AIMC Topic: Machine Learning

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Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults.

Predictive Models of Genetic Redundancy in Arabidopsis thaliana.

Molecular biology and evolution
Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studi...

Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach.

Mathematical biosciences and engineering : MBE
Harmful algal species are present in the Mediterranean Sea and are often associated with toxic events affecting the nearby coastal zones. The presence of 18 marine microalgae, at genus level, associated with potentially harmful characteristics was pr...

Towards realizing the vision of precision medicine: AI based prediction of clinical drug response.

Brain : a journal of neurology
Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring inte...

The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease.

Inflammatory bowel diseases
BACKGROUND: Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routi...

Special issue: Informatics & data-driven medicine.

Mathematical biosciences and engineering : MBE
The current state of the development of Medicine today is changing dramatically. Previously, data of the patient's health were collected only during a visit to the clinic. These were small chunks of information obtained from observations or experimen...

Pathological Spectra of the Fisher Information Metric and Its Variants in Deep Neural Networks.

Neural computation
The Fisher information matrix (FIM) plays an essential role in statistics and machine learning as a Riemannian metric tensor or a component of the Hessian matrix of loss functions. Focusing on the FIM and its variants in deep neural networks (DNNs), ...

Predicting differential ion mobility behaviour using machine learning.

The Analyst
Although there has been a surge in popularity of differential mobility spectrometry (DMS) within analytical workflows, determining separation conditions within the DMS parameter space still requires manual optimization. A means of accurately predicti...

Accelerating the optimization of enzyme-catalyzed synthesis conditions machine learning and reactivity descriptors.

Organic & biomolecular chemistry
Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge and computer predictions. In this wor...

dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.

Nucleic acids research
Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties ...