A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports.

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for biomedical text-mining applications. These tools enable investigators to integrate, conceptualize, and translate these discoveries to uncover new insights into disease pathology and therapeutics. In this protocol, we present CaseOLAP LIFT, a new computational pipeline to investigate cellular components and their disease associations by extracting user-selected information from text datasets (e.

Design and application of a knowledge network for automatic prioritization of drug mechanisms.

Motivation: Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications.

Draft Genome Sequences of Bacillus glennii V44-8, Bacillus saganii V47-23a, sp. Strain V59.32b, sp. Strain MER_TA_151, and sp. Strain MER_111, Isolated from Cleanrooms Where the Viking and Mars Exploration Rover Spacecraft Were Assembled.

We report the draft genome sequences of V44-8, V47-23a, and sp. strain V59.32b, isolated from the Viking spacecraft assembly cleanroom, and sp.