DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network.

Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity.A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways.

Dual Representation Learning for Predicting Drug-side Effect Frequency using Protein Target Information.

Knowledge of unintended effects of drugs is critical in assessing the risk of treatment and in drug repurposing. Although numerous existing studies predict drug-side effect presence, only four of them predict the frequency of the side effects. Unfortunately, current prediction methods (1) do not utilize drug targets, (2) do not predict well for unseen drugs, and (3) do not use multiple heterogeneous drug features.

Improved drug response prediction by drug target data integration via network-based profiling.

Drug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict drug response as IC50 or AUC values. Intuitively, use of drug target interaction (DTI) information can be useful for DRP.

Hepatic GSK3β-Dependent CRY1 Degradation Contributes to Diabetic Hyperglycemia.

Excessive hepatic glucose production (HGP) is a key factor promoting hyperglycemia in diabetes. Hepatic cryptochrome 1 (CRY1) plays an important role in maintaining glucose homeostasis by suppressing forkhead box O1 (FOXO1)-mediated HGP. Although downregulation of hepatic CRY1 appears to be associated with increased HGP, the mechanism(s) by which hepatic CRY1 dysregulation confers hyperglycemia in subjects with diabetes is largely unknown.

AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning.

Background: The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statistics, i.e.

MLDEG: A Machine Learning Approach to Identify Differentially Expressed Genes Using Network Property and Network Propagation.

Motivation: Identifying differentially expressed genes (DEGs) in transcriptome data is a very important task. However, performances of existing DEG methods vary significantly for data sets measured in different conditions and no single statistical or machine learning model for DEG detection perform consistently well for data sets of different traits. In addition, setting a cutoff value for the significance of differential expressions is one of confounding factors to determine DEGs.

Impact of mutations in DNA methylation modification genes on genome-wide methylation landscapes and downstream gene activations in pan-cancer.

Background: In cancer, mutations of DNA methylation modification genes have crucial roles for epigenetic modifications genome-wide, which lead to the activation or suppression of important genes including tumor suppressor genes. Mutations on the epigenetic modifiers could affect the enzyme activity, which would result in the difference in genome-wide methylation profiles and, activation of downstream genes. Therefore, we investigated the effect of mutations on DNA methylation modification genes such as DNMT1, DNMT3A, MBD1, MBD4, TET1, TET2 and TET3 through a pan-cancer analysis.

PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation.

Transcription factor (TF) has a significant influence on the state of a cell by regulating multiple down-stream genes. Thus, experimental and computational biologists have made great efforts to construct TF gene networks for regulatory interactions between TFs and their target genes. Now, an important research question is how to utilize TF networks to investigate the response of a plant to stress at the transcription control level using time-series transcriptome data.