Kaveh Kavousi

TL;DR: Modern machine learning methods are used to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degrees of ascites to avoid unnecessary risks and costs for patients with acute stages of the disease.

TL;DR: Using machine learning models, especially ensemblelearning models, can remarkably increase the prediction performance and allow practitioners to predict EV risk at any clinical visit and decrease unneeded esophagogastroduodenoscopy (EGD) and consequently reduce morbidity, mortality, and cost of the long-term follow-ups for patients with cirrhosis.

TL;DR: A new pathway enrichment analysis based on Bayesian network (BNrich) as an approach in PEA is proposed and the results are compared to well-known enrichment methods such as signaling pathway impact analysis (SPIA), bi-level meta-analysis (BLMA) and topology-based pathway enrichmentAnalysis (TPEA).

TL;DR: A significant relationship between the stability of fish Hbs and the habitat depth requirements is demonstrated and provided the evidence for three different molecular components including native (N), intermediate (I) and denatured (D) in sturgeon Hbs.

TL;DR: In this article, a novel α-amylase named PersiAmy1 has been identified through the multi-stage in-silico screening pipeline from the rumen metagenomic data.