AIMC Topic: Phenotype

Clear Filters Showing 871 to 880 of 1034 articles

Learning genotype-phenotype associations from gaps in multi-species sequence alignments.

Briefings in bioinformatics
Understanding the genetic basis of phenotypic variation is fundamental to biology. Here we introduce GAP, a novel machine learning framework for predicting binary phenotypes from gaps in multi-species sequence alignments. GAP employs a neural network...

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.

Briefings in bioinformatics
This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencin...

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.

Briefings in bioinformatics
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
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.)
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
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
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
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...

A general framework for developing computable clinical phenotype algorithms.

Journal of the American Medical Informatics Association : JAMIA
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...