Literature DB >> 34161210

Recent Progress of Machine Learning in Gene Therapy.

Cassandra Hunt1, Sandra Montgomery2, Joshua William Berkenpas1, Noel Sigafoos1, John Christian Oakley1, Jacob Espinosa3, Nicola Justice3, Kiyomi Kishaba4, Kyle Hippe1, Dong Si5, Jie Hou6, Hui Ding7, Renzhi Cao1.   

Abstract

With new developments in biomedical technology, it is now a viable therapeutic treatment to alter genes with techniques like CRISPR. At the same time, it is increasingly cheaper to perform whole genome sequencing, resulting in rapid advancement in gene therapy and editing in precision medicine. Understanding the current industry and academic applications of gene therapy provides an important backdrop to future scientific developments. Additionally, machine learning and artificial intelligence techniques allow for the reduction of time and money spent in the development of new gene therapy products and techniques. In this paper, we survey the current progress of gene therapy treatments for several diseases and explore machine learning applications in gene therapy. We also discuss the ethical implications of gene therapy and the use of machine learning in precision medicine. Machine learning and gene therapy are both topics gaining popularity in various publications, and we conclude that there is still room for continued research and application of machine learning techniques in the gene therapy field. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  CRISPR; Machine learning; cancer; cardiovascular disease; ethics; gene therapy; hemophilia; neurodegenerative disease

Mesh:

Year:  2022        PMID: 34161210     DOI: 10.2174/1566523221666210622164133

Source DB:  PubMed          Journal:  Curr Gene Ther        ISSN: 1566-5232            Impact factor:   4.391


  2 in total

1.  Ultrasound Image Classification of Thyroid Nodules Based on Deep Learning.

Authors:  Jingya Yang; Xiaoli Shi; Bing Wang; Wenjing Qiu; Geng Tian; Xudong Wang; Peizhen Wang; Jiasheng Yang
Journal:  Front Oncol       Date:  2022-07-15       Impact factor: 5.738

2.  Predicting recurrence and metastasis risk of endometrial carcinoma via prognostic signatures identified from multi-omics data.

Authors:  Ling Li; Wenjing Qiu; Liang Lin; Jinyang Liu; Xiaoli Shi; Yi Shi
Journal:  Front Oncol       Date:  2022-08-19       Impact factor: 5.738

  2 in total

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