Mining high-throughput experimental data to link gene and function

Systems Biology Approaches to Mining High Throughput Biological Data

With advances in high throughput measurement techniques, large-scale biological data have been and will continuously be produced, for example, gene expression data, protein-protein interaction (PPI) data, tandem mass spectra data, microRNA expression data, lncRNA expression data, and biomolecule-disease association data. Such data contain insightful information for understanding the mechanism o...

High-throughput and data mining with ab initio methods

Accurate ab initio methods for performing quantum mechanical calculations have been available for many years, but their speed, complexity and instability have generally constrained researchers to studying only a few systems at a time. However, advances in computer speed and ab initio algorithms have now created fast and robust codes, where large numbers of calculations can be performed automati...

Text Mining and Gene Expression Analysis - Towards Combined Interpretation of High Throughput Data

Clustering of high throughput gene expression data

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many area...

Methods for Mining Data from Genome Wide High-Throughput Technologies

Current high-throughput technologies, like DNA-microarrays, produce measurement data concerning the structure and function of cellular molecules in a genome wide manner. Analysis of such data requires using efficient and robust computational tools. The standard output from microarray analysis is a set of genes which are coor differentially expressed. Biological interpretation of such outcome ai...