Proangiogenic potential of plasma exosomes from prostate cancer patients.

Angiogenesis plays a pivotal role in the progression and metastasis of solid cancers, including prostate cancer (PCa). While small extracellular vesicles derived from PCa cell lines induce a proangiogenic phenotype in vascular endothelial cells, the contribution of plasma exosomes from patients with PCa to this process remains unclear. Here, we successfully extracted and characterized plasma exosomes.

Identification of biomarker associated with Trop2 in breast cancer: implication for targeted therapy.

Purpose: Trop2, a cell membrane glycoprotein, is overexpressed in almost all epithelial cancers. This study aimed to explore the mutational characteristics and significance of Trop2 in breast cancer (BC).

Methods: Patients diagnosed with BC (n = 77) were enrolled to investigate expression level and clinical characteristics of Trop2.

DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation.

Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs.

MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data.

Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. However, scRNA-seq data are often high dimensional and sparse, and manual cell type identification can be time-consuming, subjective, and lack reproducibility.

QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction.

Molecular toxicity prediction plays an important role in drug discovery, which is directly related to human health and drug fate. Accurately determining the toxicity of molecules can help weed out low-quality molecules in the early stage of drug discovery process and avoid depletion later in the drug development process. Nowadays, more and more researchers are starting to use machine learning methods to predict the toxicity of molecules, but these models do not fully exploit the 3D information of molecules.

DNMG: Deep molecular generative model by fusion of 3D information for de novo drug design.

Deep learning is improving and changing the process of de novo molecular design at a rapid pace. In recent years, great progress has been made in drug discovery and development by using deep generative models for de novo molecular design. However, most of the existing methods are string-based or graph-based and are limited by the lack of some very important properties, such as the three-dimensional information of molecules.