Publications by authors named "Zhu Li-da"
Article Synopsis
- - Disease pathogenesis is a key focus in biomedical research, and analyzing drug effects on specific diseases shows potential for uncovering disease-related mechanisms, though it's been limited to few drugs.
- - The study utilized text mining to gather extensive data on diseases, drugs, and their associations from over 29 million publications and developed a new analysis pipeline called 'DSEATM', which proved more effective than current methods.
- - DSEATM's findings align well with established cancer pathways, demonstrating its reliability; the number of drugs analyzed is significantly linked to the method's performance, indicating DSEATM could be a valuable tool for disease research.
Article Synopsis
- Drug combination research faces challenges due to the high costs of experimental methodologies, leading to a reliance on computational methods that often focus solely on molecular structure.
- The study introduces a more robust approach by integrating diverse drug characteristics and employing a neighbor recommender method with ensemble learning algorithms, achieving a high accuracy of 0.964 AUC.
- The new ensemble models outperformed traditional machine learning techniques and successfully identified 7 potential drug combinations for paclitaxel, with 2 of them showing promising effects in verification tests.
Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics.
View Article and Find Full Text PDFDue to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space.
View Article and Find Full Text PDFArticle Synopsis
- The study analyzed bacterial diversity in fecal samples from wild pygmy lorises using 16S rDNA techniques.
- The findings revealed four main bacterial groups: Firmicutes (43.1%), Proteobacteria (34.5%), Actinobacteria (5.2%), and Bacteroidetes (17.2%).
- A significant portion of the identified sequences were related to unknown bacteria, with many showing similarities to uncultured organisms found in human feces.