Class imbalance learning with Bayesian optimization applied in drug discovery.

Machine intelligence (MI), including machine learning and deep learning, have been regarded as promising methods to reduce the prohibitively high cost of drug development. However, a dilemma within MI has limited its wide application: machine learning models are easier to interpret but yield worse predictive performance than deep learning models. Therefore, we propose a pipeline called Class Imbalance Learning with Bayesian Optimization (CILBO) to improve the performance of machine learning models in drug discovery.

Regional gender differences in an autosomal disease result in corresponding diversity differences.

Regional gender differences in autosomal chromosome disorders have been observed repeatedly. However, the corresponding diversity changes remain unconfirmed. By analyzing previously published thalassemia data from the Dai people in Dehong and Xishuangbanna (two regions in Yunnan Province, China), we found that several sequence types, including HBA CNV and HBB mutations, significantly depend on gender in Xishuangbanna but not in Dehong.

Correction to: Relative abundance of β-thalassemia-related mutations in southern China correlates with geographical coordinates.

Figure 1c. is with numeric error. The error can not result in any change of discussion and conclusion.

Transcriptome-Wide Identification and Prediction of miRNAs and Their Targets in Paris polyphylla var. yunnanensis by High-Throughput Sequencing Analysis.

Long dormancy period of seeds limits the large-scale artificial cultivation of the scarce var. , an important traditional Chinese medicine. Characterizing miRNAs and their targets is crucial to understanding the role of miRNAs during seed dormancy in this species.