Recent studies showed that the likelihood of drug approval can be predicted with clinical data and structure information of drug using computational approaches. Predicting the likelihood of drug approval can be innovative and of high impact. However, models that leverage clinical data are applicable only in clinical stages, which is not very practical.
View Article and Find Full Text PDFComput Struct Biotechnol J
August 2023
Motivation: Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction.
View Article and Find Full Text PDFTo respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance.
View Article and Find Full Text PDFEmerging evidence indicates that the accretion of senescent cells is linked to metabolic disorders. However, the underlying mechanisms and metabolic consequences of cellular senescence in obesity remain obscure. In this study, we found that obese adipocytes are senescence-susceptible cells accompanied with genome instability.
View Article and Find Full Text PDFReactive oxygen species (ROS) are associated with various roles of brown adipocytes. Glucose-6-phosphate dehydrogenase (G6PD) controls cellular redox potentials by producing NADPH. Although G6PD upregulates cellular ROS levels in white adipocytes, the roles of G6PD in brown adipocytes remain elusive.
View Article and Find Full Text PDFGene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data.
View Article and Find Full Text PDF