计算机科学
优先次序
鉴定(生物学)
翻译(生物学)
机器学习
计算生物学
计算模型
精密医学
领域(数学)
人工智能
生物信息学
基因
生物
管理科学
遗传学
工程类
信使核糖核酸
植物
数学
纯数学
作者
Simone C. da Silva Rosa,Amir Barzegar Behrooz,Sofia Guedes,Rui Vitorino,Saeid Ghavami
标识
DOI:10.1080/14789450.2024.2337004
摘要
Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.
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