Impact of non-genetic factors on clinical mastitis incidence in Murrah buffaloes

Jaglan, Komal; Sukhija, Nidhi; George, Linda K.; Alex, Rani

doi:10.21203/rs.3.rs-1967312/v1

Cited by 1 publication

(1 citation statement)

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“…It will also help to choose candidate promoters for their incorporation in transgenic cassettes [16]. In the analysis of sequence data to identify SNPs and CNVs in Indian cattle and Buffaloes many tools and software have been used and they were built using ANN and CNN [17,18] and also for finding significant association of SNPs with phenotypes using GWAS [19,20]. Also these tools are helpful for finding epitopes [21] and to design vaccines against food allergies and pathogens [22].…”

Section: Applications In Genomicsmentioning

confidence: 99%

Deep Learning in Neural Networks and their Application in Genomics

KK¹,

Sukhija²,

Jayakumar³

et al. 2023

Act Sci VetSci

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Deep learning has emerged as a powerful tool in genomics, utilizing neural networks to uncover complex patterns in large datasets. This review explores the application of deep learning in genomics, focusing on supervised and unsupervised learning tasks.The process involves training models with appropriate evaluation metrics and curated datasets to optimize performance. Balancing training data and model flexibility is crucial to avoid underfitting or overfitting. Deep learning models, with their high capacity and flexibility, outperform traditional techniques like logistic regression and support vector machines in genomics. Various applications of deep learning in genomics are includes predicting protein sequence specificity, determining cis-regulatory elements, analyzing splicing regulation and gene expression, and predicting genomic variants. Deep learning proves particularly effective in studying functional genomics and regulatory elements, leveraging techniques from computer vision and natural language processing. Overall, deep learning shows promise in advancing genomics research and understanding complex biological processes.

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