A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks.

The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particularly single-cell transcriptomic data containing rich cell-to-cell variations, it is highly desirable to infer TF-gene interactions (TGIs) using deep learning technologies. Numerous models or software including deep learning-based algorithms have been designed to identify transcriptional regulatory relationships between TFs and the downstream genes.

TPepPro: a deep learning model for predicting peptide-protein interactions.

Motivation: Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-protein interactions (PepPIs) presents a very important approach but poses considerable technical challenges.

The output of cataract surgery performed by trained non-ophthalmologist physicians in rural areas: study of cataract outcomes and up-take of services report 7.

Background: Cataract is the leading cause of blindness worldwide and surgery can restore vision in most patients. Some patients have little access to surgical services due to lack of cataract surgeons and the unaffordable costs. In 2005 we built a service model that trained rural non-ophthalmologist physicians to perform cataract surgeries in rural China.

Repetitive Transient Ischemia-Induced Cardiac Angiogenesis is Mediated by Camkii Activation.

Background/aims: Coronary angiogenesis is an important protective mechanism in response to myocardial ischemia in coronary artery disease. However, the underlying mechanisms remain largely unclear. Here, we investigated the role of CaMKII activation in ischemia-induced cardiac angiogenesis.