计算生物学方法预测癌症分子机制

Ansari Vikhar Danish Ahmad, Subur W. Khan, Qazi Yasar, Mohd Sayeed Shaikh, Mohd Mukhtar Khan
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引用次数: 0

摘要

生物学领域为研究复杂的分子相互作用和分子之间的结合强度提供了一个完整的结构,但重点是开发治疗方法和发现医学研究中的标记物,这些标记物有望通过精确定位对肿瘤发展生长至关重要的关键信号和途径,有效地靶向特定疾病,如癌症亚型。此外,网络分析作为一种强有力的工具,可以预测小分子如何与与癌症相关的蛋白质相互作用,并确定有希望的新疗法。这种有条理的策略可以通过评估药物与致癌靶点的有效结合能力来评估潜在药物,从而提高治疗的准确性。”此外,将机器学习方法与多数据集分析相结合,大大增强了对癌症相关分子连接的彻底检查,最终简化了药物开发和生物标志物的发现。这强调了分子对接在预测相互作用中的重要性,在癌症生物信息学领域内,药物和它们的靶点之间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational biology approach to predict molecular mechanism in cancer
The field of biology offers a complete structure for studying intricate molecular interactions and the strength of bonding between molecules but with an emphasis on developing treatments and discovering markers in medicine research that hold promise for targeting specific diseases like cancer subtypes effectively by pinpointing crucial signals and pathways crucial for tumor development growth, alongside network analysis as a potent tool to foresee how small molecules interact with proteins linked to cancer and determine promising new treatments. This methodical strategy enables the evaluation of potential medications by assessing their capacity to bind effectively to cancer causing targets for enhanced treatment accuracy." Additionally integrating machine learning methods with multi dataset analyses greatly enhances the thorough examination of cancer associated molecular connections ultimately streamlining drug development and biomarker discovery. This underscores the importance of molecular docking in forecasting interactions, between drugs and their targets within the realm of cancer bioinformatics.
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