首页|Studies from Indian Council of Agricultural Research (ICAR) Research Complex Update Current Data on Machine Learning (An Advanced Approach for Predicting Selective Sweep In the Genomic Regions Using Machine Learning Techniques)
Studies from Indian Council of Agricultural Research (ICAR) Research Complex Update Current Data on Machine Learning (An Advanced Approach for Predicting Selective Sweep In the Genomic Regions Using Machine Learning Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting from New Delhi, India, by NewsRx journalists, research stated, “Selective sweep is an important phe- nomenon in the aspect of natural selection. It plays significant role in adaptability as well as survival of species, including crop cultivars.” Financial supporters for this research include Indian Council of Agricultural Research (ICAR), ICAR- Junior Research Fellowship. The news correspondents obtained a quote from the research from the Indian Council of Agricultural Research (ICAR) Research Complex, “Various existing approaches for selective sweep analysis are mostly built on traditional rule base approach that lack the advanced approaches such as machine learning and deep learning and often result in poor prediction accuracy. In this study a new method or model for the prediction of selective sweep has been presented. This method has been initiated with simulation, preceded through feature extraction and selection and finally fed to different machine learning algorithms. Here eight different machine learning based methods have been implemented-(1) Support Vector Machine (SVM), (2) Regression Tree, (3) Random Forest, (4) Naive Bayes, (5) Multiple logistic regression, (6) K-Nearest Neighbor (KNN), (7) Gradient boosting and (8) Artificial Neural Network (ANN) and results of their comparative evaluations are presented. It has been observed that random forest model outperformed to its counterparts in terms of evaluation matrices with an area under the ROC (Receiver Operating Characteristic) curve (AUC) score of 0.8448 as well as 1st rank in TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) analysis. Further, a robust model for selective sweep prediction based upon random forest has been developed. Model developed in the current study has outperformed to other existing approaches for prediction and analysis of selective sweep.”
New DelhiIndiaAsiaCyborgsEmerging TechnologiesGe- neticsMachine LearningIndian Council of Agricultural Research (ICAR) Research Complex
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NSTL
Springer Nature
