AIMC Topic: Machine Learning

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Machine Learning Techniques Applied to the Study of Drug Transporters.

Molecules (Basel, Switzerland)
With the advancement of computer technology, machine learning-based artificial intelligence technology has been increasingly integrated and applied in the fields of medicine, biology, and pharmacy, thereby facilitating their development. Transporters...

Machine learning for lumbar and pelvis kinematics clustering.

Computer methods in biomechanics and biomedical engineering
Clustering algorithms such as k-means and agglomerative hierarchical clustering (HCA) may provide a unique opportunity to analyze time-series kinematic data. Here we present an approach for determining number of clusters and which clustering algorith...

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework.

Nature genetics
Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals...

Improving the classification of cardinality phenotypes using collections.

Journal of biomedical semantics
MOTIVATION: Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in mode...

Cross-protein transfer learning substantially improves disease variant prediction.

Genome biology
BACKGROUND: Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation m...

New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements.

Scientific reports
Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of e...

Machine learning and statistical models for analyzing multilevel patent data.

Scientific reports
A recent surge of patent applications among public hospitals in China has aroused significant research interest. A country's healthcare innovation capacity can be measured by its number of patents. This paper explores the link between the number of p...

Explanations as a New Metric for Feature Selection: A Systematic Approach.

IEEE journal of biomedical and health informatics
With the extensive use of Machine Learning (ML) in the biomedical field, there was an increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex hidden relationships between variables for medical practiti...

Physiologically-Informed Gaussian Processes for Interpretable Modelling of Psycho-Physiological States.

IEEE journal of biomedical and health informatics
The widespread popularity of Machine Learning (ML) models in healthcare solutions has increased the demand for their interpretability and accountability. In this paper, we propose the Physiologically-Informed Gaussian Process (PhGP) classification mo...

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

PloS one
BACKGROUND: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate ...