AIMC Topic: Machine Learning

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Using Machine Learning to Improve Personalised Prediction: A Data-Driven Approach to Segment and Stratify Populations for Healthcare.

Studies in health technology and informatics
Population Health Management typically relies on subjective decisions to segment and stratify populations. This study combines unsupervised clustering for segmentation and supervised classification, personalised to clusters, for stratification. An in...

Identifying nonadditive contributions to the hydrophobicity of chemically heterogeneous surfaces via dual-loop active learning.

The Journal of chemical physics
Hydrophobic interactions drive numerous biological and synthetic processes. The materials used in these processes often possess chemically heterogeneous surfaces that are characterized by diverse chemical groups positioned in close proximity at the n...

BERT-Kcr: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models.

Bioinformatics (Oxford, England)
MOTIVATION: As one of the most important post-translational modifications (PTMs), protein lysine crotonylation (Kcr) has attracted wide attention, which involves in important physiological activities, such as cell differentiation and metabolism. Howe...

Towards gender equity in artificial intelligence and machine learning applications in dermatology.

Journal of the American Medical Informatics Association : JAMIA
There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin cancers and assessment of other dermatologic conditions. As these technologies continue to expand, i...

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

Proceedings of the National Academy of Sciences of the United States of America
RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-at...

Validation of a Machine Learning Model for Early Shock Detection.

Military medicine
OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity...

Prediction of whole-cell transcriptional response with machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we prese...

Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.

Archives of pathology & laboratory medicine
CONTEXT.—: Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relative...

Lung Cancer Detection Using Machine Learning Techniques.

Critical reviews in biomedical engineering
Cancer has been the deadliest of diseases since decades constituting a large number of deaths annually. Lung cancer remains one of the most significant public health issues, accounting for a substantial proportion of cancer-related deaths globally. D...

SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level.

IEEE computer graphics and applications
Understanding the interpretation of machine learning (ML) models has been of paramount importance when making decisions with societal impacts, such as transport control, financial activities, and medical diagnosis. While local explanation techniques ...