AIMC Topic: Machine Learning

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Attention-augmented multi-domain cooperative graph representation learning for molecular interaction prediction.

Neural networks : the official journal of the International Neural Network Society
Accurate identification of molecular interactions is crucial for biological network analysis, which can provide valuable insights into fundamental regulatory mechanisms. Despite considerable progress driven by computational advancements, existing met...

Machine learning-enhanced back muscle strength prediction considering lifting condition and individual characteristics.

International journal of occupational safety and ergonomics : JOSE
This study investigated factors influencing back muscle strength, focusing on sex, forearm posture and lifting height. Lower back pain, prevalent in industries involving manual materials handling, is closely linked to back muscle strength. The study ...

Navigating prevalence shifts in image analysis algorithm deployment.

Medical image analysis
Domain gaps are significant obstacles to the clinical implementation of machine learning (ML) solutions for medical image analysis. Although current research emphasizes new training methods and network architectures, the specific impact of prevalence...

Identification of a gene signature and prediction of overall survival of patients with stage IV colorectal cancer using a novel machine learning approach.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
OBJECTIVE: We sought to characterize unique gene signature patterns associated with worse overall survival (OS) among patients with stage IV colorectal cancer (CRC) using a machine learning (ML) approach.

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.

Nature reviews. Cancer
Mathematical modelling has proven to be a valuable tool in predicting the delivery and efficacy of molecular, antibody-based, nano and cellular therapy in solid tumours. Mathematical models based on our understanding of the biological processes at su...

Artificial Intelligence non-invasive methods for neonatal jaundice detection: A review.

Artificial intelligence in medicine
Neonatal jaundice is a common and potentially fatal health condition in neonates, especially in low and middle income countries, where it contributes considerably to neonatal morbidity and death. Traditional diagnostic approaches, such as Total Serum...

Hyperbolic multivariate feature learning in higher-order heterogeneous networks for drug-disease prediction.

Artificial intelligence in medicine
New drug discovery has always been a costly, time-consuming process with a high failure rate. Repurposing existing drugs offers a valuable alternative and reduces the risks associated with developing new drugs. Various experimental methods have been ...

ML-AMPs designed through machine learning show antifungal activity against C. albicans and therapeutic potential on mice model with candidiasis.

Life sciences
AIMS: C. albicans resistant strains have led to increasingly severe treatment challenges. Antimicrobial peptides with low resistance-inducing propensity for pathogens have been developed. A series of antimicrobial peptides de novo designed through ma...

Long-tailed medical diagnosis with relation-aware representation learning and iterative classifier calibration.

Computers in biology and medicine
Recently computer-aided diagnosis has demonstrated promising performance, effectively alleviating the workload of clinicians. However, the inherent sample imbalance among different diseases leads algorithms biased to the majority categories, leading ...