Extending meta-learning framework for clustering gene expression data with component-based algorithm design and internal evaluation measures

International Journal of Data Mining and Bioinformatics ◽

10.1504/ijdmb.2016.074682 ◽

2016 ◽

Vol 14 (2) ◽

pp. 101 ◽

Author(s):

Milan Vukicevic ◽

Sandro Radovanovic ◽

Boris Delibasic ◽

Milija Suknovic

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Algorithm Design ◽

Expression Data ◽

Internal Evaluation ◽

Evaluation Measures ◽

Learning Framework ◽

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Internal Evaluation Measures as Proxies for External Indices in Clustering Gene Expression Data

2011 IEEE International Conference on Bioinformatics and Biomedicine ◽

10.1109/bibm.2011.97 ◽

2011 ◽

Author(s):

Milan Vukicevic ◽

Boris Delibasic ◽

Milos Jovanovic ◽

Milija Suknovic ◽

Zoran Obradovic

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Expression Data ◽

Internal Evaluation ◽

Evaluation Measures

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Meta‐learning approach to gene expression data classification

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/17563780910959901 ◽

2009 ◽

Vol 2 (2) ◽

pp. 285-303 ◽

Author(s):

Bruno Feres de Souza ◽

Carlos Soares ◽

André C.P.L.F. de Carvalho

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Data Classification ◽

Learning Approach ◽

Expression Data ◽

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A Deep Learning Framework for Identifying Essential Proteins Based on Protein-Protein Interaction Network and Gene Expression Data

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2018.8621551 ◽

2018 ◽

Author(s):

Min Zeng ◽

Min Li ◽

Zhihui Fei ◽

Fang-Xiang Wu ◽

Yaohang Li ◽

...

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Gene Expression Data ◽

Protein Interaction Network ◽

Interaction Network ◽

Expression Data ◽

Essential Proteins ◽

Protein Protein Interaction ◽

Learning Framework ◽

Protein Protein Interaction Network

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A Self-supervised Learning Framework for Classifying Microarray Gene Expression Data

Computational Science – ICCS 2006 - Lecture Notes in Computer Science ◽

10.1007/11758525_93 ◽

2006 ◽

pp. 686-693

Author(s):

Yijuan Lu ◽

Qi Tian ◽

Feng Liu ◽

Maribel Sanchez ◽

Yufeng Wang

Keyword(s):

Gene Expression ◽

Supervised Learning ◽

Gene Expression Data ◽

Microarray Gene Expression Data ◽

Expression Data ◽

Microarray Gene Expression ◽

Learning Framework ◽

Microarray Gene

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cTAP: A Machine Learning Framework for Predicting Target Genes of a Transcription Factor using a Cohort of Gene Expression Data Sets

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm49941.2020.9313303 ◽

2020 ◽

Author(s):

Honglin Wang ◽

Pujan Joshi ◽

Seung-Hyun Hong ◽

Peter F. Maye ◽

David W. Rowe ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Transcription Factor ◽

Gene Expression Data ◽

Target Genes ◽

Data Sets ◽

Expression Data ◽

Learning Framework

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Cancer Classification from Gene Expression data using Fuzzy-Rough techniques An Empirical Study

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i6.415420 ◽

2018 ◽

Vol 6 (6) ◽

pp. 415-420

Author(s):

Ansuman Kumar ◽

Anindya Halder

Keyword(s):

Gene Expression ◽

Empirical Study ◽

Gene Expression Data ◽

Cancer Classification ◽

Expression Data

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Statistical methods for analysis of time course gene expression data

Frontiers in Bioscience ◽

10.2741/a743 ◽

2002 ◽

Vol 7 (1) ◽

pp. a90-98 ◽

Author(s):

Hongzhe Li

Keyword(s):

Gene Expression ◽

Statistical Methods ◽

Gene Expression Data ◽

Time Course ◽

Expression Data

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Faculty Opinions recommendation of A new type of stochastic dependence revealed in gene expression data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1032265.370760 ◽

2006 ◽

Author(s):

Arcady Mushegian

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Expression Data ◽

Stochastic Dependence ◽

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Faculty Opinions recommendation of A systematic comparison and evaluation of biclustering methods for gene expression data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1087930.540878 ◽

2007 ◽

Author(s):

Daniel Chamovitz

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Expression Data ◽

Systematic Comparison

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Faculty Opinions recommendation of CAERUS: predicting CAncER oUtcomeS using relationship between protein structural information, protein networks, gene expression data, and mutation data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.11029956.11977055 ◽

2011 ◽

Author(s):

Yuanpeng Janet Huang

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Structural Information ◽

Protein Networks ◽

Expression Data ◽

Cancer Outcomes ◽

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