{"title":"通过基于异构体表达排序的LSTM模型进行癌症诊断","authors":"Óscar Reyes, Eduardo Pérez","doi":"10.1145/3625237","DOIUrl":null,"url":null,"abstract":"<p>The known set of genetic factors involved in the development of several types of cancer has considerably been expanded, thus easing to devise and implement better therapeutic strategies. The automatic diagnosis of cancer, however, remains as a complex task because of the high heterogeneity of tumors and the biological variability between samples. In this work, a long short-term memory network-based model is proposed for diagnosing cancer from transcript-base data. An efficient method that transforms data into gene/isoform expression-based rankings was formulated, allowing us to directly embed important information in the relative order of the elements of a ranking that can subsequently ease the classification of samples. The proposed predictive model leverages the power of deep recurrent neural networks, being able to learn existing patterns on the individual rankings of isoforms describing each sample of the population. To evaluate the suitability of the proposal, an extensive experimental study was conducted on 17 transcript-based datasets, and the results showed the effectiveness of this novel approach and also indicated the gene/isoforms expression-based rankings contained valuable information that can lead to a more effective cancer diagnosis.</p>","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"80 1","pages":""},"PeriodicalIF":7.2000,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performing Cancer Diagnosis via an Isoform Expression Ranking-based LSTM Model\",\"authors\":\"Óscar Reyes, Eduardo Pérez\",\"doi\":\"10.1145/3625237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The known set of genetic factors involved in the development of several types of cancer has considerably been expanded, thus easing to devise and implement better therapeutic strategies. The automatic diagnosis of cancer, however, remains as a complex task because of the high heterogeneity of tumors and the biological variability between samples. In this work, a long short-term memory network-based model is proposed for diagnosing cancer from transcript-base data. An efficient method that transforms data into gene/isoform expression-based rankings was formulated, allowing us to directly embed important information in the relative order of the elements of a ranking that can subsequently ease the classification of samples. The proposed predictive model leverages the power of deep recurrent neural networks, being able to learn existing patterns on the individual rankings of isoforms describing each sample of the population. To evaluate the suitability of the proposal, an extensive experimental study was conducted on 17 transcript-based datasets, and the results showed the effectiveness of this novel approach and also indicated the gene/isoforms expression-based rankings contained valuable information that can lead to a more effective cancer diagnosis.</p>\",\"PeriodicalId\":48967,\"journal\":{\"name\":\"ACM Transactions on Intelligent Systems and Technology\",\"volume\":\"80 1\",\"pages\":\"\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2023-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Intelligent Systems and Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3625237\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Intelligent Systems and Technology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3625237","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Performing Cancer Diagnosis via an Isoform Expression Ranking-based LSTM Model
The known set of genetic factors involved in the development of several types of cancer has considerably been expanded, thus easing to devise and implement better therapeutic strategies. The automatic diagnosis of cancer, however, remains as a complex task because of the high heterogeneity of tumors and the biological variability between samples. In this work, a long short-term memory network-based model is proposed for diagnosing cancer from transcript-base data. An efficient method that transforms data into gene/isoform expression-based rankings was formulated, allowing us to directly embed important information in the relative order of the elements of a ranking that can subsequently ease the classification of samples. The proposed predictive model leverages the power of deep recurrent neural networks, being able to learn existing patterns on the individual rankings of isoforms describing each sample of the population. To evaluate the suitability of the proposal, an extensive experimental study was conducted on 17 transcript-based datasets, and the results showed the effectiveness of this novel approach and also indicated the gene/isoforms expression-based rankings contained valuable information that can lead to a more effective cancer diagnosis.
期刊介绍:
ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world.
ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.