cs 5854: projects - undergraduate...
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Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
CS 5854: Projects
T. M. Murali
January 26, 28, February 2, 2016
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Continuum of Models in Systems Biology
From Building with a scaffold: emerging strategies for high- to low-level cellular modeling, Ideker
and Lauffenburger, Trends in Biotechnology Volume 21, Issue 6 , June 2003, Pages 255-262.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Goals of the Course
I Emphasise a data-driven approach to systems biology.
I Integrate massive quantities of different types of data
I Stress methods that can prioritise experiments.
I Learn techniques from clustering, data mining, and graph theory andapply them to solve specific biological questions.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Student PresentationsI Start on Tuesday, February 9.I Each presentation is 75 minutes long (including 20–30 minutes of
questions).I Forms groups of 2 ≤ k ≤ 3 students.I A group of k students will make k presentations.I Each group of k students should select one–two papers, one for each
class they will present.I Single student can lead/give each individual presentation, but read,
understand, and discuss papers as a group.I Use the tag 2016-spring-csb-papers on CiteULike.I Read (quickly) the papers before you select them!I Use the current schedule only as a guideline for how long I think each
paper will take to present. It is not the final schedule!I Send me the names of the groups and your top four paper choices by
Tuesday, January 26.I I will post the schedule by Thursday, January 28.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Class Projects that Resulted in Papers1. VIRGO: Computational Prediction of Gene Functions, Naveed Massjouni,
Corban Rivera, and T. M. Murali, Nucleic Acids Research, 2006.2. Network Legos: Building Blocks of Cellular Wiring Diagrams, T. M. Murali
and Corban G. Rivera, RECOMB 2007, JCB 2008.3. Computational Prediction of Interactions between Host and Pathogen
Proteins, Matthew Dyer, T. M. Murali, and Bruno Sobral, ISMB 2007.4. Divergence of Gene Expression Profiles in Tandemly Arrayed Genes in
Human and Mouse, Valia Shoja, T. M. Murali, and Liqing Zhang,Comparative and Functional Genomics, 2007.
5. Network-Based Prediction and Analysis of HIV Dependency Factors, T. M.Murali, Matthew D. Dyer, David Badger, Brett M. Tyler, and Michael G.Katze, PLoS Computational Biology, 2011.
6. Top-Down Network Analysis to Drive Bottom-Up Modeling of PhysiologicalProcesses, Christopher L. Poirel, Richard R. Rodrigues, Katherine C. Chen,John J. Tyson, and T. M. Murali, JCB, 2013.
7. “Pathways on Demand: Automatic Reconstruction of Human SignalingNetworks,” Anna Ritz, Christopher L. Poirel, Allison N. Tegge, NicholasSharp, Allison Powell, Kelsey Simmons, Shiv D. Kale, and T. M. Murali, npj:Systems Biology and Applications, 2016.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
List of Projects
1. Develop PathLinker 2.0
2. Predict signal transduction pathways from gene expression data.
3. Compute chemical response networks
4. Analyze PanCancer data
5. Predict orientation of interactions in interaction networks.
6. Develop XTalk 2.0, discover links between insulin signaling andinflammation in diabetes
7. Use NLP to build gold standard for XTalk
8. Compute feedback loops in yeast regulatory networks
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Baron and Kneissel. WNT signaling in bone homeostasis and disease: from human mutations to treatments. Nature Medicine,2013.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Wnt Signaling in a Pathway Database
Destruction Complex
www.netpath.org/netslim
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Signaling Pathways as Directed Graphs
Destruction Complex
FzdWnt
DirectedRegulatoryInteractions
GSK
β-Catenin
p
BidirectedPhysical
InteractionsFzd
Wnt
CTNNB1GSK
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Signaling Pathways as Directed Graphs
Destruction Complex
Receptors
Transcription Factors andTranscriptional Regulators
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Reconstructing Signaling Pathways
Human protein-protein interactome
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Reconstructing Signaling Pathways
Curated pathway is a subnetwork of the interactome
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Reconstructing Signaling Pathways
Question: Can we reconstruct the curated pathway given onlyreceptors and transcriptional regulators?
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Reconstructing Signaling Pathways
Proposed pathway reconstruction
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructed Pathways
Curated Pathway
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructed Pathways
Curated Pathway and Proposed Reconstruction
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructed Pathways
Curated Pathway and Proposed Reconstruction
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Output
ProposedReconstruction
Input
Receptors
TranscriptionalRegulators (TRs)
Human Interactome
PathLinker
I Developed PathLinker to reconstruct proteins and interactions
I Systematically evaluated PathLinker and other algorithms onhuman signaling pathways from the NetPath and KEGG databases
“Pathways on Demand: Automatic Reconstruction of Human Signaling Pathways,” Ritz et al., Systems Biology andApplications, a Nature partner journal, to appear, 2016.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Output
ProposedReconstruction
Evaluation
Curated Pathway
Input
Receptors
TranscriptionalRegulators (TRs)
Human Interactome
PathLinkerNetPathPathways
I Developed PathLinker to reconstruct proteins and interactions
I Systematically evaluated PathLinker and other algorithms onhuman signaling pathways from the NetPath and KEGG databases
“Pathways on Demand: Automatic Reconstruction of Human Signaling Pathways,” Ritz et al., Systems Biology andApplications, a Nature partner journal, to appear, 2016.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
How Does PathLinker work?
PathLinker
RWR
IPA
0.4
0.6
0.8
Precision
Recall0.2 0.4
IPA (prec. 0.72)
PathLinker(prec. 0.65)
RWR(prec. 0.61)
(a)
(b)
Wnt network in GraphSpaceT. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
PathLinker 2.0
I Current algorithm does not learn fromthe structure of the pathway.
I Goal: develop a (machine-learning)algorithm that can use information onthe edges in a pathway to predict newedges.
I Use cross-validation to testperformance: develop meaningful waysof deleting edges.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
PathLinker 2.0
I Current algorithm does not learn fromthe structure of the pathway.
I Goal: develop a (machine-learning)algorithm that can use information onthe edges in a pathway to predict newedges.
I Use cross-validation to testperformance: develop meaningful waysof deleting edges.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
PathLinker 2.0
I Current algorithm does not learn fromthe structure of the pathway.
I Goal: develop a (machine-learning)algorithm that can use information onthe edges in a pathway to predict newedges.
I Use cross-validation to testperformance: develop meaningful waysof deleting edges.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
PathLinker 2.0
I Current algorithm does not learn fromthe structure of the pathway.
I Goal: develop a (machine-learning)algorithm that can use information onthe edges in a pathway to predict newedges.
I Use cross-validation to testperformance: develop meaningful waysof deleting edges.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructions with Cross Validation
Curated pathway
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructions with Cross Validation
Edges removed for cross validation
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructions with Cross Validation
Proposed reconstruction
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructions with Cross Validation
Curated pathway and proposed reconstruction
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructions with Cross Validation
Cross validation edges and proposed reconstruction
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Evaluating Reconstructions with Cross Validation
Precision and recall
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Project Details
I Paper: Pathways on Demand: Automatic Reconstruction of HumanSignaling Pathways, Ritz et al., Systems Biology and Applications, aNature partner journal, to appear, 2016
I PathLinker code on GitHub
I Update network dataset used in PathLinker paper.
I Develop machine learning method that can utilize partial informationabout edges in a pathway.
I Find several meaningful alternatives methods to compare youralgorithm with.
I Create computational analyses that are different from the PathLinkerpaper.
I Do literature analysis of predicted interactions.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Dissect Cellular Responses to Signals
Space of Disse
Hepatocytes
LSECs and Kupffer cells
PEM
Collagen
Endothelial/Kupffer cells
Hepatocyte
Collaboration with Padma Rajagopalan (Chem Eng)
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Dissect Cellular Responses to Signals
Space of Disse
Hepatocytes
LSECs and Kupffer cells
PEM
Collagen
Endothelial/Kupffer cells
Hepatocyte
Collaboration with Padma Rajagopalan (Chem Eng)
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Dissect Cellular Responses to Signals
Space of Disse
Hepatocytes
LSECs and Kupffer cells
PEM
Collagen
Endothelial/Kupffer cells
Hepatocyte
Collaboration with Padma Rajagopalan (Chem Eng)
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Project DetailsI Papers:
I Designing a Multi-cellular Organotypic 3D Liver Model with a Detachable,Nanoscale Polymeric Space of Disse, Larkin et al., Tissue Eng., 2013.
I A genome-wide analysis in Saccharomyces cerevisiae demonstrates the influence ofchromatin modifiers on transcription, Steinfeld, Shamir, and Kupiec, NatureGenetics, 2007. Read the technique.
I Pathways on Demand: Automatic Reconstruction of Human Signaling Pathways,Ritz et al., Systems Biology and Applications, a Nature partner journal, in press,2016
I Use liver gene expression data from Padma Rajagopalan’s group.I Find other appropriate datasets (by the middle of February):
1. Gene expression measurements after a signal.I ideally for signals on liver tissue.I ideally, accompanied by a proteomic dataset.
2. Prefer human or mammalian datasets.3. Create a high-quality dataset of molecular interactions in appropriate
organism.4. If proteomic data is available, use it to measure performance.
I Use software for existing algorithms (PathLinker, eQED, PCSF, etc).I Perform literature based validation of predicted pathways.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
ToxCast → Chemical Response Networks
I Efforts such as the EPA’s ToxCast seek to quickly and efficientlyscreen thousands of chemicals for potential human and environmentaleffects.
I Information is partial: for each chemical, these efforts study a fixedset of proteins and pathways.
I Assays are independent: Do not consider the complex network ofinteractions among assayed proteins and pathways.
I We seek to connect the responding proteins in the context of theunderlying network of regulatory and physical interactions.
I Toxicant response network: network of regulatory, signaling andphysical interactions that connects proteins that are perturbed as aresult of toxicant exposure.
I Toxicant response networks may revealI important intermediate proteins that have not have been tested andI physiological processes that have not been previously implicated in
connection with the chemical.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
ToxCast → Chemical Response Networks
I Efforts such as the EPA’s ToxCast seek to quickly and efficientlyscreen thousands of chemicals for potential human and environmentaleffects.
I Information is partial: for each chemical, these efforts study a fixedset of proteins and pathways.
I Assays are independent: Do not consider the complex network ofinteractions among assayed proteins and pathways.
I We seek to connect the responding proteins in the context of theunderlying network of regulatory and physical interactions.
I Toxicant response network: network of regulatory, signaling andphysical interactions that connects proteins that are perturbed as aresult of toxicant exposure.
I Toxicant response networks may revealI important intermediate proteins that have not have been tested andI physiological processes that have not been previously implicated in
connection with the chemical.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
ToxCast → Chemical Response Networks
I Efforts such as the EPA’s ToxCast seek to quickly and efficientlyscreen thousands of chemicals for potential human and environmentaleffects.
I Information is partial: for each chemical, these efforts study a fixedset of proteins and pathways.
I Assays are independent: Do not consider the complex network ofinteractions among assayed proteins and pathways.
I We seek to connect the responding proteins in the context of theunderlying network of regulatory and physical interactions.
I Toxicant response network: network of regulatory, signaling andphysical interactions that connects proteins that are perturbed as aresult of toxicant exposure.
I Toxicant response networks may revealI important intermediate proteins that have not have been tested andI physiological processes that have not been previously implicated in
connection with the chemical.T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Bisphenol-A Response Network
Responsive Protein
Non-responsive Protein
Physical Interaction
Regulatory Interaction
ALK1 Pathway
BMPPathway
RXR/VDRPathway
AktPathway
p38 Pathway
Activation of AP1
Down-regulation of TGFbeta Receptor
Signaling
PYK2Pathway
Posterior LiteraturePathway Probability Support
ALK1 Pathway (PID) 0.998BMP Pathway (PID) 0.996RXR VDR Pathway (PID) 0.981 XAKT Pathway (Biocarta) 0.881 XP38 γδ Pathway (PID) 0.808Activation of AP1 Family of TFs (Reactome) 0.711 XDownregulation of TGFβR Signaling (Reactome) 0.669PYK2 Pathway (Biocarta) 0.654 X
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Bisphenol-A Response Network
Posterior LiteraturePathway Probability Support
ALK1 Pathway (PID) 0.998BMP Pathway (PID) 0.996RXR VDR Pathway (PID) 0.981 XAKT Pathway (Biocarta) 0.881 XP38 γδ Pathway (PID) 0.808Activation of AP1 Family of TFs (Reactome) 0.711 XDownregulation of TGFβR Signaling (Reactome) 0.669PYK2 Pathway (Biocarta) 0.654 X
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Project Details
I Papers:I Pathways on Demand: Automatic Reconstruction of Human Signaling Pathways,
Ritz et al., Systems Biology and Applications, a Nature partner journal, to appear,2016
I Profiling 976 ToxCast Chemicals across 331 Enzymatic and Receptor SignalingAssays, Sipes et al., Chem Res Toxicol, 17; 26(6), 878–895, 2013.
I Phenotypic screening of the ToxCast chemical library to classify toxic andtherapeutic mechanisms, Kleinstreuer et al., Nature Biotechnology 32, 583–591,2014.
I Find other papers on ToxCast and Tox21 datasets and understandhow these data are accessible through the EPA and NIEHS websites.
I Apply PathLinker to each chemical to compute its “responsenetwork.”
I Integrate relevant gene expression dataset for each chemical, ifavailable.
I Demonstrate increased knowledge in response networks whencompared to the original ToxCast data.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
PanCancer + PathLinker
I Use PathLinker to analyze PanCancer data.I Papers:
I Pan-cancer network analysis identifies combinations of rare somaticmutations across pathways and protein complexes, Leiserson et al., NatGenet, 47, 2. 2015, 106–114, 2015.
I Discovering causal pathways linking genomic events to transcriptionalstates using Tied Diffusion Through Interacting Events (TieDIE)
I Pathway and network analysis of cancer genomes (Review)
I Most of these papers analyze overlap of the genes in the computednetworks with known physiological processes.
I Can we use PathLinker to discover mechanisms by which genomicevents control transcriptional changes?
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Baron and Kneissel. WNT signaling in bone homeostasis and disease: from human mutations to treatments. Nature Medicine,2013.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Wnt Signaling in a Pathway Database
Destruction Complex
www.netpath.org/netslim
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Orienting Interactions
I Interactions in pathways are often directed because they arecorrespond to regulation.
I Interactions in PPI networks are undirected because they are physical.I Human protein interactome:
I 12K nodes and 110K edgesI 87K physical interactions
BIND, DIP, InnateDB, IntAct, MINT, MatrixDB, Reactome, NetPath, KEGG, SPIKE
I 33K signaling interactionsNetPath, KEGG, SPIKE
I Develop methods to assign direction to edges in PPI networks andquantitatively evaluate your results.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Orienting Interactions
I Interactions in pathways are often directed because they arecorrespond to regulation.
I Interactions in PPI networks are undirected because they are physical.I Human protein interactome:
I 12K nodes and 110K edgesI 87K physical interactions
BIND, DIP, InnateDB, IntAct, MINT, MatrixDB, Reactome, NetPath, KEGG, SPIKE
I 33K signaling interactionsNetPath, KEGG, SPIKE
I Develop methods to assign direction to edges in PPI networks andquantitatively evaluate your results.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Approach for Orienting Interactions
Human protein-protein interactome
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Approach for Orienting Interactions
Pathway is a subnetwork of the interactome
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Approach for Orienting Interactions
Question: Can we assign edge orientations given onlyreceptors and transcriptional regulators?
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Approach for Orienting Interactions
Proposed orientations
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Project Details
I Papers:I Discovering pathways by orienting edges in protein interaction
networks, Gitter et al., Nucleic Acids Research, 2011I Network orientation via shortest paths, Silverbush and Sharan,
Bioinformatics, 2014
I Software: PathLinker (Murali), OrientEdges (Gitter), Shortest(Sharan)
I Implement OrientEdges and Shortest yourself in Python within thePathLinker package.
I Evaluate by comparing to gold standard dataset of interactions withexperimentally known orientation.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Project Details
I Papers:I Discovering pathways by orienting edges in protein interaction
networks, Gitter et al., Nucleic Acids Research, 2011I Network orientation via shortest paths, Silverbush and Sharan,
Bioinformatics, 2014
I Software: PathLinker (Murali), OrientEdges (Gitter), Shortest(Sharan)
I Implement OrientEdges and Shortest yourself in Python within thePathLinker package.
I Evaluate by comparing to gold standard dataset of interactions withexperimentally known orientation.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Signaling pathway crosstalk
Cell
NF2Mst1/2
Hippo Receptor
Lats1/2
YAP TAZ
YAP TAZp
General Assumption: pathways that share nodes crosstalk
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Signaling pathway crosstalk
Nucleus
SMAD2
TGFBR1/2
SMAD2
SMAD3
SMAD3p
SMAD2SMAD3p
Cell cycleApoptosis
General Assumption: pathways that share nodes crosstalk
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Signaling pathway crosstalk
Cell
SMAD2/3NF2
Mst1/2
Hippo Receptor
Lats1/2
E2F4/5
SMAD4
TGFBR1/2
YAP TAZSMAD2/3
X
YAPTAZp
General Assumption: pathways that share nodes crosstalk
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Signaling pathway crosstalk
Cell
SMAD2/3NF2
Mst1/2
Hippo Receptor
Lats1/2
E2F4/5
SMAD4
TGFBR1/2
YAP TAZSMAD2/3
X
YAPTAZp
General Assumption: pathways that share nodes crosstalk
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Importance of Crosstalk
I Crosstalk is a naturally occuring phenomenon
I Crosstalk has been implicated in many diseases, including cancer, hostpathogen defense, and regulation of cell survival
I Surprisingly, no computational methods exist to predict which pairs ofpathways may crosstalk
XTalk: a method to identify which pathway pairs may crosstalk.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Importance of Crosstalk
I Crosstalk is a naturally occuring phenomenon
I Crosstalk has been implicated in many diseases, including cancer, hostpathogen defense, and regulation of cell survival
I Surprisingly, no computational methods exist to predict which pairs ofpathways may crosstalk
XTalk: a method to identify which pathway pairs may crosstalk.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Importance of Crosstalk
I Crosstalk is a naturally occuring phenomenon
I Crosstalk has been implicated in many diseases, including cancer, hostpathogen defense, and regulation of cell survival
I Surprisingly, no computational methods exist to predict which pairs ofpathways may crosstalk
XTalk: a method to identify which pathway pairs may crosstalk.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Importance of Crosstalk
I Crosstalk is a naturally occuring phenomenon
I Crosstalk has been implicated in many diseases, including cancer, hostpathogen defense, and regulation of cell survival
I Surprisingly, no computational methods exist to predict which pairs ofpathways may crosstalk
XTalk: a method to identify which pathway pairs may crosstalk.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
XTalk: average length of the k shortest paths
Inputs:
A Pathway A
B Pathway B
RA Receptors in A
TB TFs in B
k number of paths
G background networkPathway AReceptor
Pathway BTranscription Factor
Notation:
d(r , t, k) the length of the kth shortest path from r to t
χ(A,B, k) the XTalk statistic
χ(A,B, k) =1
k |TB |∑
t∈TB
k∑l=1
d(RA, t, l),
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
XTalk: average length of the k shortest paths
Inputs:
A Pathway A
B Pathway B
RA Receptors in A
TB TFs in B
k number of paths
G background networkNotation:
d(r , t, k) the length of the kth shortest path from r to t
χ(A,B, k) the XTalk statistic
χ(A,B, k) =1
k |TB |∑
t∈TB
k∑l=1
d(RA, t, l),
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Performance of XTalk
0.0 0.2 0.4 0.6 0.8 1.0FPR
0.0
0.2
0.4
0.6
0.8
1.0TPR
XTalk BPLN NIC
AUC:0.65
AUC:0.58
AUC:0.50
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
XTalk network from Hippo to TGF-β Pathway
SMAD6,SMAD7
SP1
TGFBR2,TGFBR1
RHOA
SMAD2,SMAD3
SMAD3
TGFBR2
EP300,CREBBPSMAD1
RAF1
SMURF1,SMURF2
TGFBR1
BMPR1A,BMPR1B,BMPR2
RBL1
TFDP2,TFDP1
E2F5,E2F4
SMAD4
WWTR1,YAP1
YAP1
SMAD5,SMAD1,SMAD9
BMPR1A,BMPR1B,ACVR1
SMAD7
TFDP1
PPP2CA,PPP2R1A,PPP2R1B,PPP2CB
MAP2K2,MAP2K1
MAPK3,MAPK1
MAP2K1
MAP2K2
ESR2,ESR1
JUN,SP1,FOS
LEF1,TCF7,TCF7L2,TCF7L1
MYC
GSK3B
DVL2,DVL3,DVL1
FZD1,FZD2,FZD3,FZD4,FZD5FZD6,FZD7,FZD8,FZD9,FZD10
+p
+p
http://graphspace.org/graphs/[email protected]/kegg-hippo-tgfbetaCrosstalk Network on GraphSpaceT. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
XTalk 2.0
I Simple: improve the AUC of XTalk!
I Just like PathLinker, XTalk does not learn anything from thestructure of the pathways.
I Can we use machine learning techniques to improve XTalk?
I XTalk can use edges from any pathway in its crosstalk networks.Can we constrain the types of labels that can appear in a path?
I Papers:I XTalk: a path-based approach for identifying crosstalk between
signaling pathways, Tegge, Sharp, and Murali, Bioinformatics, 32,242–251
I Engineering Label-Constrained Shortest-Path Algorithms, Barrett et al.,Algorithmic Aspects in Information and Management, LNCS 5034,27–37, 2008
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
XTalk 2.0
I Simple: improve the AUC of XTalk!
I Just like PathLinker, XTalk does not learn anything from thestructure of the pathways.
I Can we use machine learning techniques to improve XTalk?
I XTalk can use edges from any pathway in its crosstalk networks.Can we constrain the types of labels that can appear in a path?
I Papers:I XTalk: a path-based approach for identifying crosstalk between
signaling pathways, Tegge, Sharp, and Murali, Bioinformatics, 32,242–251
I Engineering Label-Constrained Shortest-Path Algorithms, Barrett et al.,Algorithmic Aspects in Information and Management, LNCS 5034,27–37, 2008
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
XTalk 2.0
I Simple: improve the AUC of XTalk!
I Just like PathLinker, XTalk does not learn anything from thestructure of the pathways.
I Can we use machine learning techniques to improve XTalk?
I XTalk can use edges from any pathway in its crosstalk networks.Can we constrain the types of labels that can appear in a path?
I Papers:I XTalk: a path-based approach for identifying crosstalk between
signaling pathways, Tegge, Sharp, and Murali, Bioinformatics, 32,242–251
I Engineering Label-Constrained Shortest-Path Algorithms, Barrett et al.,Algorithmic Aspects in Information and Management, LNCS 5034,27–37, 2008
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
XTalk Gold Standard
I Selected 17 signaling pathways in KEGG.I For each pair of pathways,
I Performed PubMed query andI Read papers for evidence of crosstalkI Recorded sentence providing evidence, if any.
I Goal: use natural language processing techniques to train system thatreads articles or their abstracts to automatically determine whether apair of pathways crosstalk.
I Requires some experience with NLP tools.
Crosstalk database
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
XTalk Gold Standard
I Selected 17 signaling pathways in KEGG.I For each pair of pathways,
I Performed PubMed query andI Read papers for evidence of crosstalkI Recorded sentence providing evidence, if any.
I Goal: use natural language processing techniques to train system thatreads articles or their abstracts to automatically determine whether apair of pathways crosstalk.
I Requires some experience with NLP tools.
Crosstalk database
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Destruction Complex
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Destruction Complex
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Feedback Loops
I These computed networks contain no loops or feedback cycles!
I Feedback cycles are fundamental components of regulatory circuits.
I How difficult is to compute all the cycles of a length three in a graph?Length four, five, six? The large number of possible cycles may makecost prohibitive.
I Computing all cycles also may not be very interesting.
I Goal: Given a pair of query nodes s and t, compute the k shortestcycles containing s and t.
I Develop algorithm.
I Apply it to yeast or human regulatory and signaling networks.
I Develop methods to evaluate results.
I Use literature to provide additional support.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Feedback Loops
I These computed networks contain no loops or feedback cycles!
I Feedback cycles are fundamental components of regulatory circuits.
I How difficult is to compute all the cycles of a length three in a graph?
Length four, five, six? The large number of possible cycles may makecost prohibitive.
I Computing all cycles also may not be very interesting.
I Goal: Given a pair of query nodes s and t, compute the k shortestcycles containing s and t.
I Develop algorithm.
I Apply it to yeast or human regulatory and signaling networks.
I Develop methods to evaluate results.
I Use literature to provide additional support.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Feedback Loops
I These computed networks contain no loops or feedback cycles!
I Feedback cycles are fundamental components of regulatory circuits.
I How difficult is to compute all the cycles of a length three in a graph?Length four, five, six?
The large number of possible cycles may makecost prohibitive.
I Computing all cycles also may not be very interesting.
I Goal: Given a pair of query nodes s and t, compute the k shortestcycles containing s and t.
I Develop algorithm.
I Apply it to yeast or human regulatory and signaling networks.
I Develop methods to evaluate results.
I Use literature to provide additional support.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Feedback Loops
I These computed networks contain no loops or feedback cycles!
I Feedback cycles are fundamental components of regulatory circuits.
I How difficult is to compute all the cycles of a length three in a graph?Length four, five, six? The large number of possible cycles may makecost prohibitive.
I Computing all cycles also may not be very interesting.
I Goal: Given a pair of query nodes s and t, compute the k shortestcycles containing s and t.
I Develop algorithm.
I Apply it to yeast or human regulatory and signaling networks.
I Develop methods to evaluate results.
I Use literature to provide additional support.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Feedback Loops
I These computed networks contain no loops or feedback cycles!
I Feedback cycles are fundamental components of regulatory circuits.
I How difficult is to compute all the cycles of a length three in a graph?Length four, five, six? The large number of possible cycles may makecost prohibitive.
I Computing all cycles also may not be very interesting.
I Goal: Given a pair of query nodes s and t, compute the k shortestcycles containing s and t.
I Develop algorithm.
I Apply it to yeast or human regulatory and signaling networks.
I Develop methods to evaluate results.
I Use literature to provide additional support.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Overview
Focus of the Course
Projects
Develop PathLinker 2.0
Predict signal transduction pathways from gene expression data
Compute chemical response networks
Analyze PanCancer data
Predict orientation of interactions in networks
Develop XTalk 2.0
Use NLP to build gold standard for XTalk
Compute feedback loops in yeast regulatory networks
Support
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Hardware Support for Projects
I 40-processor, 20-node cluster in the Department of Computer Sciencededicated to bioinformatics (baobab.cs.vt.edu).
I Obtain accounts on bioinformatics.cs.vt.edu from Rob Hunter(rhunter at vt dot edu).
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Software Support for Projects
I My software page:http://bioinformatics.cs.vt.edu/~murali/software
I My Github page: http://github.com/Murali-group
I Biorithm software suite: http:
//bioinformatics.cs.vt.edu/~murali/software/biorithm-docs
I PathLinker, PCSF, ResponseNet, etc.: Code or pipelines implemented inPython and other languages.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
Ground Rules for Projects
I 1 hour meetings with each group every 2 weeks or 4 weeks.
I Maintain Google docs describing your project and your progress.
I Mid-term project review presentations on Tuesday, March 15 in class.
I Project descriptions (motivation, background, related and previousresearch, approach, data, any preliminary results) due on Tuesday,March 15.
I Final project presentations possibly on Tuesday, May 3 andWednesday, May 4. Also possible to set aside a special day just forfinal presentations.
I Final project reports due on 5pm, Friday, May 6.
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects
Focus Projects PL 2.0 Expression to Signals ToxCast PanCancer Orient XTalk 2.0 NLP Feedback Support
List of Projects
1. Develop PathLinker 2.0
2. Predict signal transduction pathways from gene expression data.
3. Compute chemical response networks
4. Analyze PanCancer data
5. Predict orientation of interactions in interaction networks.
6. Develop XTalk 2.0, discover links between insulin signaling andinflammation in diabetes
7. Use NLP to build gold standard for XTalk
8. Compute feedback loops in yeast regulatory networks
T. M. Murali January 26, 28, February 2, 2016 CS 5854: Projects