bag of na ve bayes biomarker selection and classification
play

Bag of Nave Bayes: biomarker selection and classification from - PowerPoint PPT Presentation

Sep 13, 2022 •375 likes •462 views

Bag of Nave Bayes: biomarker selection and classification from Genome-Wide SNP data Francesco Sambo Context Complex disease, with hypothesized but still not understood genetic origin Genome Wide Association Study (GWAS) O(10 6 ) Single

  1. Bag of Naïve Bayes: biomarker selection and classification from Genome-Wide SNP data Francesco Sambo

  2. Context Complex disease, with hypothesized but still not understood genetic origin Genome Wide Association Study (GWAS) • O(10 6 ) Single Nucleotyde Polymorphisms (SNPs) • O(10 3 ) case / control individuals Objectives: 1.Biomarker Selection 2.Classification

  3. Bag of Naïve Bayes (BoNB) • Both classification and biomarker selection • Based on Naïve Bayes classification • Main features: a) Ensamble of Naïve Bayes Classifiers (NBC), robustness b) Novel strategy for ranking and selecting attributes for each NBC, attribute independence c) Permutation-based procedure for biomarker selection, based on marginal utility.

  4. Bagging (Bootstrap AGGregatING) Bootstrap Ensemble of NBCs GWAS Data D 1 NBC 1 Prediction 1 SNPs subjects D Weighted Prediction D B NBC B Prediction B • B bootstrap replicates, sampled with replacement from D • B Naive Bayes Classifiers, each trained on a D b • Outcome: average of the B predictions

  5. NBC attribute selection (SNPs) Bootstrap Ensemble of NBCs GWAS Data Attribute Selection D 1 NBC 1 Prediction 1 SNPs oob 1 subjects D Weighted Prediction oob B D B NBC B Prediction B • Ranking: training error when SNP is used as single attribute • Selection: top ranked, uncorrelated SNPs ( r 2 < 0.1 if dist < 1 Mb ) • Number of selected attributes increased, as long as classification accuracy increases on the Out-Of-Bag (OOB) sets

  6. Biomarker Selection Bootstrap Ensemble of NBCs GWAS Data Attribute Selection D 1 NBC 1 Prediction 1 SNPs oob 1 subjects D Biomarker Selection Weighted Prediction oob B D B NBC B Prediction B • Random permutation of the genotype of NBC attributes in OOBs • Measure decrease in accuracy on OOBs • Wilcoxon signed-rank test for significance

  7. Results WTCCC case / control study on Type 1 Diabetes • 458376 SNPs, 1963 T1D cases, 2938 controls Biomarker Selection Predictive accuracy Matthews Correlation Coefficient rs ID chr gene rs6679677 1 RSBN1 rs9273363 6 MHC region rs3101942 6 MHC region rs492899 6 MHC region rs6936863 6 MHC region rs805301 6 MHC region rs9275418 6 MHC region rs2856688 6 MHC region

  8. Conclusions • BoNB effective for both classification and biomarker selection • Advantages of bagging:  Higher generalization ability  Sound and principled procedure for biomarker selection • Advantages of Naïve Bayes:  No pre-specified model of genetic effect  Seamless handling of missing values

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Bayes Classifiers Nave Bayes Classification Patrick Mair Bayes Classifiers Weather data

Bayes Classifiers Nave Bayes Classification Patrick Mair Bayes Classifiers Weather data

Bayes Classifiers Nave Bayes Classification Patrick Mair Bayes Classifiers Weather data set Predictors: Outlook, Temperature, Humidity, Windy. Response: Play (yes/no) Starting point Cross classification: Predictor/Response

98 views • 6 slides
STAT 339 Naive Bayes Classification 8-10 March 2017 Colin Reimer Dawson Outline Naive Bayes

STAT 339 Naive Bayes Classification 8-10 March 2017 Colin Reimer Dawson Outline Naive Bayes

STAT 339 Naive Bayes Classification 8-10 March 2017 Colin Reimer Dawson Outline Naive Bayes Classification Naive Bayes Classifier Using MLE Parameter Estimates Bayesian Naive Bayes Continuous Features Mixed Feature Types Back to Supervised

530 views • 21 slides
Classification 1 Classification: Basic Concepts and Methods Classification: Basic Concepts

Classification 1 Classification: Basic Concepts and Methods Classification: Basic Concepts

Classification 1 Classification: Basic Concepts and Methods Classification: Basic Concepts Decision Tree Bayes Classification Methods Model Evaluation and Selection Ensemble Methods 2 Motivating Example Fruit

853 views • 66 slides
Cognitive Modeling Unseen Examples 2 Bayes Classifiers Lecture 14: Naive Bayes Classifiers

Cognitive Modeling Unseen Examples 2 Bayes Classifiers Lecture 14: Naive Bayes Classifiers

Decision Making as Classification Decision Making as Classification Bayes Classifiers Bayes Classifiers Naive Bayes Classifiers Naive Bayes Classifiers 1 Decision Making as Classification Decision Making Frequencies and Probabilities

268 views • 5 slides
Part 10: Vector Space Classification Francesco Ricci 1 Content p Recap on nave Bayes p

Part 10: Vector Space Classification Francesco Ricci 1 Content p Recap on nave Bayes p

Part 10: Vector Space Classification Francesco Ricci 1 Content p Recap on nave Bayes p Vector space methods for Text Classification n K Nearest Neighbors p Bayes error rate n Decision boundaries n Vector space classification

1.09k views • 95 slides
Probabilistic classification CE-717: Machine Learning Sharif University of Technology M.

Probabilistic classification CE-717: Machine Learning Sharif University of Technology M.

Probabilistic classification CE-717: Machine Learning Sharif University of Technology M. Soleymani Fall 2016 Topics Probabilistic approach Bayes decision theory Generative models Gaussian Bayes classifier Nave Bayes

845 views • 59 slides
T ext Classification &amp; Nave Bayes CMSC 723 / LING 723 / INST 725 M ARINE C ARPUAT

T ext Classification &amp; Nave Bayes CMSC 723 / LING 723 / INST 725 M ARINE C ARPUAT

T ext Classification &amp; Nave Bayes CMSC 723 / LING 723 / INST 725 M ARINE C ARPUAT marine@cs.umd.edu Some slides by Dan Jurafsky &amp; James Martin, Jacob Eisenstein T oday Text classification problems and their evaluation

511 views • 28 slides
Introduction to Machine Learning Classification: Naive Bayes Learning goals 15 Understand the

Introduction to Machine Learning Classification: Naive Bayes Learning goals 15 Understand the

Introduction to Machine Learning Classification: Naive Bayes Learning goals 15 Understand the idea of Naive 10 Response Bayes x2 a b 5 Understand in which sense 0 Naive Bayes is a special QDA 0 5 10 15 x1 model NAIVE BAYES

317 views • 6 slides
Classification, Dose-response Modelling, and the Evaluation of Biomarker in a Microarray Setting

Classification, Dose-response Modelling, and the Evaluation of Biomarker in a Microarray Setting

Introduction to Microarrays Contents of the Dissertation Dose-response Modelling Concluding Remarks and Future Research Classification, Dose-response Modelling, and the Evaluation of Biomarker in a Microarray Setting Dan Lin Promoter: Prof.

1.09k views • 85 slides
Statistical classification Lecture notes Naive Bayes Bayes' theorem P ( c|a ) P ( a ) = P ( a|c

Statistical classification Lecture notes Naive Bayes Bayes' theorem P ( c|a ) P ( a ) = P ( a|c

Statistical classification Lecture notes Naive Bayes Bayes' theorem P ( c|a ) P ( a ) = P ( a|c ) P (c) P ( c|f ) % of class c given feature(s) a Posterior This will be our target P ( f|c ) % of feature f given class c

676 views • 36 slides
Overview Introduction to Information Retrieval Text classification

Overview Introduction to Information Retrieval Text classification

Overview Introduction to Information Retrieval Text classification http://informationretrieval.org 1 IIR 13: Text Classification &amp; Naive Bayes Naive Bayes 2 Hinrich Sch utze Evaluation of TC 3 Institute for Natural Language

172 views • 14 slides
Week 3: Na ve Bayes Instructor: Sergey Levine 1 Generative modeling In the classification

Week 3: Na ve Bayes Instructor: Sergey Levine 1 Generative modeling In the classification

Week 3: Na ve Bayes Instructor: Sergey Levine 1 Generative modeling In the classification setting, we have discrete labels y { 0 , . . . , L y 1 } (lets assume for now that L y = 2, so we are just doing binary classification), and

227 views • 3 slides
Web Information Retrieval Lecture 14 Text classification Sec. 13.1 Text Classification

Web Information Retrieval Lecture 14 Text classification Sec. 13.1 Text Classification

Web Information Retrieval Lecture 14 Text classification Sec. 13.1 Text Classification Nave Bayes Classification Vector space methods for Text Classification K Nearest Neighbors Decision boundaries Linear Classifiers

744 views • 47 slides
Overview Decision Theory Classification and Bayes decision rule Sampling vs diagnostic paradigm

Overview Decision Theory Classification and Bayes decision rule Sampling vs diagnostic paradigm

Overview Decision Theory Classification and Bayes decision rule Sampling vs diagnostic paradigm Chris Williams Classification with Gaussians Loss, Utility and Risk School of Informatics, University of Edinburgh Reject option October 2010

150 views • 4 slides
Classification using Hierarchical Naive Bayes Models HNB workshop HNB workshop p.1/18

Classification using Hierarchical Naive Bayes Models HNB workshop HNB workshop p.1/18

Classification using Hierarchical Naive Bayes Models HNB workshop HNB workshop p.1/18 Motivation Previous work on learning a HNBs focused on scientific modeling, i.e.: Find an interesting latent structure (based on the BIC score). We

444 views • 17 slides
Classification James H. Steiger Department of Psychology and Human Development Vanderbilt

Classification James H. Steiger Department of Psychology and Human Development Vanderbilt

Introduction The Linear Classification Function Quadratic Classification Functions Estimating Misclassification Rates Bias in Error Rate Estimation Error Rates in Variable Selection Classification via the k Nearest Neighbor Rule Classification

417 views • 31 slides
Implementation exercises for the course Heuristic Optimization Dr. Franco Mascia 1

Implementation exercises for the course Heuristic Optimization Dr. Franco Mascia 1

Implementation exercises for the course Heuristic Optimization Dr. Franco Mascia 1 fmascia@ulb.ac.be IRIDIA, CoDE, ULB February 29, 2012 1Slides based on last years excersises by Dr. Manuel L opez-Ib a nez. Dr. Franco Mascia

879 views • 77 slides
Human Learning in the Michalski Train Domain Ute Schmid Cognitive Systems Fakult at

Human Learning in the Michalski Train Domain Ute Schmid Cognitive Systems Fakult at

Human Learning in the Michalski Train Domain Ute Schmid Cognitive Systems Fakult at Wirtschaftsinformatik und Angewandte Informatik Otto-Friedrich Universit at Bamberg Dagstuhl, AAIP 2017 U. Schmid (Uni BA) HLCTrains AAIP 2017 1 /

341 views • 9 slides
MAESTRO: Model-based AnalysEs of Single-cell Transcriptome and RegulOme Ming (Tommy) Tang

MAESTRO: Model-based AnalysEs of Single-cell Transcriptome and RegulOme Ming (Tommy) Tang

MAESTRO: Model-based AnalysEs of Single-cell Transcriptome and RegulOme Ming (Tommy) Tang Twitter: @tangming2005 X Shirley Liu group Senior scientist at Dana-Farber Cancer Institute https://divingintogeneticsandgenomics.rbind.io/

538 views • 19 slides
On a stochastic mass conserved Allen-Cahn equation with nonlinear diffusion Perla El Kettani 1 ,

On a stochastic mass conserved Allen-Cahn equation with nonlinear diffusion Perla El Kettani 1 ,

On a stochastic mass conserved Allen-Cahn equation with nonlinear diffusion Perla El Kettani 1 , Danielle Hilhorst 2 , Kai Lee 3 1 University of Paris-Sud 2 CNRS and University of Paris-Sud 3 University of Tokyo, Japan March 25th, 2019 Danielle

941 views • 65 slides
ENSEMBLES FOR TIME SERIES FORECASTING Mariana Oliveira &amp; Lus Torgo Ensembles for Time

ENSEMBLES FOR TIME SERIES FORECASTING Mariana Oliveira &amp; Lus Torgo Ensembles for Time

ENSEMBLES FOR TIME SERIES FORECASTING Mariana Oliveira &amp; Lus Torgo Ensembles for Time Series Forecasting ACML 2014 2 Mariana Oliveira &amp; Lus Torgo (FCUP/LIAAD) Outline Introduction Delay-coordinate embedding Bagging for

374 views • 19 slides
SciForum Mol2Net Prediction of the Antagonistic Activity On the Receiving AT1 of the Angiotensin

SciForum Mol2Net Prediction of the Antagonistic Activity On the Receiving AT1 of the Angiotensin

Mol2Net , 2015 , 1( Section A, B, C, etc. ), pages 1- x, type of paper, doi: xxx-xxxx 1 http://sciforum.net/conference/mol2net-1 SciForum Mol2Net Prediction of the Antagonistic Activity On the Receiving AT1 of the Angiotensin II MSc. Luis

410 views • 3 slides
HOST VLSI Test Basics ECE 495/595 Big Picture Customers needs Determine requirements

HOST VLSI Test Basics ECE 495/595 Big Picture Customers needs Determine requirements

HOST VLSI Test Basics ECE 495/595 Big Picture Customers needs Determine requirements Write specifications Design synthesis and verification Test development Fabrication Test Chips to customers 1 (2/4/13) HOST VLSI Test Basics

360 views • 23 slides
W W HAT IS T EST D RIVEN D EVELOPMENT ? T D D ? TDD is a software development technique based

W W HAT IS T EST D RIVEN D EVELOPMENT ? T D D ? TDD is a software development technique based

A C OMPARATIVE C ASE S TUDY ON THE I MPACT OF T EST -D RIVEN D EVELOPMENT ON P ROGRAM D ESIGN AND T EST C OVERAGE M ARIA S INIAALTO AND P EKKA A BRAHAMSSON ESEM, 2007 Irena Pletikosa Cvijikj Softvare Engineering Seminar Softvare Engineering

514 views • 40 slides

More recommend