AIMC Topic: Phenotype

Clear Filters Showing 381 to 390 of 1034 articles

Any colour you like: fish interacting with bioinspired robots unravel mechanisms promoting mixed phenotype aggregations.

Bioinspiration & biomimetics
Collective behaviours in homogeneous shoals provide several benefits to conspecifics, although mixed-species aggregations have been reported to often occur. Mixed aggregations may confer several beneficial effects such as antipredator and foraging ad...

Plant phenotype relationship corpus for biomedical relationships between plants and phenotypes.

Scientific data
Medicinal plants have demonstrated therapeutic potential for applicability for a wide range of observable characteristics in the human body, known as "phenotype," and have been considered favorably in clinical treatment. With an ever increasing inter...

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records.

Journal of neurodevelopmental disorders
BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyp...

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

Scientific reports
High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from gene...

Selective chemical probes can untangle the complexity of the plant cell endomembrane system.

Current opinion in plant biology
The endomembrane system is critical for plant growth and development and understanding its function and regulation is of great interest for plant biology research. Small-molecule targeting distinctive endomembrane components have proven powerful tool...

Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models.

PloS one
To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. The outcome is modeled as a linear function of the precision variables...

Reporting details of neuroimaging studies on individual traits prediction: A literature survey.

NeuroImage
Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in he...

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.

Genome biology
Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general stra...

Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction.

BMC plant biology
Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few app...

Phenotype-Based Threat Assessment.

Proceedings of the National Academy of Sciences of the United States of America
Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pa...