AIMC Topic: Disease Resistance

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Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.

Scientific reports
Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust re...

Resistance gene identification from Larimichthys crocea with machine learning techniques.

Scientific reports
The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. ...

DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants.

Computers in biology and medicine
Plant disease outbreak is increasing rapidly around the globe and is a major cause for crop loss worldwide. Plants, in turn, have developed diverse defense mechanisms to identify and evade different pathogenic microorganisms. Early identification of ...

Harnessing the fish gut microbiome and immune system to enhance disease resistance in aquaculture.

Fish & shellfish immunology
The increasing global reliance on aquaculture is challenged by disease outbreaks, exacerbated by antibiotic resistance, and environmental stressors. Traditional strategies, such as antibiotic treatments and chemical interventions, are becoming less e...

Machine learning for phytopathology: from the molecular scale towards the network scale.

Briefings in bioinformatics
With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within...

Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.

The plant genome
New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive natur...

Distinct Transcriptional and Anti-Mycobacterial Profiles of Peripheral Blood Monocytes Dependent on the Ratio of Monocytes: Lymphocytes.

EBioMedicine
The ratio of monocytes and lymphocytes (ML ratio) in peripheral blood is associated with tuberculosis and malaria disease risk and cancer and cardiovascular disease outcomes. We studied anti-mycobacterial function and the transcriptome of monocytes i...