divergence
Introduction to graph databases and Neo4j for the bachelors student in Life sciences. Hands-on workshop for Neo4j and Cypher query language. The source of material for the hands-on training is: https://neo4j.com/graphacademy/online-training/introduction-to-neo4j/
Visualization and Analysis of Dynamic Networks Alexander Pico
DynNetwork development was taken up initially by Sabina Sara Pfister back in GSoC 2012. She laid out a strong foundation for dynamic network visualization in Cytoscape and my job was to extend the plugin’s functionality to help users analyse time changing networks. The two of us were mentored by Jason Montojo. We had developed a decent tool over the course of two GSoC programs to aid dynamic network analysis and our efforts culminated in DynNetwork getting accepted for an oral presentation at the International Network for Social Network Analysis (INSNA), Sunbelt 2014 which was held in St. Petersburg, FL in February.
Community Finding with Applications on Phylogenetic Networks [Extended Abstract]Luís Rita
[Master Thesis Extended Abstract]
With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics.
The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com).
Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 21st
Abstract. Novelty is an inherent part of innovations and discoveries. Such processes may be considered as the appearance of new ideas or as the emergence of atypical connections between existing ones. The importance of such connections hints for investigation of innovations through network or graph representation in the space of ideas. In such representation, a graph node corresponds to the relevant notion (idea), whereas an edge between two nodes means that the corresponding notions have been used in a common context. The question addressed in this research is the possibility to identify the edges between existing concepts where the innovations may emerge. To this end, a well-documented scientific knowledge landscape has been used. Namely, we downloaded 1.2M arXiv.org manuscripts dated starting from April 2007 and until September 2019; and extracted relevant concepts for them using ScienceWISE.info platform. Combining approaches developed in complex networks science and graph embedding the predictability of edges (links) on the scientific knowledge landscape where the innovations may appear is investigated. We argue that the conclusions drawn from this analysis may be used not only to the scientific knowledge analysis but are rather generic and may be applied to any domain that involves creativity within.
Coupling Australia’s Researchers to the Global Innovation EconomyLarry Smarr
08.10.10
Fifth Lecture in the
Australian American Leadership Dialogue Scholar Tour
University of Queensland
Title: Coupling Australia’s Researchers to the Global Innovation Economy
Brisbane, Australia
Understanding the Big Picture of e-ScienceAndrew Sallans
A. Sallans. "Understanding the Big Picture of e-Science." Presented at the 2011 eScience Bootcamp at the University of Virginia's Claude Moore Health Sciences Library. 4 March 2011
What's wrong with our scholarly infrastructure?Björn Brembs
First of a two-part series on the issues scientists face with their expensive, antiquated infrastructure and how to overcome these problems. First part on problems, second part (upcoming) on solutions.
Citizen Science and Rare Disease ResearchAndrew Su
Talk given at "Personalized Health in the Digital Age" September 22, 2016 at Campus Biotech in Geneva, Switzerland https://www.personalizedhealth2016.ch/
Conference:
4th International Workshop on Networks of Cooperating Objects for Smart Cities 2013 (CONET/UBICITEC 2013)
Title of the paper:
Smart Lighting in Multipurpose Outdoor Environments: Energy Efficient Solution using Network of Cooperating Objects
Authors:
Anna Florea
Ahmed Farahat
Dr. Corina Postelnicu
Prof. Jose L. Martinez Lastra, Dr.Sc.
Prof. Francisco J. Azcondo Sánchez
If you would like to receive a reprint of the original paper, please contact us
Keynote presentation for Network Biology SIG 2013 by Esti Yeger-Lotem, Senior Lecturer in Clinical Biochemistry at The National Institute for Biotechnology in the Negev, Israel
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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!
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
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
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
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.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Title: Sense of Smell
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 primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
1. July 19, 2013
Nataša Pržulj
Network Topology as a
Source of Biological
Information
Imperial College London
Department of Computing
2. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Networks → biological information
2
Networks can model:
gene interactions
protein structure
protein-protein interactions
metabolism
…
3. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model:
gene interactions
protein structure
protein-protein interactions
metabolism
…
Networks → biological information
4. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model:
gene interactions
protein structure
protein-protein interactions
metabolism
…
Networks → biological information
5. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model:
gene interactions
protein structure
protein-protein interactions
metabolism
…
Networks → biological information
6. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
2
Networks can model:
gene interactions
protein structure
protein-protein interactions
metabolism
…
Turning point in biology and bioinformatics
Advances in experimental biology data
Interesting & important problems to CS
Computational advances contribute:
Biological understanding (disease, pathogens, aging)
Therapeutics healthcare benefits (e.g., GSK)
Booming research area
Networks → biological information
7. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological information
• Based on results (ERC, NSF): topology ↔ biology
Genetic Sequence:
● Revolutionized our understanding of:
Biology
Diseases
Evolution
Networks:
● Similar ground-breaking impact
3
Networks → biological information
8. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological information
• Based on results (ERC, NSF): topology ↔ biology
Genetic Sequence:
● Revolutionized our understanding of:
Biology
Diseases
Evolution
Networks:
● Similar ground-breaking impact
3
Networks → biological information
9. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological information
• Based on results (ERC, NSF): topology ↔ biology
Genetic Sequence:
● Revolutionized our understanding of:
Biology
Diseases
Evolution
Networks:
● Similar ground-breaking impact
3
Networks → biological information
100% sequence identity
65% network wiring similarity
Degrees 54 and 9
V. Memisevic, T. Milenkovic and N. Przulj, J. Integrative Bioinformatics, 7(3):135,2010.
10. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological information
• Based on results (ERC, NSF): topology ↔ biology
Genetic Sequence:
● Revolutionized our understanding of:
Biology
Diseases
Evolution
Networks:
● Similar ground-breaking impact
3
Networks → biological information
100% sequence identity
65% network wiring similarity
Degrees 54 and 9
V. Memisevic, T. Milenkovic and N. Przulj, J. Integrative Bioinformatics, 7(3):135,2010.
11. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Idea:
• Network topology – new source of biological information
• Based on results (ERC, NSF): topology ↔ biology
Need tools to mine networks
Why?
● Analysing sequences is “computationally easy” (polynomial time)
● Analysing networks (i.e., graphs) is “computationally hard”
E.g., Is X sub-network of Y? ̶ Computationally intractable
Cannot exactly compare / align biological networks
heuristics (approximate solutions)
3
Networks → biological information
100% sequence identity
65% network wiring similarity
Degrees 54 and 9
V. Memisevic, T. Milenkovic and N. Przulj, J. Integrative Bioinformatics, 7(3):135,2010.
12. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology:
4
N. Pržulj, Bioinformatics, 23:e117-e183, 2007.
Networks → biological information
13. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology:
4
N. Pržulj, Bioinformatics, 23:e117-e183, 2007.
Networks → biological information
Graphlet Degree Vector (GDV) of node u:
GDV(u) = (u0, u1, u2, …, u72)
14. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology:
4
Why?
Edge too simplistic controversies, e.g.:
→ network structure / models: scale-free?
→ hub proteins: lethal?
→ …
Frustration: network analyses useless?
N. Pržulj, Bioinformatics, 23:e117-e183, 2007.
Networks → biological information
Graphlet Degree Vector (GDV) of node u:
GDV(u) = (u0, u1, u2, …, u72)
15. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → Results:
5
Why?
Edge too simplistic controversies, e.g.:
→ network structure / models: scale-free?
→ hub proteins: lethal?
→ …
Frustration: network analyses useless?
90% similar topology ↔
significantly enriched:
→ Biological function
→ Protein complexes
→ Sub-cellular localization
→ Tissue expression
→ Disease
1. T. Milenković & N. Pržulj, Cancer Informatics, 4:257-273, 2008. (Highly visible)
Networks → biological information
SMD1
SMB1RPO26
16. 5
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → Results: Why?
Edge too simplistic controversies, e.g.:
→ network structure / models: scale-free?
→ hub proteins: lethal?
→ …
Frustration: network analyses useless?
Cancer research:
→ Find new members of melanin production
pathways: phenotypically validated (siRNA)
→ Same cancer type - more similar topology in
PPI net
→ Could not have been identified by existing
approaches
2. T. Milenković, V. Memisević, A. K. Ganesan, and N. Pržulj, J. Roy. Soc. Interface, 7(44):423-437, 2010.
3. H. Ho, T. Milenković, V. Memisević, J. Aruri, N. Pržulj, and A. K. Ganesan, BMC Systems Biology, 4:84, 2010. (Highly accessed)
Networks → biological information
17. 55
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology → Results: Why?
Edge too simplistic controversies, e.g.:
→ network structure / models: scale-free?
→ hub proteins: lethal?
→ …
Frustration: network analyses useless?
Find new members of yeast proteosome
PPI network
4. C. Guerrero, T. Milenkovic, N. Przulj, P. Keiser, L. Huang, “Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and
protein interaction network analysis,” PNAS, 105 (36), pg. 13333-13338 2008.)
Networks → biological information
18. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology:
Network alignment – approximate subnetwork finding
6
Networks → biological information
19. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology GRAAL family:→
Network alignment – approximate subnetwork finding
Why?
Analogous to sequence alignment
Predict function, disease − by knowledge transfer
Evolution − global similarity between networks of different species
Problems:
Noise in the data all methods must be→ robust to noise
Computational intractability computational problems:→
Node similarity function?
Alignment search algorithm?
How to measure “goodness” of an inexact fit between networks?
…
6
V. Memisevic & N. Przulj, Integrative Biology, doi:10.1039/c2ib00140c, 2012
O. Kuchaiev & N. Pržulj, Bioinformatics, 27(10): 1390-6, 2011
O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, & N. Pržulj, J. Royal Society Interface, 7:1341-1354, 2010
T. Milenkovic, W.L. Wong, W. Hayes, & N. Pržulj, Cancer Informatics, 9:121-37, June 30, 2010 (Highly visible)
Networks → biological information
20. GRAAL:
267 nodes and 900 edges
Isorank:
116 nodes and 261 edges
MI-GRAAL:
1,858 nodes and 3,467 edges
6
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology GRAAL family:→
Network alignment – approximate subnetwork finding
V. Memisevic & N. Przulj, Integrative Biology, doi:10.1039/c2ib00140c, 2012
O. Kuchaiev & N. Pržulj, Bioinformatics, 27(10): 1390-6, 2011
O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, & N. Pržulj, J. Royal Society Interface, 7:1341-1354, 2010
T. Milenkovic, W.L. Wong, W. Hayes, & N. Pržulj, Cancer Informatics, 9:121-37, June 30, 2010 (Highly visible)
Networks → biological information
21. 6
GRAAL:
267 nodes and 900 edges
Isorank:
116 nodes and 261 edges
MI-GRAAL:
1,858 nodes and 3,467 edges
6
Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Example Methodology GRAAL family:→
Network alignment – approximate subnetwork finding
V. Memisevic & N. Przulj, Integrative Biology, doi:10.1039/c2ib00140c, 2012
O. Kuchaiev & N. Pržulj, Bioinformatics, 27(10): 1390-6, 2011
O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes, & N. Pržulj, J. Royal Society Interface, 7:1341-1354, 2010
T. Milenkovic, W.L. Wong, W. Hayes, & N. Pržulj, Cancer Informatics, 9:121-37, June 30, 2010 (Highly visible)
Networks → biological information
R. Patro and C. Kingsford. Global network alignment using multiscale
spectral signatures. Bioinformatics 28(23):3105-3114 (2012).
Dr. Noel Malod-Dognin, GrAlign + Poster - L103
22. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
7
Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
2. Genetic Interaction Maps (with Prof. Charlie Boone, University of Toronto)
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
4. New graphlet-based measures: disease associations and network dynamics
23. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8
Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry
Stagljar lab: new network of 50 human GPCRs and their interactors
Analysis of it in the context of the entire human PPI network
Analysis of this new network
Predictions of new GPCRs
Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
24. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8
Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry
Stagljar lab: new network of 50 human GPCRs and their interactors
Analysis of it in the context of the entire human PPI network
Analysis of this new network
Predictions of new GPCRs
“spine” of the network
functionally separates the cell
topologically separates the cell
Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
25. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8
Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry
Stagljar lab: new network of 50 human GPCRs and their interactors
Analysis of it in the context of the entire human PPI network
Analysis of this new network
Predictions of new GPCRs
“core” of the network
25 disease genes:
mostly brain disorders
Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
Vuk, Anida:
Poster - O065
Poster - O046
26. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
8
Networks → biological information
Some current development:
1. G-protein coupled receptors (with Prof. Igor Stagljar, University of Toronto)
Robert Lefkowitz and Brian Kobilka – 2012 Nobel Prize in Chemistry
Stagljar lab: new network of 50 human GPCRs and their interactors
Analysis of it in the context of the entire human PPI network
Analysis of this new network
Predictions of new GPCRs
11 proteins “similar” to 6 GPCRs
Predicted new GPCRs:
e.g., chromosome 20 open reading
frame 39 (TMEM90B)
Skolina et al., “Systematic interactome building of 50 clinically relevant human GPCRs: a resource for cell signalling research,” submitted
Vuk, Anida:
Poster - O065
Poster - O046
27. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
9
Networks → biological information
Some current development:
2. Genetic Interaction Maps (with Prof. Charlie Boone, University of Toronto)
28. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
29. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
New method for integration / fusion of molecular network data
Currently primitive “projection” methods
Purely descriptive
Provide no conceptual framework for predictions
Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
30. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
New method for integration / fusion of molecular network data
Based on:
matrix representation of the data
their fusion by:
simultaneous matrix factorization and
mining of the resulting decomposition
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.
4 Objects: Genes, GO terms, DO terms, Drugs
Constraints: Ѳi
Relation matrices: Rij
Zitnik, Janjic, Larminie, Zupan and Przulj, “Discovering disease associations by fusing systems-level molecular data,” submitted
31. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
New method for integration / fusion of molecular network data
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.
32. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Alg. 1: Data fusion by matrix factorization:
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.
33. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Alg. 2: Disease class and association prediction:
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.
34. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Some Results:
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.
35. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
10
Networks → biological information
Some current development:
3. Finding new disease associations (with Dr. Larminie, GSK & Prof. Zupan, SLO)
Some Results: DO disease class∩ − DO (pathological analysis and clinical symptoms)
from only molecular data
PPIs
Co-expression
Cell signalling
Genetic inter.
Drug-target
Gene annotation
Gene-disease
Metabolic net
DO
GO
Drug inter.
X
36. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11
Networks → biological information
Some current development:
4. New graphlet-based measures: suitable for biological network analysis
Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013
Poster - O025
37. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11
Networks → biological information
Some current development:
4. New graphlet-based measures: suitable for biological network analysis
Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013
Poster - O025
38. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11
Networks → biological information
Some current development:
4. New graphlet-based measures: suitable for biological network analysis
Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013
Poster - O025
39. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
11
Networks → biological information
Some current development:
4. New graphlet-based measures: suitable for biological network analysis
Hayes, Sun, and Przulj, “Graphlet-based measures are suitable for biological network comparison,” Bioinformatics, 29:4, pp 483-91, 2013
Poster - O025
40. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12
Networks → biological information
Some current development:
4. New graphlet-based measures: network dynamics, disease classification,...
Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
42. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12
Networks → biological information
Some current development:
4. New graphlet-based measures: network dynamics, disease classification,...
Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
43. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
12
Networks → biological information
Some current development:
4. New graphlet-based measures: network dynamics, disease classification
Yaveroglu, Malod-Dognin, Davis, Levnajic, Janjic, Karapandza, Stojmirovic and Przulj, “Untangling Network Complexity,” submitted
44. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
1) New network analysis methods to mine complex network data
Network alignment
Cell’s functional organization
Network integration/fusion of various network types
Graphlet-based network encoding for dynamics
...
1) High-performance software package
13
Summary
45. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Network topology – new source of biological information
Software
Easy to use for biologists
Open source
Parallel
Benefit biologists:
Methods ready to use
Allow benchmarking
To come: web interface
13
GraphCrunch 2:
second most accessed in BMC
3400 downloads since Feb’11
Software
O. Kuchaiev, A. Stefanovic, W. Hayes, and N. Przulj, GraphCrunch 2: Software tool for network modeling, alignment and clustering, BMC Bioinformatics, 12(24):1-13, 2011 (highly accessed)
T. Milenkovic, J. Lai, and N. Przulj, GraphCrunch: A Tool for Large Network Analyses, BMC Bioinformatics, 9:70, January 30, 2008 (highly accessed)
46. Nataša Pržulj (http://www.doc.ic.ac.uk/~natasha) July 19, 2013
Final Remarks
Network topology – contains currently hidden biological information
Need new computational tools to mine network data biology
In close collaboration with biologists
“Network biology:”
• In its infancy & rich in open research problems
• Many unforeseen problems will emerge
• Good area to be in
14
49. 1. W. Hayes, K. Sun, and N. Przulj, Graphlet-based measures are suitable for biological network comparison, Bioinformatics, 2013
2. V. Janic and N. Przulj, The Core Diseasome, Molecular BioSystems, 8:2614-2625, July 4, 2012
3. V. Janic and N. Przulj, Biological function through network topology: a survey of the human diseasome, Briefings in Functional Genomics,
September 8, 2012
4. Arabidopsis Interactome Mapping Consortium, Evidence for Network Evolution in an Arabidopsis Interactome Map, Science, 333:601-607, July
29, 2011
5. T. Milenkovic, V. Memisevic and N. Przulj, Dominating Biological Networks, PLoS ONE, 6(8):e23016, 2011
6. N. Pržulj, “Protein-protein interactions: making sense of networks via graph-theoretic modeling,” Bioessays, 33(2), 2011.
7. O. Kuchaiev and N. Przulj, “Integrative Network Alignment Reveals Large Regions of Global Network Similarity in Yeast and Human”, Bioinformatics,
27(10): 1390-1396 , 2011.
8. O. Kuchaiev, A. Stevanovic, W. Hayes and N. Przulj, “GraphCrunch 2: software tool for network modeling, alignment and clustering”, BMC
Bioinformatics, 12(24):1-13, 2011. Highly accessed.
9. T. Milenkovic, W. L. Ng, W. Hayes and N. Przulj, “Optimal Network Alignment Using Graphlet Degree Vectors”, Cancer Informatics, 9:121-137, 2010.
Highly visible.
10. O. Kuchaiev, T. Milenkovic, V. Memisevic, W. Hayes and N. Przulj, “Topological Network Alignment Uncovers Biological Function and Phylogeny”, J.
Roy Soc. Interface, 7:1341–1354, 2010.
11. N. Przulj, O. Kuchaiev, A. Stevanovic, and W. Hayes “Geometric Evolutionary Dynamics of Protein Interaction Network”, Pacific Symposium on
Biocomputing (PSB’10), Hawaii, USA, 2010.
12. T. Milenkovic, V. Memisevic, A. K. Ganesan, and N. Przulj, “Systems-level Cancer Gene Identification from Protein Interaction Network Topology
Applied to Melanogenesis-related Interaction Networks”, J. Roy. Soc. Interface, 2009.
13. O. Kuchaiev, M. Rasajski, D. Higham, and N. Przulj, “Geometric De-noising of Protein-Protein Interaction Networks”, PLoS Computational Biology
5(8), e1000454, 2009.
14. C. Guerrero, T. Milenkovic, N. Przulj, P. Keiser, L. Huang, “Characterization of the proteasome interaction network using a QTAX-based tag-team
strategy and protein interaction network analysis,” PNAS, 105 (36), pg. 13333-13338 2008.
15. T. Milenkovic and N. Przulj, “Uncovering Biological Network Function via Graphlet Degree Signatures,” Cancer Informatics, vol. 4, pg. 257-273, 2008.
Highly visible.
16. T. Milenkovic, J. Lai, N. Przulj, “GraphCrunch: A Tool for Large Network Analyses,” BMC Bioinform., 9:70, 2008. Highly accessed.
17. F. Hormozdiari, P. Berenbrink, N. Przulj, C. Sahinalp, “Not all Scale Free Networks are Born Equal: the Role of the Seed Graph in PPI Network
Emulation,” PLoS Computational Biology, 3(7), 2007.
18. N. Przulj, “Geometric Local Structure in Biological Networks,” IEEE ITW’07 Invited Paper, 2007.
19. N. Przulj, “Biological Network Comparison Using Graphlet Degree Distribution,” Bioinformatics proc. of ECCB’06,23:e177-e183, 2007.
20. N. Przulj and D. Higham, “Modelling Protein-Protein Interaction Networks via a Stickiness Index,” J Roy Soc Interf, 3(10):711-6,2006.
21. N. Przulj, D. G. Corneil, and I. Jurisica, “Efficient Estimation of Graphlet Frequency Distributions in Protein-Protein Interaction Networks,”
Bioinformatics, vol. 22, num. 8, pg 974-980, 2006.
22. M. Barrios-Rodiles, K. R. Brown, B. Ozdamar, Z. Liu, R. S. Donovan, F. Shinjo, Y. Liu, R. Bose, J. Dembowy, I. W. Taylor, V. Luga, N. Przulj, M.
Robinson, H. Suzuki, Y. Hayashizaki, I. Jurisica, and J. L. Wrana, “High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells,”
Science, vol. 307, num. 5715, pg. 1621-1625, 2005.
23. N. Przulj, D. G. Corneil, and I. Jurisica, “Modeling Interactome: Scale-Free or Geometric?,” Bioinformatics, 20(18):3508-3515, 2004.
24. N. Przulj, D. Wigle, and I. Jurisica, “Functional Topology in a Network of Protein Interactions,” Bioinformatics, 20(3):340-348, 2004.