An ensemble-based drug-target interaction prediction approach using multiple feature information with data balancing.

Background: Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately.

Efficient machine learning model for predicting drug-target interactions with case study for Covid-19.

Background: Discover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process.

Prevalence of extramucosal fungal elements in sinonasal polyposis: a mycological and pathologic study in an Egyptian population.

Objective And Hypothesis: The objective of the study was to define the true incidence of fungal elements in the nasal and sinus mucous in cases of chronic rhinosinusitis (CRS) with bilateral polyposis compared with normal controls-in an Egyptian African population-via mycological and histologic techniques.

Study Design: This study was conducted prospectively on 100 patients with the clinical diagnosis of CRS with bilateral nasal polyposis. Fifty volunteers with no history of nasal or paranasal sinus disease served as a control group.