{"title":"用机器学习从氨基酸序列中识别蛋白质紊乱","authors":"Shrinath Iyer","doi":"10.1109/EIT51626.2021.9491847","DOIUrl":null,"url":null,"abstract":"Intrinsic protein disorder can predict a whole host of neurodegenerative diseases like Alzheimer's. Predicting protein disorder itself is best undertaken using computational methods. In this paper, a novel approach to predicting protein disorder using a Convolutional Neural Network (CNN) algorithm. The algorithm had found a 92% auc-roc score, which indicates the performance of a binary classification model. To extract features, the model had used OneHotEncoding, a technique that converts the sequential data into numerical values that is fed into the model. The data was also gathered from a variety of sources including the Protein Data Bank (PDB), The Disordered Protein Database (Disprot), and the Swiss protein database (Swissprot) [1]-[3]. This paper has a unique approach in the model built and distinguished itself from prior models based on the structure of the model and feature extraction that was performed. Using a tensorflow framework, the model used multiple convolutional layers of varying filter length and a final dense layer to enable the model to learn the features and predict associated outputs. Furthermore, the output from the initial stages was fed into a binary cross entropy classifier that gave the resulting judgement of order or disorder.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"305 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identify protein disorder from amino acid sequences with Machine learning\",\"authors\":\"Shrinath Iyer\",\"doi\":\"10.1109/EIT51626.2021.9491847\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Intrinsic protein disorder can predict a whole host of neurodegenerative diseases like Alzheimer's. Predicting protein disorder itself is best undertaken using computational methods. In this paper, a novel approach to predicting protein disorder using a Convolutional Neural Network (CNN) algorithm. The algorithm had found a 92% auc-roc score, which indicates the performance of a binary classification model. To extract features, the model had used OneHotEncoding, a technique that converts the sequential data into numerical values that is fed into the model. The data was also gathered from a variety of sources including the Protein Data Bank (PDB), The Disordered Protein Database (Disprot), and the Swiss protein database (Swissprot) [1]-[3]. This paper has a unique approach in the model built and distinguished itself from prior models based on the structure of the model and feature extraction that was performed. Using a tensorflow framework, the model used multiple convolutional layers of varying filter length and a final dense layer to enable the model to learn the features and predict associated outputs. Furthermore, the output from the initial stages was fed into a binary cross entropy classifier that gave the resulting judgement of order or disorder.\",\"PeriodicalId\":162816,\"journal\":{\"name\":\"2021 IEEE International Conference on Electro Information Technology (EIT)\",\"volume\":\"305 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Electro Information Technology (EIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EIT51626.2021.9491847\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Electro Information Technology (EIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EIT51626.2021.9491847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identify protein disorder from amino acid sequences with Machine learning
Intrinsic protein disorder can predict a whole host of neurodegenerative diseases like Alzheimer's. Predicting protein disorder itself is best undertaken using computational methods. In this paper, a novel approach to predicting protein disorder using a Convolutional Neural Network (CNN) algorithm. The algorithm had found a 92% auc-roc score, which indicates the performance of a binary classification model. To extract features, the model had used OneHotEncoding, a technique that converts the sequential data into numerical values that is fed into the model. The data was also gathered from a variety of sources including the Protein Data Bank (PDB), The Disordered Protein Database (Disprot), and the Swiss protein database (Swissprot) [1]-[3]. This paper has a unique approach in the model built and distinguished itself from prior models based on the structure of the model and feature extraction that was performed. Using a tensorflow framework, the model used multiple convolutional layers of varying filter length and a final dense layer to enable the model to learn the features and predict associated outputs. Furthermore, the output from the initial stages was fed into a binary cross entropy classifier that gave the resulting judgement of order or disorder.