Mutation status coupled with RNA-sequencing data can efficiently identify important non-significantly mutated genes serving as diagnostic biomarkers of endometrial cancer.

Background: Endometrial cancers (ECs) are one of the most common types of malignant tumor in females. Substantial efforts had been made to identify significantly mutated genes (SMGs) in ECs and use them as biomarkers for the classification of histological subtypes and the prediction of clinical outcomes. However, the impact of non-significantly mutated genes (non-SMGs), which may also play important roles in the prognosis of EC patients, has not been extensively studied.

Expression dynamics and relations with nearby genes of rat transposable elements across 11 organs, 4 developmental stages and both sexes.

Background: TEs pervade mammalian genomes. However, compared with mice, fewer studies have focused on the TE expression patterns in rat, particularly the comparisons across different organs, developmental stages and sexes. In addition, TEs can influence the expression of nearby genes.

Computational identifying and characterizing circular RNAs and their associated genes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is currently still a major factor leading to death, lacking of reliable biomarkers. Therefore, deep understanding the pathogenesis for HCC is of great importance. The emergence of circular RNA (circRNA) provides a new way to study the pathogenesis of human disease.

Functional annotation of sixty-five type-2 diabetes risk SNPs and its application in risk prediction.

Genome-wide association studies (GWAS) have identified more than sixty single nucleotide polymorphisms (SNPs) associated with increased risk for type 2 diabetes (T2D). However, the identification of causal risk SNPs for T2D pathogenesis was complicated by the factor that each risk SNP is a surrogate for the hundreds of SNPs, most of which reside in non-coding regions. Here we provide a comprehensive annotation of 65 known T2D related SNPs and inspect putative functional SNPs probably causing protein dysfunction, response element disruptions of known transcription factors related to T2D genes and regulatory response element disruption of four histone marks in pancreas and pancreas islet.

An eigenvalue transformation technique for predicting drug-target interaction.

The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention.

Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network.

The human papillomavirus 16 (HPV16) has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI) network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis.

A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach.

The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast prediction of the binding affinity with promising results, but most of them were developed as all-purpose models despite of the specific functions of different protein families, since proteins from different function families always have different structures and physicochemical features. In this study, we proposed a random forest method to predict the protein-ligand binding affinity based on a comprehensive feature set covering protein sequence, binding pocket, ligand structure and intermolecular interaction.

A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

Background: Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features.

Combination use of protein-protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphisms.

Single-nucleotide polymorphisms (SNPs) are the most frequent form of genetic variations. Non-synonymous SNPs (nsSNPs) occurring in coding region result in single amino acid substitutions that associate with human hereditary diseases. Plenty of approaches were designed for distinguishing deleterious from neutral nsSNPs based on sequence level information.

Improving the prediction of chemotherapeutic sensitivity of tumors in breast cancer via optimizing the selection of candidate genes.

Estrogen receptor status and the pathologic response to preoperative chemotherapy are two important indicators of chemotherapeutic sensitivity of tumors in breast cancer, which are used to guide the selection of specific regimens for patients. Microarray-based gene expression profiling, which is successfully applied to the discovery of tumor biomarkers and the prediction of drug response, was suggested to predict the cancer outcomes using the gene signatures differentially expressed between two clinical states. However, many false positive genes unrelated to the phenotypic differences will be involved in the lists of differentially expressed genes (DEGs) when only using the statistical methods for gene selection, e.