{"title":"用遗传算法鉴定人类miRNA靶点","authors":"Kalle Karhu, S. Khuri, Juho Mäkinen, J. Tarhio","doi":"10.1145/1722024.1722059","DOIUrl":null,"url":null,"abstract":"MicroRNAs (miRNAs) play an important role in eukaryotic gene regulation. Although thousands of miRNAs have been identified in laboratories around the world, most of their targets still remain unknown. Different computational techniques exist to predict miRNA targets. In this article, we propose a new method for identifying human miRNA-mRNA interactions based on a genetic algorithm. Our cross-validation results indicate that the genetic algorithm-based miRNA target predictor outperforms the MiRanda package as evidenced by high true positive rates and moderate false positive rates.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"68 1","pages":"30"},"PeriodicalIF":0.0000,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722059","citationCount":"3","resultStr":"{\"title\":\"Identifying human miRNA targets with a genetic algorithm\",\"authors\":\"Kalle Karhu, S. Khuri, Juho Mäkinen, J. Tarhio\",\"doi\":\"10.1145/1722024.1722059\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"MicroRNAs (miRNAs) play an important role in eukaryotic gene regulation. Although thousands of miRNAs have been identified in laboratories around the world, most of their targets still remain unknown. Different computational techniques exist to predict miRNA targets. In this article, we propose a new method for identifying human miRNA-mRNA interactions based on a genetic algorithm. Our cross-validation results indicate that the genetic algorithm-based miRNA target predictor outperforms the MiRanda package as evidenced by high true positive rates and moderate false positive rates.\",\"PeriodicalId\":39379,\"journal\":{\"name\":\"In Silico Biology\",\"volume\":\"68 1\",\"pages\":\"30\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1145/1722024.1722059\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"In Silico Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1722024.1722059\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"In Silico Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1722024.1722059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
Identifying human miRNA targets with a genetic algorithm
MicroRNAs (miRNAs) play an important role in eukaryotic gene regulation. Although thousands of miRNAs have been identified in laboratories around the world, most of their targets still remain unknown. Different computational techniques exist to predict miRNA targets. In this article, we propose a new method for identifying human miRNA-mRNA interactions based on a genetic algorithm. Our cross-validation results indicate that the genetic algorithm-based miRNA target predictor outperforms the MiRanda package as evidenced by high true positive rates and moderate false positive rates.
In Silico BiologyComputer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍:
The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.