Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens DOI Open Access
Xiaochang Li,

Xiaoman Chen,

Qiulian Wang

et al.

Genes, Journal Year: 2024, Volume and Issue: 15(6), P. 690 - 690

Published: May 26, 2024

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 intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used illumina 50K SNP chip genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) bioinformatics methods were integrated fit genotypes 10 economic We evaluated effectiveness ML using Pearson correlation coefficients RMSE between predicted actual phenotypic values compared them rrBLUP BayesA. Our results indicated that algorithms exhibit significantly superior performance BayesA predicting body weight eggshell strength traits. Conversely, demonstrated 2–58% higher egg numbers. Additionally, incorporation suggestively significant SNPs obtained through GWAS into models resulted increase 0.1–27% across nearly all These findings suggest potential combining techniques improve future.

Language: Английский

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives DOI Open Access
Junqi Liu, Chengfei Zhang, Zhiyi Shan

et al.

Healthcare, Journal Year: 2023, Volume and Issue: 11(20), P. 2760 - 2760

Published: Oct. 18, 2023

In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide comprehensive overview present state AI applications orthodontics, which can be categorized into following domains: (1) diagnosis, cephalometric analysis, dental facial skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, decision making for extractions orthognathic surgery, outcome prediction; (3) clinical practice, practice guidance, remote care, documentation. We have witnessed broadening application accompanied by advancements its performance. Additionally, this outlines existing limitations within field offers future perspectives.

Language: Английский

Citations

33
The Future of Orthodontics: Deep Learning Technologies DOI Open Access
Aathira Surendran,

Pallavi Daigavane,

Sunita Shrivastav

et al.

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: June 10, 2024

Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, field of dentistry widely adopted information technology (IT), resulting several benefits, including decreased expenses, increased efficiency, need human expertise, reduced errors. The transition from preset rules to real-world examples, particularly machine (ML) artificial intelligence (AI), greatly benefited organization, analysis, storage medical data. learning, type AI, enables robots mimic neural networks, allowing them learn make decisions independently without explicit programming. Its ability automate cephalometric analysis enhance diagnosis through 3D imaging revolutionized orthodontic operations. models have potential significantly improve outcomes reduce errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, use technologies such cone-beam computed tomography (CBCT) can facilitate precise comprehensive examinations craniofacial architecture, tooth movements, airway dimensions. In today's era personalized medicine, deep learning's customize treatments individual patients propelled orthodontics forward tremendously. However, it is essential address issues related data privacy, model interpretability, ethical considerations before practices an responsible manner. Modern evolving, thanks deliver more accurate, effective, treatments, improving patient care develops.

Language: Английский

Citations

5
Comparative analysis of genomic prediction models based on body weight trait in large yellow croaker (Larimichthys crocea) DOI

Jialu Fang,

Qinglei Xu, Limin Feng

et al.

Aquaculture, Journal Year: 2025, Volume and Issue: 599, P. 742125 - 742125

Published: Jan. 7, 2025

Language: Английский

Citations

0
Integrating genomic selection and genome-wide association studies to predict Streptococcus iniae resistance traits in Golden pompano (Trachinotus ovatus) DOI

Minmin Sun,

Xiangyuan Wang, Zhuoyu Wang

et al.

Aquaculture, Journal Year: 2025, Volume and Issue: unknown, P. 742174 - 742174

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning DOI Creative Commons
Junge Wang,

Jie Chai,

Li Chen

et al.

Animals, Journal Year: 2025, Volume and Issue: 15(4), P. 525 - 525

Published: Feb. 12, 2025

The increasing volume of genome sequencing data presents challenges for traditional genome-wide prediction methods in handling large datasets. Machine learning (ML) techniques, which can process high-dimensional data, offer promising solutions. This study aimed to find a method local pig breeds, using 10 datasets with varying SNP densities derived from imputed 515 Rongchang pigs and the Pig QTL database. Three reproduction traits—litter weight, total number piglets born, born alive—were predicted six five ML methods, including kernel ridge regression, random forest, Gradient Boosting Decision Tree (GBDT), Light Machine, Adaboost. methods’ efficacy was evaluated fivefold cross-validation independent tests. predictive performance both initially increased density, peaking at 800–900 k SNPs. outperformed ones, showing improvements 0.4–4.1%. integration GWAS database enhanced robustness. models exhibited superior generalizability, high correlation coefficients (0.935–0.998) between test results. GBDT forest showed computational efficiency, making them genomic livestock breeding.

Language: Английский

Citations

0
Deep learning for genomic selection of aquatic animals DOI
Yangfan Wang, Ping Ni, Marc Sturrock

et al.

Marine Life Science & Technology, Journal Year: 2024, Volume and Issue: 6(4), P. 631 - 650

Published: Sept. 27, 2024

Language: Английский

Citations

3
Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp DOI
Z. David Luo, Yang Yu,

Zhenning Bao

et al.

Aquaculture, Journal Year: 2023, Volume and Issue: 581, P. 740376 - 740376

Published: Nov. 15, 2023

Language: Английский

Citations

5
Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review DOI Creative Commons

A. Lee,

Melissa Yuin Mern Foong,

Beng Kah Song

et al.

Molecular Breeding, Journal Year: 2024, Volume and Issue: 44(9)

Published: Sept. 1, 2024

Language: Английский

Citations

1
Genome‐wide association study and genomic prediction for growth traits in spotted sea bass (Lateolabrax maculatus) using insertion and deletion markers DOI Creative Commons
Chong Zhang,

Yonghang Zhang,

Cong Liu

et al.

Animal Research and One Health, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Abstract Spotted sea bass ( Lateolabrax maculatus ) is a species of significant economic importance in aquaculture. However, genetic degeneration, such as declining growth performance, has severely impeded industry development, necessitating urgent improvement. Here, we conducted genome‐wide association study (GWAS) and genomic prediction for traits using insertion deletion (InDel) markers, systematically compared the results with our previous studies single nucleotide polymorphism (SNP) markers. A total 97 InDels including 6 bp an exon region were identified. It worth noting that only 5 1 candidate genes DY TS populations also detected GWAS SNPs, numerous novel c4b , fgf4 dnajb9 identified vital genes. Moreover, several growth‐related procedures, development bone muscle, detected. These findings indicated InDel‐based can provide valuable complement to SNP‐based studies. The comparison predictive performance length trait under different marker selection strategies models strategy exhibits more stable evenly strategy. Additionally, support vector machine model demonstrated better accuracy efficiency than traditional best linear unbiased Bayes models. Furthermore, superior InDel markers SNP highlighted potential enhance efficiency. Our carry implications dissecting mechanisms contributing improvement spotted through resources.

Language: Английский

Citations

1
Breeding evaluations in aquaculture using neural networks DOI Creative Commons
Christos Palaiokostas

Aquaculture Reports, Journal Year: 2024, Volume and Issue: 39, P. 102468 - 102468

Published: Nov. 15, 2024

Language: Английский

Citations

1