AIMC Topic: Phenotype

Clear Filters Showing 931 to 940 of 1034 articles

Influenza virus genotype to phenotype predictions through machine learning: a systematic review.

Emerging microbes & infections
BACKGROUND: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in add...

A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.

Briefings in bioinformatics
Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate res...

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions.

Plant physiology
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance ...

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.

Plant physiology
Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping ep...

Learning Cellular Phenotypes through Supervision.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Image-based cell phenotyping is an important and open problem in computational pathology. The two principal challenges are: 1) making the cell cluster properties insensitive to experimental settings (like seed point and feature selection) and 2) ensu...

Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.

Briefings in bioinformatics
Single-cell transcriptomics technologies have vast potential in advancing our understanding of cellular heterogeneity in complex tissues. While methods to interpret single-cell transcriptomics data are developing rapidly, challenges in most analysis ...

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

Briefings in bioinformatics
This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusabl...

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.

Briefings in bioinformatics
OBJECTIVE: Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML)...

PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge.

Current protocols
The Pharmacogenomics Knowledgebase (PharmGKB) is an integrated online knowledge resource for the understanding of how genetic variation contributes to variation in drug response. Our focus includes not only pharmacogenomic information useful for clin...