AIMC Topic: Machine Learning

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Apax: A Flexible and Performant Framework for the Development of Machine-Learned Interatomic Potentials.

Journal of chemical information and modeling
We introduce Atomistic learned potentials in JAX (apax), a flexible and efficient open source software package for training and inference of machine-learned interatomic potentials. Built on the JAX framework, apax supports GPU acceleration and implem...

Transfer Learning for Heterocycle Retrosynthesis.

Journal of chemical information and modeling
Heterocycles are important scaffolds in medicinal chemistry that can be used to modulate the binding mode as well as the pharmacokinetic properties of drugs. The importance of heterocycles has been exemplified by the publication of numerous data sets...

PuMA: PubMed gene/cell type-relation Atlas.

BMC bioinformatics
BACKGROUND: Rapid extraction and visualization of cell-specific gene expression is important for automatic cell type annotation, e.g. in single cell analysis. There is an emerging field in which tools such as curated databases or machine learning met...

Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.

BMC medical informatics and decision making
INTRODUCTION: The use of machine learning (ML) in intensive care units (ICUs) has led to a large yet fragmented body of literature. It is imperative to conduct a systematic analysis and synthesis of this research to identify methodological trends, cl...

Developing and validating machine learning models to predict next-day extubation.

Scientific reports
Criteria to identify patients who are ready to be liberated from mechanical ventilation (MV) are imprecise, often resulting in prolonged MV or reintubation, both of which are associated with adverse outcomes. Daily protocol-driven assessment of the n...

Intelligent routing for human activity recognition in wireless body area networks.

Scientific reports
Human activity recognition (HAR), driven by machine learning techniques, offer the detection of diverse activities such as walking, running, and more. Considering the dynamic nature, limited energy and mobility of wireless body area networks (WBANs),...

Classifying social and physical pain from multimodal physiological signals using machine learning.

Scientific reports
Accurate pain assessment is essential for effective management; however, most studies have focused on differentiating pain from non-pain or estimating pain intensity rather than distinguishing between distinct pain types. We present a machine learnin...

COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors.

BMC public health
BACKGROUND: The COVID-19 pandemic highlighted the need to understand factors influencing individuals' risk perceptions and health behaviors. This study aimed to explore the roles of individuals' knowledge, perception, and health-related issues in det...

The application and predictive value of the weight-adjusted-waist index in BC prevalence assessment: a comprehensive statistical and machine learning analysis using NHANES data.

BMC cancer
BACKGROUND: Obesity is a known risk factor for breast cancer (BC), but conventional metrics such as body mass index (BMI) may insufficiently capture central adiposity. The weight-adjusted waist index (WWI) has emerged as a potentially superior anthro...

Machine learning-based high-benefit approach versus traditional high-risk approach in statin therapy: the Shizuoka Kokuho database study.

Scientific reports
Statins are widely prescribed for the primary prevention of cardiovascular diseases, yet individual responses vary, necessitating personalized treatment strategies. Conventional approaches prioritize treating high-risk patients, but advancements in m...