{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T12:17:50Z","timestamp":1655468270715},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T00:00:00Z","timestamp":1655164800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,14]]},"abstract":"<jats:p>Recent advances in the field of genomic trait prediction has paved the way for developing futuristic plant breeding programs. The objective of our study is to predict a single or multiple traits of rapeseed (Brassica napus) based on the RNA sequence data. We analyzed 12 different traits of rapeseed and evaluated how their pair-wise correlation impact on the yield production. Further, for predicting single or multi-traits of rapeseed, four state-of-art machine learning (ML) models, namely \u2013 Lasso Regression (Lasso), Random Forest (RF), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) were evaluated. For both single and multi-trait predictions, our RF and SVM models performed most consistently, where the lowest mean squared error was achieved by RF (0.045 and 0.016 for the single and multi-trait prediction respectively). A comparative analysis with related works showed the potentiality of our model for future multi-modal model development. Future study in this context could comprise of evaluating our models with other transcriptome dataset from related crops or deep learning-based methods for better outcomes.<\/jats:p>","DOI":"10.3233\/aise220031","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T07:22:08Z","timestamp":1655364128000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning to Predict Rapeseed Traits from RNA-seq Data"],"prefix":"10.3233","author":[{"given":"Md Ayshik","family":"Rahman Khan","sequence":"first","affiliation":[{"name":"La Trobe University, Melbourne, VIC 3083, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khandaker Asif","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, ACT 2601, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Zakir","family":"Hossain","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, ACT 2601, Australia"},{"name":"Biological Data Science Institute, College of Science, Australian National University, ACT 2601, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Ambient Intelligence and Smart Environments","Workshops at 18th International Conference on Intelligent Environments (IE2022)"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/AISE220031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T07:22:09Z","timestamp":1655364129000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/AISE220031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/aise220031","relation":{},"ISSN":["1875-4163","1875-4171"],"issn-type":[{"value":"1875-4163","type":"print"},{"value":"1875-4171","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,14]]}}}