The emergence of digital twins (DTs) in the arena of anticancer treatment echoes the transformative impact of artificial intelligence in drug development. DTs provide dynamic, accessible platforms that may accurately replicate patient and tumor chara...
Accurately predicting the pharmacological and toxicological properties of molecules is a critical step in the drug development process. Owing to the heterogeneity of molecular property prediction tasks, most of the current methods rely on building a ...
Identification of T cell receptor (TCR) specificities for antigens from large-scale single-cell or bulk TCR repertoire data plays a vital role in disease diagnosis and immunotherapy. In silico prediction models have emerged in recent years. However, ...
DNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through hand...
Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell profiling data. Its analysis presents a significant challenge in accurately annotating cell types and their associated biomarkers. Different pipelines ...
Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in mole...
Methylated RNA m6A immunoprecipitation sequencing (m6A-Seq) is a powerful technique for investigating transcriptome-wide m6A modification. However, most of the existing m6A-Seq protocols rely on reference genomes, limiting their use in species lackin...
Reconstructing high-resolution gene regulatory networks (GRNs) based on single-cell RNA sequencing data provides an opportunity to gain insight into disease pathogenesis. At present, there are a large number of GRN reconstruction methods based on gra...
Catalytic constant (Kcat) is to describe the efficiency of catalyzing reactions. The Kcat value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challengi...
Accurate prediction of patient survival rates in cancer treatment is essential for effective therapeutic planning. Unfortunately, current models often underutilize the extensive multimodal data available, affecting confidence in predictions. This stu...