AIMC Topic: Genotype

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Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.

PloS one
This study investigates the influence of cadmium (Cd) stress on the micropropagation of Goji Berry (Lycium barbarum L.) across three distinct genotypes (ERU, NQ1, NQ7), employing an array of machine learning (ML) algorithms, including Multilayer Perc...

Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens.

Genes
Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increa...

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Plant communications
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target...

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.

The Plant journal : for cell and molecular biology
As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genot...

Deep neural network for the prediction of KRAS, NRAS, and BRAF genotypes in left-sided colorectal cancer based on histopathologic images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: The KRAS, NRAS, and BRAF genotypes are critical for selecting targeted therapies for patients with metastatic colorectal cancer (mCRC). Here, we aimed to develop a deep learning model that utilizes pathologic whole-slide images (WSIs) to ...

Machine learning based DNA melt curve profiling enables automated novel genotype detection.

BMC bioinformatics
Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening f...

Application of machine learning approaches for predicting hemophilia A severity.

Journal of thrombosis and haemostasis : JTH
BACKGROUND: Hemophilia A (HA) is an X-linked congenital bleeding disorder, which leads to deficiency of clotting factor (F) VIII. It mostly affects males, and females are considered carriers. However, it is now recognized that variants of F8 in femal...

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.

International journal of molecular sciences
The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a wa...

Self-replicating artificial neural networks give rise to universal evolutionary dynamics.

PLoS computational biology
In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRAN...

An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.

Human genetics
Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an...