AIMC Topic: Machine Learning

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Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

Molecular pharmaceutics
While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) ma...

MultiscaleDTA: A multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction.

Methods (San Diego, Calif.)
The task of predicting drug-target affinity (DTA) plays an increasingly important role in the early stage of in silico drug discovery and development. Currently, a variety of machine learning-based methods have been presented for DTA prediction and a...

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Artificial Intelligence in healthcare is growing quickly in diagnostics and treatment management. Despite the quantity and variety of studies its role in clinical care is not clear. To identify the evidence gaps and characteristics of the Artificial ...

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks.

Sensors (Basel, Switzerland)
In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor ...

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.

Molecules (Basel, Switzerland)
The classification of biological neuron types and networks poses challenges to the full understanding of the human brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morpholog...

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network.

Respiratory research
BACKGROUND: Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk str...

FathomNet: A global image database for enabling artificial intelligence in the ocean.

Scientific reports
The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As baselines are sought by the research community, the volume and rate of...

Machine learning for cell type classification from single nucleus RNA sequencing data.

PloS one
With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into sp...

Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction.

Bioresource technology
In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Co...

A guide to systems-level immunomics.

Nature immunology
The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the character...