AIMC Topic: Phenotype
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Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.
Briefings in bioinformatics
Nov 22, 2024
Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant chall...
Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning.
Journal of experimental botany
Nov 15, 2024
Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describ...
Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.
G3 (Bethesda, Md.)
Nov 6, 2024
In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, g...
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.
Briefings in bioinformatics
Sep 23, 2024
The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the a...
Fine-tuning large language models for rare disease concept normalization.
Journal of the American Medical Informatics Association : JAMIA
Sep 1, 2024
OBJECTIVE: We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO).
Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
Aug 3, 2024
Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recen...
AI-recognized mitochondrial phenotype enables identification of drug targets.
Nature computational science
Aug 1, 2024
A general framework for developing computable clinical phenotype algorithms.
Journal of the American Medical Informatics Association : JAMIA
Aug 1, 2024
OBJECTIVE: To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machi...
BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.
Briefings in bioinformatics
Jul 25, 2024
Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratificati...
Predicting Cardiovascular Disease Risk in Tobacco Users Using Machine Learning Algorithms.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Cardiovascular Diseases (CVDs) present a substantial global health burden, with tobacco use as a major risk factor. While extensive research has identified several risk factors for CVDs, there is a gap in predictive models that account for a combinat...
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