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

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Knowledge discovery using big data in biomedical systems

Divide and conquer approach to contact map overlap problem using 2D-pattern mining of protein contact networks

Biclustering with flexible plaid models to unravel interactions between biological processes

Unsupervised structure detection in biomedical data

Guest editorial for the 25th International Conference on Genome Informatics (GIW/ISCB-Asia 2014)

Randomized subspace learning for proline CIS-trans isomerization prediction

An O(m log m)-time algorithm for detecting superbubbles

An efficient search algorithm for finding genomic-range overlaps based on the maximum range length

Curatable named-entity recognition using semantic relations

Guest editors introduction to the special section on software and databases

ResSeq: enhancing short-read sequencing alignment by rescuing error-containing reads

MMBIRFinder: a tool to detect microhomology-mediated break-induced replication

Heterogeneous compression of large collections of evolutionary trees

ClusterViz: a cytoscape APP for cluster analysis of biological network

Building transcriptional association networks in cytoscape with RegNetC

iPFPi: a system for improving protein function prediction through cumulative iterations

Software suite for gene and protein annotation prediction and similarity search

Rapid assessment of adverse drug reactions by statistical solution of gene association network

Quantitative measurement of split of the second heart sound (S2)

An optimized LMMSE based method for 3D MRI denoising

RBioCloud: a light-weight framework for bioconductor and R-based jobs on the cloud

A new method for detecting protein complexes based on the three node cliques

Adaptive fuzzy consensus clustering framework for clustering analysis of cancer data

An integrated framework for functional annotation of protein structural domains

Bayesian normalization model for label-free quantitative analysis by LC-MS

Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach

Optimal experimental design for gene regulatory networks in the presence of uncertainty

Statistical detection of intrinsically multivariate predictive genes

RPCA-based tumor classification using gene expression data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 11

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 10

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

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

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 4

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

Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach

Content Provider	ACM Digital Library
Author	Zeng, Jianyang Liang, Muxuan Chen, Ting Li, Zhizhong
Copyright Year	2015
Description	Author Affiliation: Tsinghua University, Beijing, P. R. China and University of Wisconsin-Madison, Madison, WI(Tsinghua university, Beijing, P. R. China and University of Southern California, LA, CA (Chen, Ting; Genomics institute of the novartis research foundation, San Diego, CA (Li, Zhizhong); Tsinghua university, Beijing, P. R. China (Zeng, Jianyang); Liang, Muxuan))
Abstract	Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.
Starting Page	928
Ending Page	937
Page Count	10
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2014.2377729
Volume Number	12
Issue Number	4
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Clinical data Genomic data Identification of cancer subtypes Multi-platform cancer data analysis Multimodal deep belief network Restricted boltzmann machine
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach

Latent feature decompositions for integrative analysis of multi-platform genomic data

A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text

Latent feature decompositions for integrative analysis of multi-platform genomic data

Development and Application of Matrix Variate Restricted Boltzmann Machine

Latent Feature Decompositions for Integrative Analysis of Multi-Platform Genomic Data

Deep Belief Network Modeling for Automatic Liver Segmentation

Inductive Principles for Learning Restricted Boltzmann Machines (2010)

Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning

Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach

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