AIMC Topic: Machine Learning

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The variant artificial intelligence easy scoring (VARIES) system.

Computers in biology and medicine
PURPOSE: Medical artificial intelligence (MAI) is artificial intelligence (AI) applied to the healthcare field. AI can be applied to many different aspects of genetics, such as variant classification. With little or no prior experience in AI coding, ...

Not just "big" data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions.

Behaviour research and therapy
There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular...

Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning.

Journal of chemical information and modeling
Graph neural network (GNN)-based deep learning (DL) models have been widely implemented to predict the experimental aqueous solvation free energy, while its prediction accuracy has reached a plateau partly due to the scarcity of available experimenta...

Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images.

Communications biology
Combining experiments with artificial intelligence algorithms, we propose a machine learning based approach called wrinkle force microscopy (WFM) to extract the cellular force distributions from the microscope images. The full process can be divided ...

Explainable multiview framework for dissecting spatial relationships from highly multiplexed data.

Genome biology
The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spati...

Two-year death prediction models among patients with Chagas Disease using machine learning-based methods.

PLoS neglected tropical diseases
Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the hi...

Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications.

IEEE journal of biomedical and health informatics
AI healthcare applications rely on sensitive electronic healthcare records (EHRs) that are scarcely labelled and are often distributed across a network of the symbiont institutions. It is challenging to train the effective machine learning models on ...

Data Pre-Processing Using Neural Processes for Modeling Personalized Vital-Sign Time-Series Data.

IEEE journal of biomedical and health informatics
Clinical time-series data retrieved from electronic medical records are widely used to build predictive models of adverse events to support resource management. Such data is often sparse and irregularly-sampled, which makes it challenging to use many...

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation.

IEEE journal of biomedical and health informatics
Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to a...

A Heterogeneous Ensemble Learning Method For Neuroblastoma Survival Prediction.

IEEE journal of biomedical and health informatics
Neuroblastoma is a pediatric cancer with high morbidity and mortality. Accurate survival prediction of patients with neuroblastoma plays an important role in the formulation of treatment plans. In this study, we proposed a heterogeneous ensemble lear...