Identification of Active Molecules against Thrombocytopenia through Machine Learning.

Thrombocytopenia, which is associated with thrombopoietin (TPO) deficiency, presents very limited treatment options and can lead to life-threatening complications. Discovering new therapeutic agents against thrombocytopenia has proven to be a challenging task using traditional screening approaches. Fortunately, machine learning (ML) techniques offer a rapid avenue for exploring chemical space, thereby increasing the likelihood of uncovering new drug candidates.

The research progress on synchronous endometrial and ovarian carcinoma.

Synchronous endometrial and ovarian carcinoma (SEOC) is the most common combination of primary double cancer in the female reproductive system. The etiology and pathogenesis of SEOC remain unclear, and clinically, it is often misdiagnosed as metastatic cancer, affecting the formulation of treatment plans and prognosis for patients. This article provides a review of its epidemiology, pathological and clinical characteristics, risk factors, pathogenesis, diagnosis, treatment, and prognosis.

A novel method for predicting DNA N-methylcytosine sites based on deep forest algorithm.

N-methyladenosine (4mC) methylation is an essential epigenetic modification of deoxyribonucleic acid (DNA) that plays a key role in many biological processes such as gene expression, gene replication and transcriptional regulation. Genome-wide identification and analysis of the 4mC sites can better reveal the epigenetic mechanisms that regulate various biological processes. Although some high-throughput genomic experimental methods can effectively facilitate the identification in a genome-wide scale, they are still too expensive and laborious for routine use.