Translational Bioinformatics and Healthcare Informatics: Computational and Ethical Challenges
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2804463/
- - Feature selection or dimensionality reduction: These techniques select a set of relevant genes (which behave as discriminatory features) that are most strongly related to a particular class of sample.9–11
- - Supervised classification: Challenges in microarray mining include predicting the class label of the unknown sample on the basis of its gene expression profile.12–14 An example in proteomics is the functional classification of proteins for the prediction of cellular function.15
- - Unsupervised clustering: This technique reveals groups of genes or conditions based on gene expression patterns or rapid structural/topological clustering of proteins.16–19
- - Prediction analysis: This technique predicts the three-dimensional structure of a protein from its amino acid sequence.20
- - Associative pattern mining: This refers to finding associations between genes that are similar in behavior, such as genes that are up-regulated or down-regulated throughout the identified conditions.21–23
- - Text mining and natural language processing (NLP): These approaches extract important biological relationships in retrieving associated documents. These techniques provide insight into the disease being studied and further enhance the research.24
The object of bioinformatics is to design and deliver novel methodologies and tools that track and predict the future course of a disease based on its genetic mutations and protein interactions.
For example, the Electronic Medical Records and Genomics (eMERGE) project funded by the National Institutes of Health (NIH) is a five-institution network in the United States that develops a consortium of biorepositories and links them to EHRs for high-throughput genome-wide association analysis.26
Translational bioinformatics
http://bib.oxfordjournals.org/content/11/1/96.long
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