The Application of Artificial Intelligence to The Bayesian Model Algorithm for Combining Genome Data

Authors

  • Zepeng Shen
  • Kuo Wei
  • Hengyi Zang
  • Linxiao Li
  • Guanghui Wang

DOI:

https://doi.org/10.54097/ykhccb53

Keywords:

Bayesian hypothesis; Large-scale genomes; Artificial intelligence algorithm.

Abstract

 The combination of bioinformatics and artificial intelligence (AI) has made significant progress in the current phase. AI technology, especially deep learning, has been widely used in biology, resulting in many innovations. Currently, AI plays a key role in genomics, proteomics, and drug discovery. Deep learning models are used to predict protein structures, discover potential drug compounds, interpret genomic sequences, analyze medical images, and make personalized medical recommendations. In addition, AI can also help accelerate the processing and interpretation of biological big data, helping biologists to understand complex problems in the life sciences more deeply. Compared to traditional genetic data analysis methods, AI combined with bioinformatics methods are often faster and more accurate, and are capable of processing large-scale, high-dimensional biological data, opening up unprecedented opportunities for life science research. In this paper, the whole genome data and biomedical imaging data are used from the perspective of Bayesian hypothesis testing. Genome-wide association analysis, led by large-scale multiple tests, is a very popular tool for identifying genetic variation points in new complex diseases. In genome-wide association analysis, tens of thousands of SNPS need to be tested simultaneously to find out some SNPS related to traits. These tests are related due to factors such as linkage imbalances in the genetic process, and the test questions are set against the background of high-dimensional data (p >=n).

Downloads

Download data is not yet available.

References

WATTS DJSTROGATZ S H.Collective dynamicsofsmall-word networks [J]. Nature, 1998,(393):440-442.

BARABASI A L,ALBERT R.Emergence of scalingin random networks [J].Science,1999,(286):509-512.

BARABASI A L,ALBERT R,JEONG H,etal.Power-Law distribution of the World Wide Web [J] Science, 2000, 287 (5461):2115.

Gasser, Thomas, and Manu Sharma, "Computational Approaches for Understanding the Diagnosis and Treatment of Parkinson's Disease", Journal of Neurology, 2015.

Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu. Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10):36–41, 2023.DOI: 10.1021/acs.jctc.3c00031.

Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, and Change Che. Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10):42–46, 2023. https://doi.org/10.53469/jtpes.2023.03(10).06

Chang Che, Qunwei Lin, Xinyu Zhao, Jiaxin Huang, and Liqiang Yu. 2023. Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (ICBDT '23). Association for Computing Machinery, New York, NY, USA, 414–418. https://doi.org/10.1145/3627377.3627442

Y. Wang, K. Yang, W. Wan, Y. Zhang and Q. Liu, "Energy-Efficient Data and Energy Integrated Management Strategy for IoT Devices Based on RF Energy Harvesting," in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13640-13651, 1 Sept.1, 2021, doi: 10.1109/JIOT.2021.3068040.

Y. Wang, K. Yang, W. Wan, Y. Zhang and Q. Liu, "Energy-Efficient Data and Energy Integrated Management Strategy for IoT Devices Based on RF Energy Harvesting," in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13640-13651, 1 Sept.1, 2021, DOI: 10.1109/JIOT.2021.3068040.

Wang, Y, Yang, K, Wan, W, Mei, H. Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies. Trans Emerging Tel Tech. 2021; 32:e4122.DOI: https://doi.org/10.1002/ett.4122

Xu, J., Pan, L., Zeng, Q., Sun, W., & Wan, W. Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks. https://api.semanticscholar.org/Corpus

Yao, J., Zou, Y., Du, S., Wu, H., & Yuan, B. Progress in the Application of Artificial Intelligence in Ultrasound Diagnosis of Breast Cancer. DOI:https://api.semanticscholar.org/Corpus

Zhou Y, Chen S, Wu Y, Li L, Lou Q, Chen Y, Xu S. Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer. Front Oncol. 2023 May 10;13:1171837. DOI: 10.3389/fonc.2023.1171837. PMID: 37234992; PMCID: PMC10206143.

Li L, Xu C, Wu W, et al. Zero-resource knowledge-grounded dialogue generation[J]. Advances in Neural Information Processing Systems, 2020, 33: 8475-8485. DOI: https://doi.org/10.48550/arXiv.2008.12918

Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281–285.DOI: 10.1111/jgs.18617

Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278–280. https://doi.org/10.54097/ajst.v8i1.14333

Zheng Yang, Tien Tuan Anh Dinh, Chao Yin, Yingying Yao, Dianshi Yang, Xiaolin Chang, and Jianying Zhou. "LARP: A Lightweight Auto-Refreshing Pseudonym Protocol for V2X." Proceedings of the 27th ACM on Symposium on Access Control Models and Technologies, 2022, pp. 49-60. DOI:https://doi.org/10.1145/3532105.3535027

KWAK J G,LEE J. Thermoresponsiveinvertedcolloidal crystal hydrogel scaffolds for lymphoid tissueengineering [J]. Advanced Healthcare Materials ,2020.9( 6): 1901556:1-9.

ALBERT RBARABASI LStatistical mechanicsof complex networks[J].Reviews of Modern Physics,2002(74):47-97.

GIRVAN M,NEWMAN M E J.Community structure in social and biological networks[J].Proc of the National Academy of Science,2002,9(12):7821-7826.

Downloads

Published

28-12-2023

Issue

Section

Articles

How to Cite

Shen, Z., Wei, K., Zang, H., Li, L., & Wang, G. (2023). The Application of Artificial Intelligence to The Bayesian Model Algorithm for Combining Genome Data. Academic Journal of Science and Technology, 8(3), 132-135. https://doi.org/10.54097/ykhccb53