Gene functional classification from heterogeneous data

Abstract

In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. We also show how to use knowledge about heterogeneity to aid in feature selection.

Keywords

Computer scienceSupport vector machineKernel (algebra)InferenceFeature selectionArtificial intelligenceSet (abstract data type)Function (biology)Machine learningKernel methodGenomeData miningComputational biologyGeneBiologyMathematicsGenetics

Affiliated Institutions

Related Publications

Kernel Logistic Regression and the Import Vector Machine

Ji Zhu , Trevor Hastie

The support vector machine (SVM) is known for its good performance in two-class classification, but its extension to multiclass classification is still an ongoing research issue...

2001 136 citations

Object Detection with Discriminatively Trained Part-Based Models

Pedro F. Felzenszwalb , Ross Girshick , David McAllester +1 more

We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-...

2009 IEEE Transactions on Pattern Analysis... 9911 citations

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

Hanchuan Peng , Fuhui Long , Chen Ding

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion base...

2005 IEEE Transactions on Pattern Analysis... 10050 citations

Large-scale analysis of the human and mouse transcriptomes

Andrew I. Su , M. Cooke , Keith A. Ching +11 more

High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority ...

2002 Proceedings of the National Academy o... 1481 citations

Biomarker Identification by Feature Wrappers

Momiao Xiong , Xiangzhong Fang , Jinying Zhao

Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenoty...

2001 Genome Research 266 citations

Publication Info

Year: 2001
Type: article
Pages: 249-255
Citations: 273
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Gene functional classification from heterogeneous data

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

273

OpenAlex

Cite This

APA Style

                            
                                    Paul Pavlidis, 
                                
                                    Jason Weston, 
                                
                                    Jinsong Cai
                                
                                et al.
                            
                            (2001). 
                            Gene functional classification from heterogeneous data. 
                            
                            , 249-255.
                            https://doi.org/10.1145/369133.369228

Identifiers

DOI: 10.1145/369133.369228