This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390918 | PMC |
http://dx.doi.org/10.1177/1177932216687545 | DOI Listing |
Environ Sci Technol
January 2025
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Air pollution is a leading contributor to the global disease burden. However, the complex nature of the chemicals to which humans are exposed through inhalation has obscured the identification of the key compounds responsible for diseases. Here, we develop a network topology-based framework to identify key toxic compounds in the airborne chemical exposome.
View Article and Find Full Text PDFBrain Inform
January 2025
Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland.
A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions.
View Article and Find Full Text PDFBreast Cancer
January 2025
Division of Breast and Endocrine Surgery, Department of Surgery, School of Medicine, Hyogo Medical University, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan.
Purpose: The aim of this study was to examine the clinical utility of tumor-infiltrating lymphocytes (TILs) evaluated by "average" and "hot-spot" methods in breast cancer patients.
Methods: We examined 367 breast cancer patients without neoadjuvant chemotherapy (NAC) by average and hot-spot methods to determine the consistency of TIL scores between biopsy and surgical specimens. TIL scores before NAC were also compared with the pathological complete response (pCR) rate and clinical outcomes in 144 breast cancer patients that received NAC.
Eur J Sport Sci
February 2025
School of Human Sciences (Exercise and Sport Science), The University of Western Australia, Perth, Australia.
End-range movements are among the most demanding but least understood in the sport of tennis. Using male Hawk-Eye data from match-play during the 2021-2023 Australian Open tournaments, we evaluated the speed, deceleration, acceleration, and shot quality characteristics of these types of movement in men's Grand Slam tennis. Lateral end-range movements that incorporated a change of direction (CoD) were identified for analysis using k-means (end-range) and random forest (CoD) machine learning models.
View Article and Find Full Text PDFJ Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!