AIMC Topic: Machine Learning

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Metagenomics-Toolkit: the flexible and efficient cloud-based metagenomics workflow featuring machine learning-enabled resource allocation.

NAR genomics and bioinformatics
The metagenome analysis of complex environments with thousands of datasets, such as those in the Sequence Read Archive, requires substantial computational resources for it to be completed within a reasonable time frame. Efficient use of infrastructur...

Optimizing genomic prediction with transfer learning under a ridge regression framework.

The plant genome
Genomic selection (GS) is a predictive plant and animal methodology that allows the selection of plants and animals based on predictions without the need to measure the phenotype. However, its practical application requires challenging prediction acc...

How can machine learning inform about chemical risks in circular textiles?

Integrated environmental assessment and management
Hazardous chemicals in textiles represent a serious health issue. This is mainly due to missing data on the used chemicals and/or on their hazard, which prevents proper chemical risk assessment. Although identifying and filling these data gaps is cru...

Identification and validation of epithelial‑mesenchymal transition‑related genes for diabetic nephropathy by WGCNA and machine learning.

Molecular medicine reports
Diabetic nephropathy (DN) is the main cause of end‑stage renal disease, with epithelial‑mesenchymal transition (EMT) serving a key role in its initiation and progression. Nevertheless, the precise mechanisms involved remain unidentified. The present ...

Machine learning-driven GWAS uncovers novel candidate genes for resistance to frosty pod rot and witches' broom disease in cacao.

The plant genome
Cacao (Theobroma cacao), the source of chocolate, is threatened by devastating diseases like frosty pod rot (FPR) and witches' broom disease (WBD), impacting global production and farmer livelihoods. Here, we employ a machine learning-driven genome-w...

Prediction model for postoperative urinary retention in patients undergoing totally extraperitoneal groin hernia repair.

Surgery
BACKGROUND: Postoperative urinary retention remains a common complication after totally extraperitoneal groin hernia repair, often prolonging hospitalization and increasing patient discomfort. This study aimed to develop a prediction model using mach...

Utilizing Predictive Analytics to Understand Neurogenic Bladder Symptom Score (NBSS) Variations in Adults With Acquired Spinal Cord Injury.

Neurourology and urodynamics
INTRODUCTION: Individuals with spinal cord injury (SCI) have varying bladder health trajectories after their injury. We explored whether a predictive machine learning model could identify which variables impact urinary symptoms.

Artificial intelligence and digital twins for the personalised prediction of hypertension risk.

Computers in biology and medicine
Hypertension is a significant global health challenge, contributing substantially to morbidity and mortality through its association with various cardiovascular diseases. Traditional approaches to hypertension risk prediction, which rely on broad epi...

Mechanobiology-guided machine learning models for predicting long bone fracture healing across diverse scenarios.

Computers in biology and medicine
BACKGROUND: Fracture healing is a complex, time-dependent process governed by biological and mechanical factors, including implant properties. While finite element (FE) modeling provides detailed mechanobiological insights into this process, its comp...

Machine learning models for predicting chemotherapy-induced adverse drug reactions in colorectal cancer patients.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Chemotherapy-induced adverse drug reactions (ADRs) are common in patients with colorectal cancer. We developed four machine learning models to predict chemotherapy-induced ADRs and assessed the performance. These models leverage high-dime...