Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.

Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect.

Lenvatinib Induces AKT/NF-κB Inactivation, Apoptosis Signal Transduction and Growth Inhibition of Non-small Cell Lung Cancer .

Background/aim: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer with poor prognosis. Lenvatinib is a multi-kinase inhibitor that has the potential to suppress tumor progression. Our previous study suggested that lenvatinib induces cytotoxicity and apoptosis in CL-1-5-F4 cells in vitro.

Identification of key genes and pathways associated with topotecan treatment using multiple bioinformatics tools.

Background: The goal of this study is to determine critical genes and pathways associated with topotecan using publicly accessible bioinformatics tools.

Methods: Topotecan signatures were downloaded from the Library of Integrated Network-Based Cellular Signatures (LINCS) database (http://www.ilincs.

A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks.

Motivation: A major challenge in systems biology is to reveal the cellular pathways that give rise to specific phenotypes and behaviours. Current techniques often rely on a network representation of molecular interactions, where each node represents a protein or a gene and each interaction is assigned a single static score. However, the use of single interaction scores fails to capture the tendency of proteins to favour different partners under distinct cellular conditions.

The TissueNet database of human tissue protein-protein interactions.

Knowledge of protein-protein interactions (PPIs) is important for identifying the functions of proteins and the processes they are involved in. Although data of human PPIs are easily accessible through several public databases, these databases do not specify the human tissues in which these PPIs take place. The TissueNet database of human tissue PPIs (http://netbio.