title: Mining Gene Expression Data Focusing Cancer Therapeutics: A Digest.
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.
authors.forename: Shaurya
authors.initials: S
authors.lastname: Jauhari
authors.forename: S A M
authors.initials: SA
authors.lastname: Rizvi
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D057890:Epigenomics / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis