Classification of Cancerous Profiles using Machine Learning

被引：10
|
作者：
Sharma, Aman [1 ]
Rani, Rinkle [1 ]
机构：
[1] Thapar Univ, Dept Comp Sc & Engn, Patiala, Punjab, India
来源：
2017 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND DATA SCIENCE (MLDS 2017) | 2017年
关键词：
Cancer; Clustering; Machine Learning; Genes; Drug Prediction; GENE-EXPRESSION DATA; PREDICTION;
D O I：
10.1109/MLDS.2017.6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
There are 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 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 anticancer 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.
引用
收藏
页码：31 / 36
页数：6
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