6533b85ffe1ef96bd12c119e
RESEARCH PRODUCT
Taxonomic Classification for Living Organisms Using Convolutional Neural Networks
Alaa Eddin AlchalabiNayel Al-zubiMohammed ElsharnobySaed KhawaldehUsama PervaizUsama Pervaizsubject
0301 basic medicinelcsh:QH426-470Taxonomic classificationADNCodificació Teoria de laBiologyBioinformaticsMachine learningcomputer.software_genreDNA; genes; taxonomic classification; convolutional neural networks; encodingConvolutional neural networkArticle03 medical and health sciences0302 clinical medicineBiologia -- ClassificacióEncoding (memory)convolutional neural networksGeneticstaxonomic classificationSensitivity (control systems)genesGenetics (clinical)ta113Biology -- Classificationbusiness.industryBiological classificationCoding theoryDNAencodinglcsh:Genetics030104 developmental biologyGenes030220 oncology & carcinogenesisEncodingConvolutional neural networksArtificial intelligenceCoding theorybusinesscomputerGensdescription
Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis. Peer reviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-11-17 |