Towards Precision Medicine for Cancer Patient Stratification by Classifying Cancer by Using Machine Learning
DOI:
https://doi.org/10.55662/JST.2022.3301Downloads
Keywords:
Precision Medicine, Cancer Patient, Machine LearningAbstract
On average, a drug or a treatment is effective in only about half of patients who take it. These patients need to try several until they find one that is effective at the cost of side effects associated with every treatment. The ultimate goal of precision medicine is to provide a treatment best suited for every individual. Sequencing technologies have now made genome means ics data available in abundance to be used towards this goal. In this project, we will specifically focus on cancer. Most cancer patients get a particular treatment based on the cancer type and the stage, though different individuals will react differently to a treatment. It is now well established that genetic mutations cause cancer growth and spreading and importantly, these mutations are different in individual patients. The aim of this project is to use genomic data to allow for better stratification of cancer patients, to predict the treatment most likely to work. Specifically, the project will use a machine learning approach to classify cancer and suggest medicine. The whole work is divided into two parts, one is predicting cancer using several machine learning classification techniques and then suggesting medicine.
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Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
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Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
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Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
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Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.