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Laboratory for Text information, Mining, Analysis and Prediction Indiana University-Purdue University, Indianapolis Construction of Molecular Networks.

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Presentation on theme: "Laboratory for Text information, Mining, Analysis and Prediction Indiana University-Purdue University, Indianapolis Construction of Molecular Networks."— Presentation transcript:

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2 Laboratory for Text information, Mining, Analysis and Prediction Indiana University-Purdue University, Indianapolis Construction of Molecular Networks and Pathways using OMICs and Literature Data Mathew Palakal and Meeta Pradhan School of Informatics IUPUI 1

3 2 From Bibliomics to Target Discovery for Colorectal Cancer CRC related Keywords BioSIFTER Literature harvesting and Personalization BioSIFTER Literature harvesting and Personalization BioMAP Mining and Identification of novel biomarkers BioMAP Mining and Identification of novel biomarkers

4 BioSIFTER

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10 9 “ A major challenge faced by biologist is to identify the most significant genes in a disease that can be targeted” BioMAP: BioMedical Literature Mining Our Hypothesis: Augmenting the experimental data with literature data can help to identify novel molecules that may be of significant relevance to the study under consideration. Nodes/Links Experimental Data New Nodes/Links Augmented with Literature Data

11 10 Regulatory Network Construction and Analysis Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Multi-scale Multi-level Analysis Multi-scale Multi-level Analysis CRC TF Network CRC miRNA Network

12 11 Experiments on TF Networks Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Set of 48 keywords: myh, mlh1, cdk8, crcs7, dcc, crcs6, tgfbr1, tpx2, crcs, apc, hnpcc7, msh2, mlh1, braf, hnpcc, msh6, pten, fus1, cxcl2, rad18, hgf, axin2, casp3, prl3, nat1, gstm1, gstt1, cyp2c9, bcl2, prmt1, sn38, cpt11, proxy, smad3, igfbp1, pdgfb, capg, plk1, ifim1, csnk2a2, mbl2, pms2, cxcl2, igfir, cyp27b1, cyp24, mucins, colorectal Cancer

13 12 Literature Mining Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS  Retrieved 133,923 articles.  Obtained 2724 unique Swiss-Prot entry names.

14 13 Protein Interaction Prediction Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Annotating the Interaction Network with miRNA and miRNA Expression Data Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network P53 EP300 Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning 2724 Protein-protein interaction prediction is based on:  Gene Ontology Annotation Similarity Association  Structural Interaction  Pfam domain interaction  Sequence Potential Analysis

15 14 Sliding Window Algorithm for PPI Prediction Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Annotating the Interaction Network with miRNA and miRNA Expression Data P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning Physico-chemical parameters for probable interacting interface identification Hydrophobicity Accessibility Residue Interface Propensity P53 : EP300= Total Interacting Score (Number of Interface Residue and Number of Structure Interacting) P53 2F1Y A1c26 A1Z1M A EP300 1L3E B3BIY A Protein % structure Interacting % structure Interacting P53_HUMAN70MDM2_HUMAN93 P53_HUMAN59EP300_HUMAN100 P53_HUMAN67MDM4_HUMAN100 UBP7_HUMAN100P53_HUMAN74

16 15 Transcription Factor Network Generation for CRC Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Annotating the Interaction Network with miRNA and miRNA Expression Data P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning  117 transcription factors  277 non-transcription factors  700 interactions

17 16 Multi-level Multi-parametric Approach to Identify Significant Transcription Factors in CRC Network Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Validation of the Significant Genes TF Network miRNA Network Topological Analysis Sub-Graph Analysis Hyper geometric Associations Identification of significant nodes in the network  Topological Analysis Nodestrength = function (ProteinInteractionPropensityScore, Topological Features)  Sub-Graph Analysis  Hyper geometric Associations Multiparametric approach is used to identify significant Transcription Factors.

18 17 Results: Significant Transcription Factors in CRC Network Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Validation of the Significant Genes TF Network miRNA Network Topological Analysis Sub-Graph Analysis Hyper geometric Associations Identification of significant nodes in the network Highly Scored Common Transcription factors: c-Jun, NF-kB, P53, STAT3, SP1, STAT1, c-MYC, E2F1, SMAD3, MEF2A Highly Scored Unique Transcription Factors: Topological: LEF1, MEF2C, SMAD2, SMAD4, ELK-1, PPARA Hypergeometric:DAND5, RXRA, ESR1, ATF-2, SP3, RARA, PPARD Module:P73, ETS1, ETS2, GATA-1, FOXA1, FOXA2, SLUG, HAND1, SNAIL, VDR, TF7L2, ITF-2, REST, SRF, IRF1

19 18 Result: A Highly-scored Module Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Validation of the Significant Genes TF Network miRNA Network Topological Analysis Sub-Graph Analysis Hyper geometric Associations Identification of significant nodes in the network MAPK1 C-JUN JNK1 ELK-1 ATF-2 MAPK14 MK09 ESR1 MK10 PIAS1

20 19 Validation of the Significant Genes Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Validation of the Significant Genes

21 20 Validation of the Significant Genes Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Validation of the Significant Genes

22 21 Validation of the Significant Genes Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Validation of the Significant Genes

23 22 Global Transcription Factor Association Network showing Functional Groups Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network

24 23 Annotation of miRNA with Transcription Factors in CRC Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Annotating the Interaction Network with miRNA and miRNA Expression Data  Expression dataset: GSE14985  3 Normal samples, 3 colon samples  No. of miRNA :723  Top 100 differentially expressed miRNA are identified.  26 upregulated and 74 downregulated miRNA are further analyzed.

25 24 Novel miRNA identified Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Up-regulated Novel miRNATarget of miRNARelevance to cancer hsa-miR-663 CCND1, FOS, PTEN, TGFBR1Not reported* hsa-miR-630ATM, BAX,BCL2,BCL2L2, CASP3,Not reported* p53, TP73 hsa-miR-424ATF2, BCR, CCND1,CDK6, CHEK1, Kidney, E2F1, EGFR, ESR1, ETS1, FLT3, Pancreatic cancer HIF1A, MUC1, MYB, RARA, RUNX1, SMAD3, SP2,WEE1 * The target genes were identified by literature mining and many genes are important in CRC

26 25 Novel miRNA Identified Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Down-regulated Novel miRNA Target of miRNADisease hsa-let-7cBBC3, BCL2, MCL1, MEF2C, MYC, Lung,hepatocellular NGF, PPARA, ADAM9cancer hsa-let-7dBDNF, CCND1, EGFR, SMAD3Epithelial Ovariancancer hsa-let-7iBCL2, HIF1A, NFKB1, TLR4Breast cancer hsa-miR-103BMP7, CDK6, PPARAPancreatic cancer hsa-miR-100AKT1,CCND1, ESR1,FGFR3,JUN,P53Oral squamous cell MYCcarcinoma hsa-miR-99aAKT1, BDNF, CCND1, JUN,IGF1, JUN, Bladder cancer MYC, p53 hsa-miR-30eBcl2l2, ERBB2Lung cancer hsa-miR-425SMAD3Glioblastoma hsa-miR-361-5pAKT1, IRS1Ovarian cancer hsa-miR-494AKT1, CDK6, JUN, PTENCardiac Hypertrophy hsa-miR-331-3pAKT1, EGFR, ERBB2Epithelial ovarian cancer

27 26 miRNA-gene Network Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network

28 27 Number of miRNA Associated with CRC Related Pathways Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Validation of the Significant Genes Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network

29 28 Validation of the Significant Genes Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Experiment Data hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Literature augmented data SMAD4, P53, NF-kB, AKT1, PAK1, SOS Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Protein Interaction Prediction  Gene Ontology Annotation Similarity Association  Structural Interactions  Pfam Domain Interactions  Sequence Potential Analysis Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning P53 EP300 Topological Analysis Sub-Graph Analysis Hyper geometric Associations Identification of significant nodes in the network TF Network miRNA Network Validation of the Significant Genes Module: Brca1: p53:c-Myc Pathway: Brca1 as a transcription regulator Domain: DNA Damage

30 Identification of significant nodes in the network Topological Analysis Sub-Graph Analysis Validation of the Significant Nodes Hyper geometric Associations 29 Protein-Protein Interaction Prediction Tool hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling SMAD4, P53, NF-kB, AKT1, PAK1, SOS Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning Experiment Data Literature augmented data P53 EP300 Algorithm for Interacting Proteins

31 Identification of significant nodes in the network Topological Analysis Sub-Graph Analysis Validation of the Significant Nodes Hyper geometric Associations 30 hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling SMAD4, P53, NF-kB, AKT1, PAK1, SOS Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning Experiment Data Literature augmented data P53 EP300 Algorithm for Interacting Proteins Protein-Protein Interaction Prediction Tool

32 Identification of significant nodes in the network Topological Analysis Sub-Graph Analysis Validation of the Significant Nodes Hyper geometric Associations 31 hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling SMAD4, P53, NF-kB, AKT1, PAK1, SOS Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning Experiment Data Literature augmented data P53 EP300 Algorithm for Interacting Proteins Protein-Protein Interaction Prediction Tool

33 Identification of significant nodes in the network Topological Analysis Sub-Graph Analysis Validation of the Significant Nodes Hyper geometric Associations 32 hMLH1: DNA repair MSH2: DNA repair CDK8: Wnt signaling SMAD4, P53, NF-kB, AKT1, PAK1, SOS Annotating the Interaction Network with miRNA and miRNA Expression Data Interaction Scoring (i) First Principle Methods (ii) Machine Learning Interaction Scoring (i) First Principle Methods (ii) Machine Learning Experiment Data Literature augmented data P53 EP300 Algorithm for Interacting Proteins Protein-Protein Interaction Prediction Tool

34 33 M. Pradhan, P. Gandra, M. Palakal, Predicting Protein-Protein Interactions using First Principle Methods and Statistical Scoring, ACM International Symposium on Biocomputing, Calicut, 2010. M. Pradhan and M. Palakal, Global analysis of transcription factors and functional domains in CRC. (Manuscript under preparation). M. Pradhan, P. Gandra, M. Palakal, Predicting Protein-Protein Interactions using First Principle Methods and Statistical Scoring, ACM International Symposium on Biocomputing, Calicut, 2010. M. Pradhan and M. Palakal, Identifying CRC specific pathways and biomarkers from literature augmented proteomics data, BIOCOMP 2010. M. Pradhan and M. Palakal Global analysis of miRNA target genes in colon rectal cancer, IEEE BIBM Hong Kong, 2010. M. Pradhan and M. Palakal, Global analysis of transcription factors in CRC using protein interaction networks. (Manuscript in final stages). M. Pradhan and M. Palakal, Identifying candidate pathways and genes in CRC: meta-analysis of gene expression data (Manuscript in preparation). M. Pradhan and M. Palakal, Machine Learning for Predicting Protein Interactions (Manuscript in preparation). M. Pradhan, Sanders P and M. Palakal, Algorithm for Protein-drug binding predictions (Manuscript in preparation). Y. Pandit, M. Pradhan and M. Palakal, Database for Protein-Protein Interaction Predictions (Manuscript in preparation). Publications

35 Acknowledgements The Ti MAP team: Meeta Pradhan Shielly Hartanto Premchand Gandra Deepali Jhamb Rini Pauly Gokul Kilaru Philip Sanders Yogesh Pandit Sijin C. A. Tulip Nadu Kshithija Nagulapalli http://regen.informatics.iupui.edu/research/regen.informatics.iupui.edu/research

36 Questions? 35

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