Feature-Based Machine Intelligent Mapping of Cancer Beating Molecules
Keywords:
Anti-cancer, Prediction Model, Stacked Ensemble Neural networks, Machine learning, OncologyAbstract
Despite the fact that cancer has been treated using a variety of surgical and therapeutic techniques such as chemotherapy, target therapy, immunotherapy, and hormone therapy, understanding cancer cell biology, and cancer metastatic mechanisms are critical. According to recent studies, up to 30–40% of cancers can be avoided simply by changing one's diet and lifestyle. Diet and nutritional factors are vital in the prevention of a variety of diseases, and they have a substantial impact on patients' disease outcomes both during and after therapy. In this,the mechanical response of food particles plays a major role to modulate muscle activity and the dielectric properties of lossy materials are affected by frequency, temperature, and material composition. Hence a unique, network machine learning approach is used for finding mechanical and electrical features that are proposed, that intern used for cancer-fighting. The groundwork models that planned to employ stacked ensemble learning methods. ElasticNet, pairwise support vector regression, Kernelized Bayesian Multitask Learning,and Neural networks use in this prediction. In terms of sensitivity and activity, the two signatures indicated cell lines and drugs. A sensitivity signature shows the changes in gene expression that cause cell death in a particular cell line. During the action, mechanical, electrical properties and expressions are taken to suppress cancer in the sensitive line. To improve the fitness analysis in the drug response,the cost function is calculated in this research.
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