NDLI: Mining gene expression data focusing cancer therapeutics: a digest

Content Provider	ACM Digital Library
Author	Jauhari, Shaurya Rizvi, S. A. M.
Copyright Year	2014
Abstract	An understanding towards genetics and epigenetics is essential to cope up with the paradigm shift which is underway. Personalized medicine and gene therapy will confluence the days to come. This review highlights traditional approaches as well as current advancements in the analysis of the gene expression data from cancer perspective. Due to improvements in biometric instrumentation and automation, it has become easier to collect a lot of experimental data in molecular biology. Analysis of such data is extremely important as it leads to knowledge discovery that can be validated by experiments. Previously, the diagnosis of complex genetic diseases has conventionally been done based on the non-molecular characteristics like kind of tumor tissue, pathological characteristics, and clinical phase. The microarray data can be well accounted for high dimensional space and noise. Same were the reasons for ineffective and imprecise results. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data. While differences in efficiency do exist, none of the well-established approaches is uniformly superior to others. The quality of algorithm is important, but is not in itself a guarantee of the quality of a specific data analysis.
Starting Page	533
Ending Page	547
Page Count	15
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2014.2312002
Volume Number	11
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-05-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Association rules Cancer Classification Clinicopathology Clustering Data mining Epigenetics Gene expression data Gene therapy Next generation sequencing
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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Mining Gene Expression Data Focusing Cancer Therapeutics: A Digest

Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data

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Heuristic Bayesian Segmentation for Discovery of Coexpressed Genes within Genomic Regions

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Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifying Gene and Protein Expression Studies

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