Classification of Cancerous Profiles Using Machine Learning

Aman Sharma, Rinkle Rani
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引用次数: 19

Abstract

There are a variety of options available for cancer treatment. The type of treatment recommended for an individual is influenced by various factors such as cancer-type, the severity of a cancer (stage) and most important the genetic heterogeneity. In such a complex environment, the targeted drug treatments are likely to be irresponsive or respond differently. To study anti-cancer drug response we need to understand cancerous profiles. These cancerous profiles carry information which can reveal the underlying factors responsible for cancer growth. Hence, there is need to analyze cancer data for predicting optimal treatment options. Analysis of such profiles can help to predict and discover potential drug targets and drugs. In this paper the main aim is to provide machine learning based classification technique for cancerous profiles.
使用机器学习对癌症特征进行分类
癌症治疗有多种选择。个人推荐的治疗类型受各种因素的影响,如癌症类型、癌症的严重程度(阶段),最重要的是遗传异质性。在这样一个复杂的环境中,靶向药物治疗很可能没有反应或反应不同。为了研究抗癌药物的反应,我们需要了解癌症的概况。这些癌症档案携带的信息可以揭示导致癌症生长的潜在因素。因此,有必要分析癌症数据以预测最佳治疗方案。分析这些特征可以帮助预测和发现潜在的药物靶点和药物。本文的主要目的是提供基于机器学习的癌症特征分类技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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