Yuan Kexin, Chen Xuexinyi, Zhu Xinru, Li Yun, Wang Junlu, Li Tianzi
{"title":"Bacterial signatures for diagnosis of colorectal cancer by machine learning","authors":"Yuan Kexin, Chen Xuexinyi, Zhu Xinru, Li Yun, Wang Junlu, Li Tianzi","doi":"10.61603/ceas.v1i1.6","DOIUrl":"https://doi.org/10.61603/ceas.v1i1.6","url":null,"abstract":"Invasive methods such as colonoscopy are more commonly used in colorectal cancer (CRC) screening and diagnosis, but these methods are not easily accepted and have limitations. In this paper, we aim to exploit the close relationship between intestinal flora and the development of CRC. A T-test was used to screen and compare the intestinal flora of healthy individuals and patients, and strains with significant differences were selected as characteristic ones. In addition, three AI learning models, Random forest (RF), K-Nearest Neighbor (KNN), and Back propagation neural network (BPNN), were used to build a colorectal cancer diagnosis model based on intestinal flora. Overall, the investigation carried out by us has revealed six highly divergent species between healthy individuals and patients from t-tests and key species associated with CRC. The results were validated against each other, confirming the reliability of the obtained key strains, and providing a new idea for the clinical diagnosis of CRC.","PeriodicalId":491421,"journal":{"name":"Cambridge Explorations in Arts and Sciences","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136368453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zheng Yiting, Huang Menghan, Gong Zixin, Li Rui, Guo Yifeiyang, Gan Haiting
{"title":"Machine Learning Application in cfDNA Analysis to Achieve Tumour Assessment","authors":"Zheng Yiting, Huang Menghan, Gong Zixin, Li Rui, Guo Yifeiyang, Gan Haiting","doi":"10.61603/ceas.v1i1.5","DOIUrl":"https://doi.org/10.61603/ceas.v1i1.5","url":null,"abstract":"Breast cancer (BC) is the leading cause of cancer in women and the second leading cause of cancer-related death. Early and accurate screening of BC is a promising way of reducing the proportion of patients with advanced stages of BC. In recent years, the non-invasive test of tumour diagnosis by assessing the level of Plasma cell-free DNA (cfDNA) has become a research hotspot. Here, we demonstrate the use of random forest models to predict BC by evaluating the levels of 26 known breast cancer-related cfDNA methylation molecular markers (model-tested accuracy of 67.88%). Then, we improved the accuracy of the model to 71.52% by parameter optimization. In addition, considering that the diagnosis of BC is closely related to the health of every female, we have extended the project from scientific research to social investigation by carrying out a sample survey of Chinese college students to understand various perspectives on the application of artificial intelligence in the diagnosis of diseases. We found that the response was rather optimistic, while some participants showed concerns about the maturity of the technology and the disclosure of privacy. Therefore, future research should focus on the optimisation of the machine learning model, so as to effectively improve the accuracy of diagnosis and provide better pre-service for the population at risk of cancer.","PeriodicalId":491421,"journal":{"name":"Cambridge Explorations in Arts and Sciences","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136370093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}