OBJECTIVE: To develop and evaluate machine learning (ML) approaches for muscle identification using intraoperative motor evoked potentials (MEPs), and to compare their performance to human experts.
BACKGROUND: Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factor...
BACKGROUND: Accurately predicting ovulation timing is critical for women undergoing natural cycle-frozen embryo transfer. However, the precise predicting of the ovulation timing remains challenging due to the lack of consensus among different clinics...
Obstructive sleep apnea (OSA) is closely associated with the development and chronicity of temporomandibular disorder (TMD). Given the intricate pathophysiology of both OSA and TMD, comprehensive diagnostic approaches are crucial. This study aimed to...
Artificial insemination (AI) success in bovine reproduction is vital for the cattle industry's economic sustainability and for advancing the understanding of reproductive physiology. Identify high-fertile animals' fertility is a complex task due to m...
The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug desig...
We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usuall...
BACKGROUND: Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and add...
BACKGROUND: Accurate patient outcome prediction in the intensive care unit (ICU) can potentially lead to more effective and efficient patient care. Deep learning models are capable of learning from data to accurately predict patient outcomes, but the...
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