Impact of Formal Methods in Biology and Medicine

Slides:



Advertisements
Similar presentations
Predictive Analysis of Gene Expression Data from Human SAGE Libraries Alexessander Alves* Nikolay Zagoruiko + Oleg Okun § Olga Kutnenko + Irina Borisova.
Advertisements

M. Kathleen Kerr “Design Considerations for Efficient and Effective Microarray Studies” Biometrics 59, ; December 2003 Biostatistics Article Oncology.
Sino-Danish Breast Cancer Research Centre Beijing Genomic Institute, Shenzhen University of Copenhagen University of Århus University of Southern Denmark.
Departments - Surgery - Gerontology and Geriatrics Department of SurgeryDepartment of Gerontology & Geriatrics Prof. dr. C.J.H. van de VeldeProf. dr. R.G.J.
Introduction to Genomics, Bioinformatics & Proteomics Brian Rybarczyk, PhD PMABS Department of Biology University of North Carolina Chapel Hill.
ONCOMINE: A Bioinformatics Infrastructure for Cancer Genomics
Introduction of Cancer Molecular Epidemiology Zuo-Feng Zhang, MD, PhD University of California Los Angeles.
Introduction to molecular networks Sushmita Roy BMI/CS 576 Nov 6 th, 2014.
References 1.Salazar R, Roepman P, Capella G et al. Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J.
Metastatic Breast Cancer: One Size Does Not Fit All Clifford Hudis, M.D. Chief, Breast Cancer Medicine Service MSKCC.
Georgia Wiesner, MD CREC June 20, GATACAATGCATCATATG TATCAGATGCAATATATC ATTGTATCATGTATCATG TATCATGTATCATGTATC ATGTATCATGTCTCCAGA TGCTATGGATCTTATGTA.
Taiwan 2000 PETACC 3 ASCO 2009 Molecular markers in colon cancer have a stage specific prognostic value. Results of the translational study on the PETACC.
ICBP, Stanford University 1 Implication Networks from Large Gene-expression Datasets Debashis Sahoo PhD Candidate, Electrical Engineering, Stanford University.
Stanford University Boolean Analysis of Large Gene-expression Datasets Debashis Sahoo PhD Candidate, Electrical Engineering Joint work with David Dill,
Sgroi DC et al. Proc SABCS 2012;Abstract S1-9.
Insert Program or Hospital Logo Introduction Melanoma is notoriously resistant to chemotherapy. While surgical resection and adjuvant chemotherapy can.
A Quantitative Multi-Gene RT-PCR Assay for Prediction of Recurrence in Stage II Colon Cancer (CC): Selection of the Genes in 4 Large Studies and Results.
©Edited by Mingrui Zhang, CS Department, Winona State University, 2008 Identifying Lung Cancer Risks.
DEREK KENYENSO MENTOR: DR. JAYA SATAGOPAN HOSPITAL: MSKCC DEPARTMENT: EPIDEMIOLOGY/BIOSTTISTICS.
INCREASED EXPRESSION OF PROTEIN KINASE CK2  SUBUNIT IN HUMAN GASTRIC CARCINOMA Kai-Yuan Lin 1 and Yih-Huei Uen 1,2,3 1 Department of Medical Research,
Bioinformatics for Stem Cell Lecture 2 Debashis Sahoo, PhD.
Cross-platform comparisons of microarray data. Elucidation of common differentially expressed genes in bladder cancer. Apostolos Zaravinos 1, George I.
CHFR METHYLATION AS AN EPIGENETIC MARKER FOR RECURRENCE OF COLON CANCER M. D. Anderson Cancer Center, Houston, Texas Motofumi Tanaka, Salil Sethi, Donghui.
Class 23, 2001 CBCl/AI MIT Bioinformatics Applications and Feature Selection for SVMs S. Mukherjee.
7th Global Summit October 05-07, 2015 on Cancer Therapy Dubai, UAE CD49d and CD26 are independent prognostic markers for disease progression in patients.
Prof. Yechiam Yemini (YY) Computer Science Department Columbia University (c)Copyrights; Yechiam Yemini; Lecture 2: Introduction to Paradigms 2.3.
Equivalent Opposite PTPRC low  CD19 low FAM60A low  NUAK1 high XIST high  RPS4Y1 low COL3A1 high  SPARC high Boolean analysis of large gene-expression.
Pan-cancer analysis of prognostic genes Jordan Anaya Omnes Res, In this study I have used publicly available clinical and.
Eigengenes as biological signatures Dr. Habil Zare, PhD PI of Oncinfo Lab Assistant Professor, Department of Computer Science Texas State University 5.
INFERENCE FOR BIG DATA Mike Daniels The University of Texas at Austin Department of Statistics & Data Sciences Department of Integrative Biology.
Inferring Regulatory Networks from Gene Expression Data BMI/CS 776 Mark Craven April 2002.
David Amar, Tom Hait, and Ron Shamir
Mamounas EP et al. Proc SABCS 2012;Abstract S1-10.
Introduction to Bioinformatics
1. SELECTION OF THE KEY GENE SET 2. BIOLOGICAL NETWORK SELECTION
Immunoscore Prognostic in Colon Cancer
Statistical Applications in Biology and Genetics
Gene expression.
Impact of Formal Methods in Biology and Medicine Final Review
Impact of Formal Methods in Biology and Medicine
Graphing / Plotting Points Review
Bioinformatics for Stem Cell Lecture 2
High-level TNFSF13 predict a good response to post-operative chemotherapy in patients with basal-like breast cancer: A systematic review 林惠鈺1,2 歸家豪1,3.
Figure S1 C A B D E F G H I MHCC-97H Control NC pCDH kDa
Impact of Formal Methods in Biology and Medicine
MiDReG: Mining Developmentally Regulated Genes
KEY CONCEPT Entire genomes are sequenced, studied, and compared.
Department of Computer Science
Loyola Marymount University
Prognostic Significance of TAZ Expression in Resected Non-Small Cell Lung Cancer  Mian Xie, MD, PhD, Li Zhang, MD, Chao-Sheng He, MD, Jin-Hui Hou, MD,
The mRNA stem cell signature.
KEY CONCEPT Entire genomes are sequenced, studied, and compared.
Recurrence-Associated Long Non-coding RNA Signature for Determining the Risk of Recurrence in Patients with Colon Cancer  Meng Zhou, Long Hu, Zicheng.
Published online September 20, 2017 by JAMA Surgery
Volume 10, Issue 5, Pages (May 2018)
Gene Expression Analysis
Robust diagnosis of DLBCL from gene expression data from different laboratories DIMACS - RUTCOR Workshop on Boolean and Pseudo-Boolean Functions in Memory.
Uni- & Multivariate Analysis Sponsored by GERCOR (
Loyola Marymount University
Altered Caspase-8 Expression
Loyola Marymount University
Coiffier B et al. Proc ASH 2011;Abstract 265.
Loyola Marymount University
Loyola Marymount University
Department of Computer Science
Supplementary Figure S1
EN1 expression in breast cancer and clinical outcome.
A, TSG-6 staining in human prostate.
Introduction to Biological Systems
Volume 28, Issue 3, Pages e7 (July 2019)
Presentation transcript:

Impact of Formal Methods in Biology and Medicine Debashis Sahoo Department of Computer Science CSE291 – H00 – Lecture 1

Outline Introduction History of Bioinformatics Introduction to computing Data collection Experiment design Data analysis

Biological Systems What I would like to cover are these points: More formal Basic intro to boolean logic Describe with example Show how these are applied Markers vs cell types Normal vs cancer

Tissue What I would like to cover are these points: More formal Basic intro to boolean logic Describe with example Show how these are applied Markers vs cell types Normal vs cancer

Boolean Analysis George Boole (1815-1864) Two values High, Low 1, 0 Boolean operations AND, OR, NOT Implication x  y Add High/Low – maybe remove True/False Remove figures: Simple Boolean logic that can simplify incredbly complex continuous data Infer biological meaning.

Boolean Implication Pair of genes. Four quadrants. Sparse quadrants. ACPP GABRB1 45,000 Affymetrix microarrays Pair of genes. Four quadrants. Sparse quadrants. Boolean relationships. If ACPP high, then GABRB1 low If GABRB1 high, then ACPP low Put the introductory slides How many microarrays Seems like a fundamental… If -> then Describe x and y axis. Describe a point. Statistical tests for identifying sparse quadrant.

Modeling Colon Tissue What I would like to cover are these points: More formal Basic intro to boolean logic Describe with example Show how these are applied Markers vs cell types Normal vs cancer

Normal Colon Tissue http://www.siumed.edu/~dking2/erg/GI125b.htm Make a case before http://www.siumed.edu/~dking2/erg/GI125b.htm

Simple Patterns in Cancer Dataset Gene CA1, Gene KRT20 Dalerba*, Kalisky* and Sahoo* et al. Nat Biotechnol. 2011 Nov 13;29(12):1120-7.

Search for the Stem Cell Genes X Y Expression KRT20 ALCAM Colon Cell Differentiation Criteria: 1. KRT20 high => X high 2. Y high => ALCAM high 3. KRT20 high => Y low 4. X low => ALCAM high

Search for High Risk Colon Cancer Patients List of genes fulfilling the pattern X low => ALCAM high GPX2 CDX2 EPS8L3 GPR35 LAD1 DTX4 CDX1 USH1C VIL1 PPP1R14D MUC3B PLEKHG6 IHH ACOT11 NHEJ1 Change this Dalerba P et al. N Engl J Med 2016;374:211-222.

CDX2 mRNA Expression and Disease-free Survival Discovery Dataset (JSTO, n=466) Figure 2. Relationship between CDX2 Expression and Disease-free Survival in the NCBI-GEO Discovery Data Set. Analysis of CDX2 messenger RNA (mRNA) expression in the NCBI-GEO discovery data set revealed the presence of a minority subgroup of CDX2-negative colon cancers that were characterized by high ALCAM mRNA expression levels (Panel A) and that were associated with a lower rate of 5-year disease-free survival than CDX2-positive colon cancers (Panel B). In Panel A, each circle in the scatter plot represents one patient sample. The association between CDX2-negative cancers and a lower rate of disease-free survival remained significant in a multivariate analysis that excluded tumor stage, tumor grade, age, and sex as confounding variables (Panel C). Dalerba P et al. N Engl J Med 2016;374:211-222.

CDX2 Protein Expression and Disease-free Survival Validation Dataset (NCI CDP, n=466) Change colors Dalerba P et al. N Engl J Med 2016;374:211-222.

Dalerba P et al. N Engl J Med 2016;374:211-222. CDX2 Expression and Benefit from Adjuvant Chemotherapy. Stage II Colon Cancer (Pooled dataset, n=669) Figure 5. Relationship between CDX2 Expression and Benefit from Adjuvant Chemotherapy. The relationship between CDX2 expression and benefit from adjuvant chemotherapy was evaluated in a pooled database of 669 patients with stage II disease (Panel A) and 1228 patients with stage III disease (Panel B) from four independent data sets (NCBI-GEO, NCI-CDP, NSABP C-07, and Stanford TMAD). Among all patients with stage II disease in the entire database, treatment with adjuvant chemotherapy was not associated with a higher rate of 5-year disease-free survival. However, treatment with adjuvant chemotherapy was strongly associated with a higher rate of 5-year disease-free survival in the CDX2-negative subgroup, but it was not associated with a higher rate of 5-year disease-free survival in the CDX2-positive subgroup. Among patients with stage III disease, treatment with adjuvant chemotherapy was associated with a higher rate of 5-year disease-free survival in the entire database and in both the CDX2-negative and CDX2-positive subgroups. A test for an interaction between the biomarker and the treatment indicated that in both stage II and stage III disease, the benefit associated with adjuvant chemotherapy was superior among CDX2-negative patients than among CDX2-positive patients. Dalerba P et al. N Engl J Med 2016;374:211-222.