Developing an Analytical Pipeline to Classify Patient Safety Event Reports Using Optimized Predictive Algorithms.

Background: Patient safety event reports provide valuable insight into systemic safety issues but deriving insights from these reports requires computational tools to efficiently parse through large volumes of qualitative data. Natural language processing (NLP) combined with predictive learning provides an automated approach to evaluating these data and supporting the work of patient safety analysts.

Objectives: The objective of this study was to use NLP and machine learning techniques to develop a generalizable, scalable, and reliable approach to classifying event reports for the purpose of driving improvements in the safety and quality of patient care.

Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template.

Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Gene product levels could potentially be decoupled from these changes via built-in adaptation mechanisms, thereby boosting network reliability.

Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature.

Motivation: A major goal of biomedical research in personalized medicine is to find relationships between mutations and their corresponding disease phenotypes. However, most of the disease-related mutational data are currently buried in the biomedical literature in textual form and lack the necessary structure to allow easy retrieval and visualization. We introduce a high-throughput computational method for the identification of relevant disease mutations in PubMed abstracts applied to prostate (PCa) and breast cancer (BCa) mutations.

DMDM: domain mapping of disease mutations.

Unlabelled: Domain mapping of disease mutations (DMDM) is a database in which each disease mutation can be displayed by its gene, protein or domain location. DMDM provides a unique domain-level view where all human coding mutations are mapped on the protein domain. To build DMDM, all human proteins were aligned to a database of conserved protein domains using a Hidden Markov Model-based sequence alignment tool (HMMer).