AIMC Topic: Machine Learning

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Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

BMC infectious diseases
BACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how...

Efficient Automated Disease Diagnosis Using Machine Learning Models.

Journal of healthcare engineering
Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagno...

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical and translational science
Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a m...

Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design.

Drug metabolism and pharmacokinetics
The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning predic...

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures.

Genetic epidemiology
The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpre...

Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities.

Genome biology
A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay mul...