AIMC Topic: Phenotype

Clear Filters Showing 331 to 340 of 1034 articles

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Mammalian genome : official journal of the International Mammalian Genome Society
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for ...

Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.

BMC bioinformatics
Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases affiliations. However, no accessible met...

Semiquantitative Fingerprinting Based on Pseudotargeted Metabolomics and Deep Learning for the Identification of and Its Major Serotypes.

Analytical chemistry
The rapid identification of pathogenic microorganism serotypes is still a bottleneck problem to be solved urgently. Compared with proteomics technology, metabolomics technology is directly related to phenotypes and has higher specificity in identifyi...

PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology.

IEEE/ACM transactions on computational biology and bioinformatics
Automated recognition of Human Phenotype Ontology (HPO) terms from clinical texts is of significant interest to the field of clinical data mining. In this study, we develop a combined deep learning method named PhenoBERT for this purpose. PhenoBERT u...

A Multi-Attention Approach for Person Re-Identification Using Deep Learning.

Sensors (Basel, Switzerland)
Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning a...

Label-free macrophage phenotype classification using machine learning methods.

Scientific reports
Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functio...

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.

Genome medicine
BACKGROUND: Rapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NI...

Predicting functional effects of ion channel variants using new phenotypic machine learning methods.

PLoS computational biology
Missense variants in genes encoding ion channels are associated with a spectrum of severe diseases. Variant effects on biophysical function correlate with clinical features and can be categorized as gain- or loss-of-function. This information enables...

Deep Learning Phenotype Automation and Cohort Analyses of 1,946 Knees Using the Coronal Plane Alignment of the Knee Classification.

The Journal of arthroplasty
BACKGROUND: The Coronal Plane Alignment of the Knee (CPAK) classification allows for knee phenotyping which can be used in preoperative planning prior to total knee arthroplasty. We used deep learning (DL) to automate knee phenotyping and analyzed CP...

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods.

Scientific reports
Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and ...