Article

Gene functional classification from heterogeneous data

Authors:

William Noble GrundyAuthors Info & Claims

RECOMB '01: Proceedings of the fifth annual international conference on Computational biology

Pages 249 - 255

https://doi.org/10.1145/369133.369228

Published: 22 April 2001 Publication History

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.

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Ding XCui MLi YChen S(2024)A maximal accuracy and minimal difference criterion for multiple kernel learningExpert Systems with Applications10.1016/j.eswa.2024.124378254(124378)Online publication date: Nov-2024
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Shimmura RSuzuki J(2024)Newton-Type Methods with the Proximal Gradient Step for Sparse EstimationOperations Research Forum10.1007/s43069-024-00307-x5:2Online publication date: 20-Mar-2024
https://doi.org/10.1007/s43069-024-00307-x
Handhayani T(2023)An integrated analysis of air pollution and meteorological conditions in JakartaScientific Reports10.1038/s41598-023-32817-913:1Online publication date: 9-Apr-2023
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Index Terms

Gene functional classification from heterogeneous data
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Conferences

RECOMB '01: Proceedings of the fifth annual international conference on Computational biology

April 2001

316 pages

ISBN:1581133537

DOI:10.1145/369133

Chairman:
Thomas Lengauer
German National Research Center for Information Technology, Germany

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGACT: ACM Special Interest Group on Algorithms and Computation Theory

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Association for Computing Machinery

New York, NY, United States

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Published: 22 April 2001

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RECOMB01: The Fifth Annual International Conference on Computational Molecular Biology

April 22 - 25, 2001

Quebec, Montreal, Canada

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RECOMB '01 Paper Acceptance Rate 35 of 128 submissions, 27%;

Overall Acceptance Rate 148 of 538 submissions, 28%

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Ding XCui MLi YChen S(2024)A maximal accuracy and minimal difference criterion for multiple kernel learningExpert Systems with Applications10.1016/j.eswa.2024.124378254(124378)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.124378
Shimmura RSuzuki J(2024)Newton-Type Methods with the Proximal Gradient Step for Sparse EstimationOperations Research Forum10.1007/s43069-024-00307-x5:2Online publication date: 20-Mar-2024
https://doi.org/10.1007/s43069-024-00307-x
Handhayani T(2023)An integrated analysis of air pollution and meteorological conditions in JakartaScientific Reports10.1038/s41598-023-32817-913:1Online publication date: 9-Apr-2023
https://doi.org/10.1038/s41598-023-32817-9
Veisi H(2023)Introduction to SVMLearning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines10.1007/978-981-19-6553-1_1(3-18)Online publication date: 19-Mar-2023
https://doi.org/10.1007/978-981-19-6553-1_1
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Huang XDhall AGoecke RPietikainen MZhao G(2022)Analyzing Group-Level Emotion with Global Alignment Kernel based ApproachIEEE Transactions on Affective Computing10.1109/TAFFC.2019.295366413:2(713-728)Online publication date: 1-Apr-2022
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Liu YWentzlaff DKung S(2022)Class-Discriminative CNN Compression2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956066(2070-2077)Online publication date: 21-Aug-2022
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Li PSofuoglu SAviyente SMaiti T(2022)Coupled support tensor machine classification for multimodal neuroimaging dataStatistical Analysis and Data Mining: The ASA Data Science Journal10.1002/sam.1158715:6(797-818)Online publication date: 23-May-2022
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Ida YFujiwara YKashima H(2021)Fast Block Coordinate Descent for Sparse Group LassoSparse Group Lassoのための高速なBlock Coordinate DescentTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.36-1_A-JB136:1(A-JB1_1-11)Online publication date: 1-Jan-2021
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