Sentence examples for molecular interaction data from inspiring English sources

Protein and molecular interaction data (optional).

With the increasing development of biotechnology and informatics technology, the past decade has seen an exceptional growth in publicly available data in chemistry and biology, especially in human-specific molecular interaction data.

Consequently, public databases for human-specific molecular interaction data have been undergoing a rapid growth within the past decade, such as BIND [18], DIP [19], STITCH [20], HPRD [21], TTD [22], DrugBank [23], ChEMBL [24], KEGG [25], BindingDB [26], SuperTarget and Matador [27], to name a few.

The MiMi plugin for Cytoscape (http://mimiplugin.ncibi.org/) was developed to facilitate access to the molecular interaction data assembled in MiMI that contains integrated data from multiple well-known protein interaction databases using an intelligent deep-merging approach [30].

Compared with our previously reported strategy, which was applied to TRN inference in the yeast cell cycle [25], this new scheme includes an integrative use of PPI and PDI data (hereafter called molecular interaction data) from thirteen publically available databases coupled with the detection of significant network motifs for each transcription factor.

One such resource, VirHostNet, 101, 102 is a database of curated virus host molecular interaction data.

Also, the regulatory interactions documented in the database have been ascertained in a non-uniform way, this simply reflects the non-exhaustive nature of present molecular interaction data-sets.

However, to investigate these complex molecular interactions, data resources providing a comprehensive collection of all genes involved in the development of the diseases are urgently needed.

Recently, while collecting several sets of molecular interactions data from IntAct, BioGRID, BIND, and TAIR databases, PAIR [ 12] has supplied 5,990 experimentally reported molecular interactions in Arabidopsis thaliana together with 145,494 predicted interactions, making it the most comprehensive and highly reliable database of the Arabidopsis interactome.

The predictive strength of this strategy is based on the combined constraints arising from multiple biological data sources, including time course gene expression data, combined molecular interaction network data, and GO category information.

The goal of IDA is to provide not only molecular and interaction data of ARS/AIMPs but also analytical tools that generate network models and then network-driven hypotheses for understanding of cancer-associated functions of ARS/AIMPs.