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

Bag of Naïve 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 Nucleotyde Polymorphisms (SNPs) • O(10 3 ) case / control individuals Objectives: 1.Biomarker Selection 2.Classification
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.
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
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
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
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
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

Recommend

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 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 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

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

269 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 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. Soleymani Fall 2016 Topics Probabilistic approach Bayes decision theory Generative models Gaussian Bayes classifier Nave Bayes

845 views • 59 slides

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

T ext Classification & Nave Bayes CMSC 723 / LING 723 / INST 725 M ARINE C ARPUAT marine@cs.umd.edu Some slides by Dan Jurafsky & 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 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

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 ) 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 http://informationretrieval.org 1 IIR 13: Text Classification & 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 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 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 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 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

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

An empirical Bayes procedure for the selection of Gaussian graphical models Estimation bay

An empirical Bayes procedure for the selection of Gaussian graphical models Estimation bay esienne pour les mod` eles graphiques gaussiens d ecomposables Jean-Michel Marin I3M, Universit e Montpellier 2 joint with Sophie Donnet ,

567 views • 42 slides

1 Problem with Brute force Nave Bayes ( ) ( ) ( ) It cannot generalize to unseen

Outline Bayes theorem Discrete Bayesian classifiers Maximum likelihood classification Brute force Bayesian learning Nave Bayes Lecture 5 Bayesian Belief Networks Bayes theorem Maximum likelihood ( ) ( ) ( ) P x

72 views • 4 slides

Nave Bayes Classification Nickolai Riabov, Kenneth Tiong Brown University Fall 2013 Nickolai

Theory Nave Bayes in SQL Nave Bayes Classification Nickolai Riabov, Kenneth Tiong Brown University Fall 2013 Nickolai Riabov, Kenneth Tiong Nave Bayes Classification Theory Nave Bayes in SQL Structure of the Talk Theory of Nave

440 views • 21 slides

Machine Learning Lecture 3 Justin Pearson 1 2020 1

Machine Learning Lecture 3 Justin Pearson 1 2020 1 http://user.it.uu.se/~justin/Teaching/MachineLearning/index.html 1 / 39 Todays plan Classification Revision basic probability Bayes Theorem Naive Bayes Classification 2 / 39

937 views • 39 slides

AUTOMATIC CLASSIFICATION: NAVE BAYES WM&R 2019/20 2 U NITS R. Basili ( many slides

1 AUTOMATIC CLASSIFICATION: NAVE BAYES WM&R 2019/20 2 U NITS R. Basili ( many slides borrowed by: H. Schutze) Universit di Roma Tor Vergata Email: basili@info.uniroma2.it 2 Summary The nature of probabilistic modeling

446 views • 29 slides

Nave Bayes, Maxent and Neural Models CMSC 473/673 UMBC Some slides adapted from 3SLP Outline

Nave Bayes, Maxent and Neural Models CMSC 473/673 UMBC Some slides adapted from 3SLP Outline Recap: classification (MAP vs. noisy channel) & evaluation Nave Bayes (NB) classification Terminology: bag-of-words Nave assumption

1.35k views • 132 slides

Introduction to Machine Learning CMU-10701 2. MLE, MAP, Bayes classification Barnabs Pczos

Introduction to Machine Learning CMU-10701 2. MLE, MAP, Bayes classification Barnabs Pczos & Aarti Singh 2014 Spring Administration http://www.cs.cmu.edu/~aarti/Class/10701_Spring14/index.html Blackboard manager & Peer grading:

1.08k views • 41 slides

INF4080 2020 FALL NATURAL LANGUAGE PROCESSING Jan Tore Lnning 2 (Mostly Text)

1 INF4080 2020 FALL NATURAL LANGUAGE PROCESSING Jan Tore Lnning 2 (Mostly Text) Classification, Naive Bayes Lecture 3, 31 Aug Today - Classification 3 Motivation Classification Naive Bayes classification NB for text

909 views • 62 slides