肝脏疾病的生物信息学方法:没有实验验证的不完整

Gaur Pratibha
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引用次数: 0

摘要

在寻找肝脏疾病的治疗方法时,经常使用多种方法,如生物信息学和基于算法的系统生物学,用于筛选已经发表的数据,从而在分子水平上确定有希望的候选药物。这些研究提供了大量的数据信息,并声称确定了许多基因,这些基因可能成为治疗的目标。然而,重要的是要注意,已确定的化合物只是预测,这些建议的分子或基因的潜力应该通过采用稳健和关键的实验方法来充分证实,以满足所有可能的方面,安全地用于治疗目的。因此,我提出的观点是,这些预测的标记,无论是预后,诊断,或分子必须通过一个明确定义的实验管道进行批判性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bio-informatics approaches in liver disease: Incomplete without experimental validation
In the search for the treatment of hepatic diseases, multiple approaches have been used frequently such as bioinformatics and algorithm-based systems biology for the screening of already published data that leads to the identification of promising drug candidates at molecular levels. These studies provide a large data set of information and claim to identify many genes, which could be targeted for the cure. However, it is important to note that the identified compounds are only predictions and the potential of these suggested molecules or genes should be well confirmed by adopting robust and critical experimental approaches to satisfy all possible aspects to use them safely for therapeutic purposes. Therefore, I put forward the opinion that these predicted markers whether prognostic, diagnostic, or molecular must be evaluated critically through a well-defined experimental pipeline.
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