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Computers in biology and medicine.

Periodical
Biology
Medical Informatics
Medicine
Electronic Data Processing
Publisher:
Pergamon Press.. New York : Elsevier
Frequency: Bimonthly
Country: United States
Language: English
Start Year:1970 -
ISSN:
0010-4825 (Print)
1879-0534 (Electronic)
0010-4825 (Linking)
Impact Factor
7.7
2022
NLM ID:1250250
(DNLM):C36120000(s)
(OCoLC):01564614
Coden:CBMDAW
LCCN:72623964
Classification:W1 CO457T
A multi-task learning model for clinically interpretable sesamoiditis grading.
Computers in biology and medicine    September 25, 2024   Volume 182 109179 doi: 10.1016/j.compbiomed.2024.109179
Guo L, Tahir AM, Hore M, Collins A, Rideout A, Wang ZJ.Sesamoiditis is a common equine disease with varying severity, leading to increased injury risks and performance degradation in horses. Accurate grading of sesamoiditis is crucial for effective treatment. Although deep learning-based approaches for grading sesamoiditis show promise, they remain underexplored and often lack clinical interpretability. To address this issue, we propose a novel, clinically interpretable multi-task learning model that integrates clinical knowledge with machine learning. The proposed model employs a dual-branch decoder to simultaneously perform sesamoiditis grading ...
Explainable machine learning for assessing upper respiratory tract of racehorses from endoscopy videos.
Computers in biology and medicine    August 21, 2024   Volume 181 109030 doi: 10.1016/j.compbiomed.2024.109030
Tahir AM, Guo L, Ward RK, Yu X, Rideout A, Hore M, Wang ZJ.Laryngeal hemiplegia (LH) is a major upper respiratory tract (URT) complication in racehorses. Endoscopy imaging of horse throat is a gold standard for URT assessment. However, current manual assessment faces several challenges, stemming from the poor quality of endoscopy videos and subjectivity of manual grading. To overcome such limitations, we propose an explainable machine learning (ML)-based solution for efficient URT assessment. Specifically, a cascaded YOLOv8 architecture is utilized to segment the key semantic regions and landmarks per frame. Several spatiotemporal features are then ex...
Binary Horse herd optimization algorithm with crossover operators for feature selection.
Computers in biology and medicine    December 18, 2021   Volume 141 105152 doi: 10.1016/j.compbiomed.2021.105152
Awadallah MA, Hammouri AI, Al-Betar MA, Braik MS, Elaziz MA.This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are al...
Computational insights into differential interaction of mammalian angiotensin-converting enzyme 2 with the SARS-CoV-2 spike receptor binding domain.
Computers in biology and medicine    November 3, 2021   Volume 141 105017 doi: 10.1016/j.compbiomed.2021.105017
Lupala CS, Kumar V, Su XD, Wu C, Liu H.The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Angiotensin-converting enzyme 2 (ACE2) has been identified as the host cell receptor that binds to the receptor-binding domain (RBD) of the SARS-COV-2 spike protein and mediates cell entry. Because the ACE2 proteins are widely available in mammals, it is important to investigate the interactions between the RBD and the ACE2 of other mammals. Here we analyzed the sequences of ACE2 proteins from 16 mammals, predicted the structures of ACE2-RBD complexes by homology modeling, and refi...