Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics.

Background: This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes.

Methods: Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately.

Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction.

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs.

Sensitivity analysis based on the random forest machine learning algorithm identifies candidate genes for regulation of innate and adaptive immune response of chicken.

Two categories of immune responses-innate and adaptive immunity-have both polygenic backgrounds and a significant environmental component. The goal of the reported study was to define candidate genes and mutations for the immune traits of interest in chickens using machine learning-based sensitivity analysis for single-nucleotide polymorphisms (SNPs) located in candidate genes defined in quantitative trait loci regions. Here the adaptive immunity is represented by the specific antibody response toward keyhole limpet hemocyanin (KLH), whereas the innate immunity was represented by natural antibodies toward lipopolysaccharide (LPS) and lipoteichoic acid (LTA).

Identification with all humanity-A test of the factorial structure and measurement invariance of the scale in five countries.

Identification with all humanity measured as an individual characteristic is an important factor related to social and international relations, such as concern for global issues and human rights, prosocial attitudes, intergroup forgiveness, attitudes toward immigrants, solving global problems, reactions to hate crimes and dehumanisation. We examine the factorial structure, psychometric properties and measurement invariance of the Identification with All Humanity (IWAH) scale in student samples from five countries (the United States, Poland, France, Mexico and Chile; N = 1930). Separate confirmatory factor analyses (CFAs) for each country showed a second-order model of one superordinate factor with two subfactors.

Comparative study of DNA damage and repair in head and neck cancer after radiation treatment.

We compared DNA damage and the efficacy of its repair after genotoxic treatment with gamma-radiation of lymphocytes and tissue cells isolated from patients with squamous cell carcinoma of head and neck (HNSCC) and healthy donors. Thirty-seven subjects with HNSCC and 35 healthy donors were enrolled in the study. The extent of DNA damage including oxidative lesions and efficiency of the repair were examined by alkaline comet assay.