NDLI: A Comprehensive Statistical Model for Cell Signaling

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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 13

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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 8

Issue 6, November 2011

Issue 5, September 2011

Issue 4, July 2011

Issue 3, May 2011

A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

A Comprehensive Statistical Model for Cell Signaling

A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks

A Max-Flow-Based Approach to the Identification of Protein Complexes Using Protein Interaction and Microarray Data

A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks

A Theoretical Analysis of the Prodrug Delivery System for Treating Antibiotic-Resistant Bacteria

Cancer Classification from Gene Expression Data by NPPC Ensemble

Component-Based Modeling and Reachability Analysis of Genetic Networks

Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

FEAST: Sensitive Local Alignment with Multiple Rates of Evolution

Identifiability of Two-Tree Mixtures for Group-Based Models

Incorporating Nonlinear Relationships in Microarray Missing Value Imputation

Manipulating the Steady State of Metabolic Pathways

Multitask Learning for Protein Subcellular Location Prediction

Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms

Prediction of Protein Functions with Gene Ontology and Interspecies Protein Homology Data

Regular Networks Can be Uniquely Constructed from Their Trees

3D Shape Reconstruction of Loop Objects in X-Ray Protein Crystallography

TCLUST: A Fast Method for Clustering Genome-Scale Expression Data

TRIAL: A Tool for Finding Distant Structural Similarities

True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction

A Note on the Fixed Parameter Tractability of the Gene-Duplication Problem

Identifying Relevant Data for a Biological Database: Handcrafted Rules versus Machine Learning

Toward Better Understanding of Protein Secondary Structure: Extracting Prediction Rules

Issue 2, March 2011

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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 7

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 6

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 5

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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 3

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 2

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 1

A Comprehensive Statistical Model for Cell Signaling

Content Provider	ACM Digital Library
Author	Younes, Laurent Yoruk, Erdem Ochs, Michael F. Geman, Donald
Abstract	Protein signaling networks play a central role in transcriptional regulation and the etiology of many diseases. Statistical methods, particularly Bayesian networks, have been widely used to model cell signaling, mostly for model organisms and with focus on uncovering connectivity rather than inferring aberrations. Extensions to mammalian systems have not yielded compelling results, due likely to greatly increased complexity and limited proteomic measurements in vivo. In this study, we propose a comprehensive statistical model that is anchored to a predefined core topology, has a limited complexity due to parameter sharing and uses micorarray data of mRNA transcripts as the only observable components of signaling. Specifically, we account for cell heterogeneity and a multilevel process, representing signaling as a Bayesian network at the cell level, modeling measurements as ensemble averages at the tissue level, and incorporating patient-to-patient differences at the population level. Motivated by the goal of identifying individual protein abnormalities as potential therapeutical targets, we applied our method to the RAS-RAF network using a breast cancer study with 118 patients. We demonstrated rigorous statistical inference, established reproducibility through simulations and the ability to recover receptor status from available microarray data.
Starting Page	592
Ending Page	606
Page Count	15
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2010.87
Volume Number	8
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cell signaling networks, signaling protein, microarray, statistical learning, Bayesian networks, stochastic approximation expectation maximization, Gibbs sampling, Mann-Whitney-Wilcoxon test.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Comprehensive Statistical Model for Cell Signaling

Mann-Whitney U-Test The Mann-Whitney-Wilcoxon ( MWW ) or Wilcoxon Rank-Sum Test The Mann-Whitney-Wilcoxon ( MWW ) or Wilcoxon Rank-Sum Test

A Comprehensive Statistical Model for Cell Signaling and Protein Activity Inference

Nonparametric Tests of Differences in Medians: Comparison of the WilcoxonMann-Whitney and Robust Rank-Order Tests," mimeo (2003)

A Wilcoxon-Mann-Whitney spatial scan statistic for functional data

Wilcoxon–Mann–Whitney Test

Cluster permutation analysis for EEG series based on non-parametric Wilcoxon–Mann–Whitney statistical tests

A weighted generalization of the Mann–Whitney–Wilcoxon statistic (2002)

RNA modeling using Gibbs sampling and stochastic context free grammars (1994)

A Comprehensive Statistical Model for Cell Signaling

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