AIMC Topic: Machine Learning

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Predictive Minisci late stage functionalization with transfer learning.

Nature communications
Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates t...

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models.

Frontiers in public health
INTRODUCTION: Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a p...

Stacked deep learning approach for efficient SARS-CoV-2 detection in blood samples.

Artificial intelligence in medicine
Identifying COVID-19 through blood sample analysis is crucial in managing the disease and improving patient outcomes. Despite its advantages, the current test demands certified laboratories, expensive equipment, trained personnel, and 3-4 h for resul...

Review of applications of artificial intelligence (AI) methods in crop research.

Journal of applied genetics
Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computatio...

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

Journal of the American Geriatrics Society
BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screen...

Proceedings from the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference.

The Journal of allergy and clinical immunology
Here, we summarize the proceedings of the inaugural Artificial Intelligence in Primary Immune Deficiencies conference, during which experts and advocates gathered to advance research into the applications of artificial intelligence (AI), machine lear...

AI-driven GPCR analysis, engineering, and targeting.

Current opinion in pharmacology
This article investigates the role of recent advances in Artificial Intelligence (AI) to revolutionise the study of G protein-coupled receptors (GPCRs). AI has been applied to many areas of GPCR research, including the application of machine learning...

Segmenting mechanically heterogeneous domains via unsupervised learning.

Biomechanics and modeling in mechanobiology
From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformatio...

Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning.

Nature communications
Antimicrobial resistance (AMR) and healthcare associated infections pose a significant threat globally. One key prevention strategy is to follow antimicrobial stewardship practices, in particular, to maximise targeted oral therapy and reduce the use ...