Sentence examples for bioinformatics data such as from inspiring English sources

To demonstrate the genetic relationship between schizophrenia and cancer, network biology and systemic bioinformatics data such as protein-protein interactions (PPIs) and related pathways were introduced.

Thus, several computational approaches have been developed to predict protein-protein interactions utilizing existing bioinformatics data such as gene proximity information [ 18, 19], gene fusion events [ 20, 21], gene co-expression data [ 22- 24], phylogenetic profiling [ 25], orthologous protein interactions [ 26] and identification of interacting protein domains [ 27- 30].

Many bioinformatics tools use reference data, such as genome assemblies or sequence databanks.

Currently existing methods for constraint-based model reconstruction primarily depend on the bioinformatic information such as genomics data, biochemical data and models of related microorganisms at the initial phase of model building.

In areas where there is a need to analyze large amounts of data, such as bioinformatics, machine learning is a key technique, particularly when analyzing "big data" [ 16].

A more ambitious challenge lies in the need to be able to efficiently deal with the steady stream of updates to model data (such as genomic references), bioinformatics tools and analysis procedures.

As databases become increasingly accepted in areas such as Geographic Information Systems GISS) and Bioinformatics, commercial Database Management System DBMSS) needs to support data types for complex data such as spatial geometries and protein structures.

Medical bioinformatics, in fact, is often concerned with sensitive and expensive data such as projects contributing to computer-aided drug design or in environments like hospitals.

Bioinformatics is the science of storing, analyzing, and utilizing information from biological data such as sequences, molecules, gene expressions, and pathways.

With advances in bioinformatics, systems biology and molecular biology, different types of high-throughput "omic" data such as genomics, transcriptomics and metabolomics data have emerged as critical information in the biomedical and pharmaceutical fields [ 63– 63].

Currently, with the public availability of genomic data such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), a plenty of bioinformatics researchers analyzed gene expression data with clinical data to attempt to predict the prognosis and find biomarkers for therapy [ 2– 5].