The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug-target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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http://dx.doi.org/10.1093/bib/bbz157 | DOI Listing |
STAR Protoc
December 2024
Princess Máxima Center for Pediatric Oncology, Utrecht 3584 CS, the Netherlands; Oncode Institute, Utrecht 3521 AL, the Netherlands. Electronic address:
The study of somatic mutations in single cells provides insights into aging and carcinogenesis, which is complicated by the dependency on whole-genome amplification (WGA). Here, we describe a detailed workflow starting from single-cell isolation to WGA by primary template-directed amplification (PTA), sequencing, quality control, and downstream analyses. A machine learning approach, the PTA Analysis Toolkit (PTATO), is used to filter the hundreds to thousands of artificial variants induced by WGA from true mutations at high sensitivity and accuracy.
View Article and Find Full Text PDFGut Microbes
December 2025
Hypertension Research Laboratory, School of Biological Sciences, Faculty of Science, Monash, Clayton, Australia.
The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut health, and CVD. Traditional CVD risk factors (age, BMI, sex, blood pressure) explained < 10% of the proteome variance.
View Article and Find Full Text PDFHum Genomics
December 2024
Precision Medicine Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Atherosclerosis (AS) is a major cause of cardiovascular diseases and neutrophil extracellular traps (NETs) may be actively involved in the development of atherosclerosis. Identifying key biomarkers in this process is essential for developing targeted treatments for AS.
Methods: We performed bioinformatics analysis using a NETosis-related gene (NRGs) set and three AS datasets (GSE100927, GSE21545, and GSE159677).
BMC Pregnancy Childbirth
December 2024
Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, 5612 AZ, North Brabant, The Netherlands.
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation on data from 8,830 women in the Nulliparous Pregnancy Outcomes Study: New Mothers-to-Be (nuMoM2b) dataset at three prenatal visits: - , - , and - weeks of gestational age (GA). The models' performance, assessed using Area Under the Curve (AUC), sensitivity, specificity, and accuracy, consistently improved with the incorporation of data from later prenatal visits.
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