Integrative Omics for The Systems Biology of Complex Phenotypes
|
|
- Lucinda Henry
- 5 years ago
- Views:
Transcription
1 Integrative Omics for The Systems Biology of Complex Phenotypes Mehmet Koyutürk Case Western Reserve University (1) Electrical Engineering and Computer Science (2) Center for Proteomics and Bioinformatics
2 Joint Work With Salim Chowdhury CMU CS Sinan Erten CWRU EECS Vishal Patel CWRU Genetics/MSTP Rod Nibbe CWRU Pharmacology Mark Chance CWRU Proteomics Giri Gokulrangan (CWRU Proteomics), Jill Barnholtz-Sloan (CWRU Oncology) Andrew Sloan (CWRU Neurological Surgery), Yanwen Chen (Case Comprehensive Cancer Center)
3 Complex Diseases 3 Many diseases are based on complex interactions between multiple genes. Cancer, diabetes, obesity, Alzheimer s disease, hearth disease, etc. It is possible to interrogate affected samples at multiple levels Systems-level insights into disease mechanisms
4 omics Genomics Transcriptomics Proteomics Interactomics DNA mrna Protein Functional Protein Transcriptional Control Translational Control Post-Translational Control 4
5 Transcriptomics Differential mrna expression Dysregulated genes Diagnosis Prognosis Response to treatment Individual differential expression DNA microarrays Classification RNAseq
6 Systems-Level Analysis Collective dysregulation of (potentially) functionally associated genes Pathways Established biological knowledge Enrichment-based analyses Statistical tests (Protein Interaction) Networks Large-scale Noisy, incomplete Novel hypotheses Computational algorithms
7 Pathway-Based Analysis Gene Set Enrichment Analysis (GSEA) Subramanian et al., PNAS, 2005
8 Network-Based Algorithms First aggregate gene expression levels for each sample Then quantify dysregulation using mutual information Chuang et al., Mol Sys Bio, 2007
9 Identifying Dysregulated Subnetworks Synergy Watkinson et al., BMC Sys Bio, 2008 Biclustering Dao et al., ECCB, 2010 Combinatorial Dysregulation Chowdhury et al., RECOMB, 2010 Set Cover Ulitsky et al., RECOMB, 2008 Chowdhury & Koyutürk, PSB, 2010 Discriminative Subnetworks Dao et al., ISMB, 2011 Network-Guided Forests Dutkowski & Ideker., PLoS CB, 2011
10 Combinatorial Coordinate Dysregulation Quantize gene expression levels Compute the mutual information between the expression state of the subnetwork and the phenotype random variable
11 State Functions
12 CRANE Algorithm Exhaustive, yet efficient search for subnetworks and associated state functions that are informative of phenotype Branch-and-bound algorithm based on provable bounds on J- value Network further confines the search space
13 Validation via Cross-Classification
14 Predicting Colorectal Cancer Metastasis
15 Generating Novel Hypotheses Subnetwork composed of membrane-bound proteins ITGAV (Integrin alpha chain) is not differentially expressed at the mrna level, but is involved in cell adhesion Post-translational dysregulation of integrins?
16 Classification of Brain Tumors Brain tumors account for ~1-2% of all cancers Majority of malignant BTs are GLIOMAS, majority of benign BTs are MENINGIOMAS: Incidence and survival vary greatly by histological type Median survival in Gliablastoma (GBM) 12 months (Surgery + Chemotherapy + Radiation) Can we find markers of survival?
17 Predicting Survival Time Transcriptomic Profiling (TCGA) CRANE Top-Scoring Subnetworks Proteomic Markers of Survival Targeted LC-MS/MS 43 (25%) ShortTerm Survivors (< 225 days) Candidate Proteomic Markers 43 (25%) LongTerm Survivors (> 635 days) PPI Network (HPRD) 18 Clinical Samples (Ohio Brain Tumor Study)
18 Top 5 Subnetwork Markers of Survival in GBM
19 Cross-Validation on An Independent Dataset Top 5 CRANE subnetworks were used for binary classification of an independent dataset (GSE13041) obtained from the Gene Expression Omnibus
20 Proteomic Validation Question: How does protein expression vary as a function of survival? Hypothesis: The expression of proteins that are coordinately dysregulated at the mrna-level is more likely to serve as a marker for survival, as compared to that of proteins that exhibit individual mrna-level differential expression Sample: Ohio Brain Tumor Study 18 patients (10 short term vs. 6 long term survivors) 2 technical replicates for each patient
21 Differential Protein Expression
22 Conclusions Network algorithms can find combinations of genes that can distinguish phenotypes more accurately Useful classifiers Network markers provide insights into the regulatory logic of disease mechanisms Gene expression + protein-protein interactions => Dysregulated proteins at the functional level Computational algorithms can drive targeted proteomics
23 Acknowledgments Salim Chowdhury CMU CS Sinan Erten CWRU EECS Vishal Patel CWRU Genetics/MSTP Rod Nibbe CWRU Pharmacology Mark Chance CWRU Proteomics Giri Gokulrangan (CWRU Proteomics), Jill Barnholtz-Sloan (CWRU Oncology) Andrew Sloan (CWRU Neurological Surgery), Yanwen Chen (Case Comprehensive Cancer Center)
24 Acknowledgments Ananth Grama Purdue CS Tom LaFramboise CWRU Genetics Matthew Ruffalo CWRU EECS Gurkan Bebek CWRU Proteomics
Network-assisted data analysis
Network-assisted data analysis Bing Zhang Department of Biomedical Informatics Vanderbilt University bing.zhang@vanderbilt.edu Protein identification in shotgun proteomics Protein digestion LC-MS/MS Protein
More informationGene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases
Liu et al. BMC Systems Biology 2012, 6:65 RESEARCH ARTICLE Open Access Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases Yu Liu
More informationBreast cancer. Risk factors you cannot change include: Treatment Plan Selection. Inferring Transcriptional Module from Breast Cancer Profile Data
Breast cancer Inferring Transcriptional Module from Breast Cancer Profile Data Breast Cancer and Targeted Therapy Microarray Profile Data Inferring Transcriptional Module Methods CSC 177 Data Warehousing
More informationVL Network Analysis ( ) SS2016 Week 3
VL Network Analysis (19401701) SS2016 Week 3 Based on slides by J Ruan (U Texas) Tim Conrad AG Medical Bioinformatics Institut für Mathematik & Informatik, Freie Universität Berlin 1 Motivation 2 Lecture
More informationSUPPLEMENTARY FIGURES: Supplementary Figure 1
SUPPLEMENTARY FIGURES: Supplementary Figure 1 Supplementary Figure 1. Glioblastoma 5hmC quantified by paired BS and oxbs treated DNA hybridized to Infinium DNA methylation arrays. Workflow depicts analytic
More informationAdvances in Brain Tumor Research: Leveraging BIG data for BIG discoveries
Advances in Brain Tumor Research: Leveraging BIG data for BIG discoveries Jill Barnholtz-Sloan, PhD Associate Professor & Associate Director for Bioinformatics and Translational Informatics jsb42@case.edu
More informationGene Ontology and Functional Enrichment. Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein
Gene Ontology and Functional Enrichment Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein The parsimony principle: A quick review Find the tree that requires the fewest
More informationCharacteriza*on of Soma*c Muta*ons in Cancer Genomes
Characteriza*on of Soma*c Muta*ons in Cancer Genomes Ben Raphael Department of Computer Science Center for Computa*onal Molecular Biology Soma*c Muta*ons and Cancer Clonal Theory (Nowell 1976) Passenger
More informationThe 16th KJC Bioinformatics Symposium Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis
The 16th KJC Bioinformatics Symposium Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis Tieliu Shi tlshi@bio.ecnu.edu.cn The Center for bioinformatics
More informationRNA-seq Introduction
RNA-seq Introduction DNA is the same in all cells but which RNAs that is present is different in all cells There is a wide variety of different functional RNAs Which RNAs (and sometimes then translated
More informationComputer Science, Biology, and Biomedical Informatics (CoSBBI) Outline. Molecular Biology of Cancer AND. Goals/Expectations. David Boone 7/1/2015
Goals/Expectations Computer Science, Biology, and Biomedical (CoSBBI) We want to excite you about the world of computer science, biology, and biomedical informatics. Experience what it is like to be a
More informationIntegration of high-throughput biological data
Integration of high-throughput biological data Jean Yang and Vivek Jayaswal School of Mathematics and Statistics University of Sydney Meeting the Challenges of High Dimension: Statistical Methodology,
More informationMulti-omics data integration colon cancer using proteogenomics approach
Dept. of Medical Oncology Multi-omics data integration colon cancer using proteogenomics approach DTL Focus meeting, 29 August 2016 Thang Pham OncoProteomics Laboratory, Dept. of Medical Oncology VU University
More informationComputational Thinking in Genome and Proteome Analysis: A Logician s Adventures in Computational Biology. Wong Limsoon
Computational Thinking in Genome and Proteome Analysis: A Logician s Adventures in Computational Biology Wong Limsoon 2 what computational thinking is 3 An example of computational thinking Suppose 20%
More informationUnderstanding Genotype- Phenotype relations in Cancer via Network Approaches
AlgoCSB Algorithmic Methods in Computational and Systems Biology Understanding Genotype- Phenotype relations in Cancer via Network Approaches Teresa Przytycka NIH / NLM / NCBI Phenotypes Journal Wisla
More informationBIOMARKERS IN SEPSIS: DO THEY REALLY GUIDE US? Asist. Prof. M.D. Mehmet Akif KARAMERCAN Gazi University School of Medicine Depertment of Emergency
BIOMARKERS IN SEPSIS: DO THEY REALLY GUIDE US? Asist. Prof. M.D. Mehmet Akif KARAMERCAN Gazi University School of Medicine Depertment of Emergency Medicine 1 NO CONFLICT OF INTEREST 2 We do not fully understand
More informationA quick review. The clustering problem: Hierarchical clustering algorithm: Many possible distance metrics K-mean clustering algorithm:
The clustering problem: partition genes into distinct sets with high homogeneity and high separation Hierarchical clustering algorithm: 1. Assign each object to a separate cluster. 2. Regroup the pair
More informationSSM signature genes are highly expressed in residual scar tissues after preoperative radiotherapy of rectal cancer.
Supplementary Figure 1 SSM signature genes are highly expressed in residual scar tissues after preoperative radiotherapy of rectal cancer. Scatter plots comparing expression profiles of matched pretreatment
More informationRASA: Robust Alternative Splicing Analysis for Human Transcriptome Arrays
Supplementary Materials RASA: Robust Alternative Splicing Analysis for Human Transcriptome Arrays Junhee Seok 1*, Weihong Xu 2, Ronald W. Davis 2, Wenzhong Xiao 2,3* 1 School of Electrical Engineering,
More informationIntroduction to Gene Sets Analysis
Introduction to Svitlana Tyekucheva Dana-Farber Cancer Institute May 15, 2012 Introduction Various measurements: gene expression, copy number variation, methylation status, mutation profile, etc. Main
More informationEXPression ANalyzer and DisplayER
EXPression ANalyzer and DisplayER Tom Hait Aviv Steiner Igor Ulitsky Chaim Linhart Amos Tanay Seagull Shavit Rani Elkon Adi Maron-Katz Dorit Sagir Eyal David Roded Sharan Israel Steinfeld Yossi Shiloh
More informationIdentification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks
Biologists are from Venus, Mathematicians are from Mars, They cosegregate on Earth, And conditionally associate to create a DIGGIT. Identification of Causal Genetic Drivers of Human Disease through Systems-Level
More informationResearch Article RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes
e Scientific World Journal, Article ID 362141, 13 pages http://dx.doi.org/10.1155/2014/362141 Research Article RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes Ashish
More informationComputational Investigation of Homologous Recombination DNA Repair Deficiency in Sporadic Breast Cancer
University of Massachusetts Medical School escholarship@umms Open Access Articles Open Access Publications by UMMS Authors 11-16-2017 Computational Investigation of Homologous Recombination DNA Repair
More informationPackage diggitdata. April 11, 2019
Type Package Title Example data for the diggit package Version 1.14.0 Date 2014-08-29 Author Mariano Javier Alvarez Package diggitdata April 11, 2019 Maintainer Mariano Javier Alvarez
More informationFeature Vector Denoising with Prior Network Structures. (with Y. Fan, L. Raphael) NESS 2015, University of Connecticut
Feature Vector Denoising with Prior Network Structures (with Y. Fan, L. Raphael) NESS 2015, University of Connecticut Summary: I. General idea: denoising functions on Euclidean space ---> denoising in
More informationDominic J Smiraglia, PhD Department of Cancer Genetics. DNA methylation in prostate cancer
Dominic J Smiraglia, PhD Department of Cancer Genetics DNA methylation in prostate cancer Overarching theme Epigenetic regulation allows the genome to be responsive to the environment Sets the tone for
More informationMultiplexed Cancer Pathway Analysis
NanoString Technologies, Inc. Multiplexed Cancer Pathway Analysis for Gene Expression Lucas Dennis, Patrick Danaher, Rich Boykin, Joseph Beechem NanoString Technologies, Inc., Seattle WA 98109 v1.0 MARCH
More informationDeSigN: connecting gene expression with therapeutics for drug repurposing and development. Bernard lee GIW 2016, Shanghai 8 October 2016
DeSigN: connecting gene expression with therapeutics for drug repurposing and development Bernard lee GIW 2016, Shanghai 8 October 2016 1 Motivation Average cost: USD 1.8 to 2.6 billion ~2% Attrition rate
More informationLiposarcoma*Genome*Project*
LiposarcomaGenomeProject July2015! Submittedby: JohnMullen,MD EdwinChoy,MD,PhD GregoryCote,MD,PhD G.PeturNielsen,MD BradBernstein,MD,PhD Liposarcoma Background Liposarcoma is the most common soft tissue
More informationRNA- seq Introduc1on. Promises and pi7alls
RNA- seq Introduc1on Promises and pi7alls DNA is the same in all cells but which RNAs that is present is different in all cells There is a wide variety of different func1onal RNAs Which RNAs (and some1mes
More informationTitle: Pathway-Based Classification of Cancer Subtypes
Title: Pathway-Based Classification of Cancer Subtypes Running title: Pathway-based classification of cancer subtypes Shinuk Kim 1, Mark Kon 1,2*, Charles DeLisi 1 1 Bioinformatics program, Boston University,
More informationThe value of Omics to chemical risk assessment
The value of Omics to chemical risk assessment Timothy W Gant There is a focus on transcriptomics in this talk but for example only. All omics are useful in risk assessment Outline What are we aiming to
More informationIntegrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning
Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning Chihyun Park, Jaegyoon Ahn, Hyunjin Kim, Sanghyun Park* Department of Computer Science, Yonsei University,
More informationIntroduction to Cancer Bioinformatics and cancer biology. Anthony Gitter Cancer Bioinformatics (BMI 826/CS 838) January 20, 2015
Introduction to Cancer Bioinformatics and cancer biology Anthony Gitter Cancer Bioinformatics (BMI 826/CS 838) January 20, 2015 Why cancer bioinformatics? Devastating disease, no cure on the horizon Major
More informationCase Studies on High Throughput Gene Expression Data Kun Huang, PhD Raghu Machiraju, PhD
Case Studies on High Throughput Gene Expression Data Kun Huang, PhD Raghu Machiraju, PhD Department of Biomedical Informatics Department of Computer Science and Engineering The Ohio State University Review
More informationIntroduction to Genetics
Introduction to Genetics Table of contents Chromosome DNA Protein synthesis Mutation Genetic disorder Relationship between genes and cancer Genetic testing Technical concern 2 All living organisms consist
More informationGene Ontology 2 Function/Pathway Enrichment. Biol4559 Thurs, April 12, 2018 Bill Pearson Pinn 6-057
Gene Ontology 2 Function/Pathway Enrichment Biol4559 Thurs, April 12, 2018 Bill Pearson wrp@virginia.edu 4-2818 Pinn 6-057 Function/Pathway enrichment analysis do sets (subsets) of differentially expressed
More informationOMICS Journals are welcoming Submissions
OMICS Journals are welcoming Submissions OMICS International welcomes submissions that are original and technically so as to serve both the developing world and developed countries in the best possible
More informationBIOMEDICAL SCIENCES GRADUATE PROGRAM AUTUMN 2014
THE OHIO STATE UNIVERSITY BIOMEDICAL SCIENCES GRADUATE PROGRAM AUTUMN 2014 Nathan James Dissinger PhD Candidate Role of HTLV-1 HBZ and HTLV-2 APH-2 in Disease Outcome November 5 th, 2014 84 Veterinary
More informationAbout OMICS International
About OMICS International OMICS International through its Open Access Initiative is committed to make genuine and reliable contributions to the scientific community. OMICS International hosts over 700
More informationLesson 19 Study Guide: Medical Biotechnology Cancer Treatment
URI CMB 190 Issues in Biotechnology Lesson 19 Study Guide: Medical Biotechnology Cancer Treatment 11. There have been genes that have been identified to be associated with certain types of cancer. Microarrays
More informationSUPPLEMENTARY INFORMATION
doi:10.1038/nature10866 a b 1 2 3 4 5 6 7 Match No Match 1 2 3 4 5 6 7 Turcan et al. Supplementary Fig.1 Concepts mapping H3K27 targets in EF CBX8 targets in EF H3K27 targets in ES SUZ12 targets in ES
More informationClassification of cancer profiles. ABDBM Ron Shamir
Classification of cancer profiles 1 Background: Cancer Classification Cancer classification is central to cancer treatment; Traditional cancer classification methods: location; morphology, cytogenesis;
More informationUniversity of Pittsburgh Cancer Institute UPMC CancerCenter. Uma Chandran, MSIS, PhD /21/13
University of Pittsburgh Cancer Institute UPMC CancerCenter Uma Chandran, MSIS, PhD chandran@pitt.edu 412-648-9326 2/21/13 University of Pittsburgh Cancer Institute Founded in 1985 Director Nancy Davidson,
More informationPredicting outcome from cancer data
Predicting outcome from cancer data Jeffrey Chuang The Jackson Laboratory for Genomic Medicine Drowning in data, thirsting for knowledge 2 Clinical outcome across cancer types TCGA Cancer types 5-year
More informationHALLA KABAT * Outreach Program, mircore, 2929 Plymouth Rd. Ann Arbor, MI 48105, USA LEO TUNKLE *
CERNA SEARCH METHOD IDENTIFIED A MET-ACTIVATED SUBGROUP AMONG EGFR DNA AMPLIFIED LUNG ADENOCARCINOMA PATIENTS HALLA KABAT * Outreach Program, mircore, 2929 Plymouth Rd. Ann Arbor, MI 48105, USA Email:
More informationgenomics for systems biology / ISB2020 RNA sequencing (RNA-seq)
RNA sequencing (RNA-seq) Module Outline MO 13-Mar-2017 RNA sequencing: Introduction 1 WE 15-Mar-2017 RNA sequencing: Introduction 2 MO 20-Mar-2017 Paper: PMID 25954002: Human genomics. The human transcriptome
More informationSUPPLEMENTARY FIGURES
SUPPLEMENTARY FIGURES Figure S1. Clinical significance of ZNF322A overexpression in Caucasian lung cancer patients. (A) Representative immunohistochemistry images of ZNF322A protein expression in tissue
More informationDiscovery and Validation of Prognostic Genomic Based Signatures in High Risk Bladder Cancer Following Cystectomy
Discovery and Validation of Prognostic Genomic Based Signatures in High Risk Bladder Cancer Following Cystectomy Anirban P. Mitra, M.D., Ph.D. Center for Personalized Medicine University of Southern California
More informationSpontaneous canine malignancies: Models for precision cancer medicine
National Cancer Institute Spontaneous canine malignancies: Models for precision cancer medicine Amy K. LeBlanc, DVM DACVIM (Oncology) Director, NCI Comparative Oncology Program NIH/NCI Center for Cancer
More informationBIOCRATES Life Sciences AG
BIOCRATES Life Sciences AG The Deep Phenotyping Company European Business Development Conference BIO-Deutschland, 23.-24.09.2013 Dr. Wulf Fischer-Knuppertz CEO 1 The Company Targeted Metabolomics Founded
More informationThe Role and Importance of Research
The Role and Importance of Research What Research Is and Isn t A Model of Scientific Inquiry Different Types of Research Experimental Research What Method to Use When Applied and Basic Research Increasing
More informationLinking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogeneinduced
Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogeneinduced Signaling The MIT Faculty has made this article openly available. Please share how this
More informationRESEARCHER S NAME: Làszlò Tora RESEARCHER S ORGANISATION: Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC)
Thursday 5 November EU-India PARTNERING EVENT Theme: Health RESEARCHER S NAME: Làszlò Tora RESEARCHER S ORGANISATION: Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS, INSERM,
More informationPCxN: The pathway co-activity map: a resource for the unification of functional biology
PCxN: The pathway co-activity map: a resource for the unification of functional biology Sheffield Institute for Translational Neurosciences Center for Integrative Genome Translation GENOME INFORMATICS
More informationThe Value of Omics in Cardiovascular Research: Getting More Comfortable, More Frustrated or More Curious
The Value of Omics in Cardiovascular Research: Getting More Comfortable, More Frustrated or More Curious Daniel Levy, MD Framingham Heart Study Population Sciences Branch National Heart, Lung, and Blood
More informationNovel Biomarkers (Kallikreins) for Prognosis and Therapy Response in Ovarian cancer
Novel Biomarkers (Kallikreins) for Prognosis and Therapy Response in Ovarian cancer Eleftherios P. Diamandis, M.D., Ph.D., FRCP(C) EORTC-NCI-ASCO Meeting,November 16, 2007 Yousef GM, Diamandis EP. Endocr.
More informationMicroRNA expression profiling and functional analysis in prostate cancer. Marco Folini s.c. Ricerca Traslazionale DOSL
MicroRNA expression profiling and functional analysis in prostate cancer Marco Folini s.c. Ricerca Traslazionale DOSL What are micrornas? For almost three decades, the alteration of protein-coding genes
More informationLung Cancer. Public Outcomes Report. Submitted by G. Brooks Brennan, MD. Based on 2015 data
Public Outcomes Report Lung Cancer Submitted by G. Brooks Brennan, MD 2016 Based on 2015 data Lung cancer remains a significant factor in the morbidity and mortality of the United States population. There
More informationProcessing, integrating and analysing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data
Processing, integrating and analysing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data Bioinformatics methods, models and applications to disease Alex Essebier ChIP-seq experiment To
More informationRECENT ADVANCES IN THE MOLECULAR DIAGNOSIS OF BREAST CANCER
Technology Transfer in Diagnostic Pathology. 6th Central European Regional Meeting. Cytopathology. Balatonfüred, Hungary, April 7-9, 2011. RECENT ADVANCES IN THE MOLECULAR DIAGNOSIS OF BREAST CANCER Philippe
More informationWhat Math Can Tell You About Cancer
What Math Can Tell You About Cancer J. B. University of Hawai i at Mānoa Hofstra University, October 2017 Overview The human body is complicated. Cancer disrupts many normal processes. Mathematical analysis
More informationMolecular BioSystems PAPER. Gene module based regulator inference identifying mir-139 as a tumor suppressor in colorectal cancer.
Molecular BioSystems PAPER Cite this: Mol. BioSyst., 2014, 10, 3249 Gene module based regulator inference identifying mir-139 as a tumor suppressor in colorectal cancer Jin Gu, * a Yang Chen, a Huiya Huang,
More information8/1/2017. Imaging and Molecular Biomarkers of Lung Cancer Prognosis. Disclosures. The Era of Precision Oncology
Imaging and Molecular Biomarkers of Lung Cancer Prognosis Ruijiang Li, PhD Assistant Professor of Radiation Oncology 08/01/2017 Stanford University Department of Radiation Oncology School of Medicine Disclosures
More informationIdentification of Tissue Independent Cancer Driver Genes
Identification of Tissue Independent Cancer Driver Genes Alexandros Manolakos, Idoia Ochoa, Kartik Venkat Supervisor: Olivier Gevaert Abstract Identification of genomic patterns in tumors is an important
More informationAnalysis of Massively Parallel Sequencing Data Application of Illumina Sequencing to the Genetics of Human Cancers
Analysis of Massively Parallel Sequencing Data Application of Illumina Sequencing to the Genetics of Human Cancers Gordon Blackshields Senior Bioinformatician Source BioScience 1 To Cancer Genetics Studies
More informationPioneering vaccines that transform lives.
Pioneering vaccines that transform lives. Immunomic Therapeutics, Inc. LAMP-Vax for Glioblastoma: CMV-LAMP-Vax Executive Summary Executive Summary pp65-lamp-vax First Line Therapy for Glioblastoma Multiforme
More informationProbabilistic retrieval and visualization of relevant experiments
Probabilistic retrieval and visualization of relevant experiments Samuel Kaski Joint work with: José Caldas, Nils Gehlenborg, Ali Faisal, Alvis Brazma Motivation 2 How to best use collections of measurement
More informationFUNCTIONAL GENE SETS IN POST-TRAUMATIC STRESS DISORDER
PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY C h e m i s t r y a n d B i o l o g y 2016, 1, p. 43 48 B i o l o g y FUNCTIONAL GENE SETS IN POST-TRAUMATIC STRESS DISORDER A. A. ARAKELYAN Institute of Molecular
More informationSpontaneous canine malignancies: Models for precision cancer medicine
National Cancer Institute Spontaneous canine malignancies: Models for precision cancer medicine Amy K. LeBlanc, DVM DACVIM (Oncology) Director, NCI Comparative Oncology Program NIH/NCI Center for Cancer
More informationCS2220 Introduction to Computational Biology
CS2220 Introduction to Computational Biology WEEK 8: GENOME-WIDE ASSOCIATION STUDIES (GWAS) 1 Dr. Mengling FENG Institute for Infocomm Research Massachusetts Institute of Technology mfeng@mit.edu PLANS
More informationEvaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value
Lehmann et al. BMC Cancer (2015) 15:179 DOI 10.1186/s12885-015-1102-7 RESEARCH ARTICLE Open Access Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic
More informationUnderstanding DNA Copy Number Data
Understanding DNA Copy Number Data Adam B. Olshen Department of Epidemiology and Biostatistics Helen Diller Family Comprehensive Cancer Center University of California, San Francisco http://cc.ucsf.edu/people/olshena_adam.php
More informationDigitizing the Proteomes From Big Tissue Biobanks
Digitizing the Proteomes From Big Tissue Biobanks Analyzing 24 Proteomes Per Day by Microflow SWATH Acquisition and Spectronaut Pulsar Analysis Jan Muntel 1, Nick Morrice 2, Roland M. Bruderer 1, Lukas
More informationUsing Bayesian Networks to Analyze Expression Data. Xu Siwei, s Muhammad Ali Faisal, s Tejal Joshi, s
Using Bayesian Networks to Analyze Expression Data Xu Siwei, s0789023 Muhammad Ali Faisal, s0677834 Tejal Joshi, s0677858 Outline Introduction Bayesian Networks Equivalence Classes Applying to Expression
More informationComparison of Triple Negative Breast Cancer between Asian and Western Data Sets
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops Comparison of Triple Negative Breast Cancer between Asian and Western Data Sets Lee H. Chen Bioinformatics and Biostatistics
More informationWhat yield in the last decade about Molecular Diagnostics in Neuro
What yield in the last decade about Molecular Diagnostics in Neuro Oncology? Raphael Salles S.Medeiros Neuropathologist at HC FMUSP Clinical Research Project Manager at Oncology department at Hospital
More information8/1/2016. FDG uptake in a heterogeneous microenvironment: A single-cell study. Heterogeneity of FDG uptake in tumors grafts. Goals of the study
FDG uptake in a heterogeneous microenvironment: A single-cell study J O IN T AAPM- W M I S S YMP O S IU M: M E TA B O L IC I MA G IN G OF C A N C E R Guillem Pratx, PhD Radiation Oncology & Medical Physics
More informationPersonalized Therapy for Prostate Cancer due to Genetic Testings
Personalized Therapy for Prostate Cancer due to Genetic Testings Stephen J. Freedland, MD Professor of Urology Director, Center for Integrated Research on Cancer and Lifestyle Cedars-Sinai Medical Center
More informationPHD STUDENTSHIP PROJECT PROPOSAL
The Institute of Cancer Research PHD STUDENTSHIP PROJECT PROPOSAL PROJECT DETAILS Project Title: Short Project Title: SUPERVISORY TEAM Primary Supervisor(s): Understanding therapeutic responses in BRCA
More informationComplexity DNA. Genome RNA. Transcriptome. Protein. Proteome. Metabolites. Metabolome
DNA Genome Complexity RNA Transcriptome Systems Biology Linking all the components of a cell in a quantitative and temporal manner Protein Proteome Metabolites Metabolome Where are the functional elements?
More informationBig data vs. the individual liver from a regulatory perspective
Big data vs. the individual liver from a regulatory perspective Robert Schuck, Pharm.D., Ph.D. Genomics and Targeted Therapy Office of Clinical Pharmacology Center for Drug Evaluation and Research Food
More informationPancreas Quizzes c. Both A and B a. Directly into the blood stream (not using ducts)
Pancreas Quizzes Quiz 1 1. The pancreas produces hormones. Which type of hormone producing organ is the pancreas? a. Endocrine b. Exocrine c. Both A and B d. Neither A or B 2. Endocrine indicates hormones
More informationNON-ALCOHOLIC STEATOHEPATITIS AND NON-ALCOHOLIC FATTY LIVER DISEASES
NON-ALCOHOLIC STEATOHEPATITIS AND NON-ALCOHOLIC FATTY LIVER DISEASES Preface Zobair M. Younossi xiii Epidemiology and Natural History of NAFLD and NASH 1 Janus P. Ong and Zobair M. Younossi Understanding
More informationMicro RNA Research. Ken Kosik. Harriman Professor, Department of Molecular, Cellular & Developmental Biology and Biomolecular Sciences & Engr.
Ken Kosik Harriman Professor, Department of Molecular, Cellular & Developmental Biology and Biomolecular Sciences & Engr. Program Co-Director, Neurosciences Research Institute Micro RNA Research Neuroscience
More informationSingle SNP/Gene Analysis. Typical Results of GWAS Analysis (Single SNP Approach) Typical Results of GWAS Analysis (Single SNP Approach)
High-Throughput Sequencing Course Gene-Set Analysis Biostatistics and Bioinformatics Summer 28 Section Introduction What is Gene Set Analysis? Many names for gene set analysis: Pathway analysis Gene set
More information2018 Internship Directory: High School Summer Research Experience Program in Cancer Science (10 pages)
2018 Internship Directory: High School Summer Mentor area(s) Internship Boyko Atanassov Dept. of Pharmacology and Therapeutics /Boyko-Atanassov cellular biology Defining the functions of Ubiquitin Specific
More informationComparison of open chromatin regions between dentate granule cells and other tissues and neural cell types.
Supplementary Figure 1 Comparison of open chromatin regions between dentate granule cells and other tissues and neural cell types. (a) Pearson correlation heatmap among open chromatin profiles of different
More informationA Strategic Centre for Translational Cancer Research Lund University
A Strategic Centre for Translational Cancer Research Lund University Our recent achievements During the recent five years CREATE Health has focused on biomarkers mainly for cancer diagnosis and prognosis.
More informationTitle: Human breast cancer associated fibroblasts exhibit subtype specific gene expression profiles
Author's response to reviews Title: Human breast cancer associated fibroblasts exhibit subtype specific gene expression profiles Authors: Julia Tchou (julia.tchou@uphs.upenn.edu) Andrew V Kossenkov (akossenkov@wistar.org)
More informationBIOMEDICAL SCIENCES GRADUATE PROGRAM SUMMER 2014
THE OHIO STATE UNIVERSITY BIOMEDICAL SCIENCES GRADUATE PROGRAM SUMMER 2014 Elizabeth Stofko Barrie PhD Candidate Genetic Factors Regulating Expression of Dopaminergic Genes July 15 th, 2014 159 DHLRI 1:00
More informationCorporate Medical Policy
Corporate Medical Policy Proteomics-based Testing Related to Ovarian Cancer File Name: Origination: Last CAP Review: Next CAP Review: Last Review: proteomics_based_testing_related_to_ovarian_cancer 7/2010
More informationCancer Science nd International Conference on Oncology & Cancer Science. August 06-08, 2018 Berlin, Germany. Hosting Organization
Cancer Science 2018 2nd International Conference on Oncology & Cancer Science August 06-08, 2018 Berlin, Germany Hosting Organization Invitation Cenetri Publishing Group takes keen delectation and highly
More informationBIO333 Comparative Physiology and Pharmacology of Sleep. Genetics of Sleep December 3, Raphaelle Winsky-Sommerer, PhD, PD
BIO333 Comparative Physiology and Pharmacology of Sleep Genetics of Sleep December 3, 2011 Raphaelle Winsky-Sommerer, PhD, PD r.winsky-sommerer@surrey.ac.uk Genetics of Sleep Quantitative traits are determined
More informationPathAct: a novel method for pathway analysis using gene expression profiles
www.bioinformation.net Hypothesis Volume 9(8) PathAct: a novel method for pathway analysis using gene expression profiles Kaoru Mogushi & Hiroshi Tanaka* Department of Bioinformatics, Division of Medical
More informationGene Regulation Part 2
Michael Cummings Chapter 9 Gene Regulation Part 2 David Reisman University of South Carolina Other topics in Chp 9 Part 2 Protein folding diseases Most diseases are caused by mutations in the DNA that
More informationThe epigenetic landscape of T cell subsets in SLE identifies known and potential novel drivers of the autoimmune response
Abstract # 319030 Poster # F.9 The epigenetic landscape of T cell subsets in SLE identifies known and potential novel drivers of the autoimmune response Jozsef Karman, Brian Johnston, Sofija Miljovska,
More informationSupplementary Figure 1: Digitoxin induces apoptosis in primary human melanoma cells but not in normal melanocytes, which express lower levels of the
Supplementary Figure 1: Digitoxin induces apoptosis in primary human melanoma cells but not in normal melanocytes, which express lower levels of the cardiac glycoside target, ATP1A1. (a) The percentage
More informationThe Cancer Genome Atlas & International Cancer Genome Consortium
The Cancer Genome Atlas & International Cancer Genome Consortium Session 3 Dr Jason Wong Prince of Wales Clinical School Introductory bioinformatics for human genomics workshop, UNSW 31 st July 2014 1
More information