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An Effective Integrated Machine Learning Framework for Identifying Severity of Tomato Yellow Leaf Curl Virus and Their Experimental Validation

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Research 2023年第3期2023卷 91-104页

作者： Nattanong Bupi Vinoth Kumar Sangaraju Le Thi Phan aamir lal Thuy Thi Bich Vo Phuong Thi Ho Muhammad Amir Qureshi Marjia Tabassum Sukchan Lee Balachandran Manavalan Department of Integrative Biotechnology College of Biotechnology and BioengineeringSungkyunkwan UniversitySuwon 16419.Gyeonggi-doRepublic of Korea Computational Biology and Bioinformatics Laboratory Department of Integrative BiotechnologyCollege of Biotechnology and Bioengineering.Sungkyunkwan UniversitySuwon 16419Gyeonggi-doRepublic of Korea

Tomato yellow leaf curl virus(TYLCV)dispersed across different countries,specifically to subtropical regions,associated with more severe *** TYLCV was first isolated in 1931,it has been a menace to tomato industrial production worldwide over the past *** groups were newly isolated from TYLCV-resistant tomatoes in 2022;however,their functions are *** development of machine learning(ML)-based models using characterized sequences and evaluating blind predictions is one of the major challenges in interdisciplinary *** purpose of this study was to develop an integrated computational framework for the accurate identification of symptoms(mild or severe)based on TYLCV sequences(isolated in Korea).For the development of the framework,we first extracted 11 different feature encodings and hybrid features from the training data and then explored 8 different classifiers and developed their respective prediction models by using randomized 10-fold ***,we carried out a systematic evaluation of these 96 developed models and selected the top 90 models,whose predicted class labels were combined and considered as reduced *** the basis of these features,a multilayer perceptron was applied and developed the final prediction model(IML-TYLCVs).We conducted blind prediction on 3 groups using IML-TYLCVs,and the results indicated that 2 groups were severe and 1 group was ***,we confirmed the prediction with virus-challenging experiments of tomato plant phenotypes using infectious clones from 3 *** virologists and plant breeding professionals can access the user-friendly online IML-TYLCVs web server at https://***/IML-TYLCVs,which can guide them in developing new protection strategies for newly emerging viruses.

关键词： breeding fir prediction

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Non-alcoholic fatty liver disease development:A multifactorial pathogenic phenomena

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Liver Research 2022年第2期6卷 72-83页

作者： aamir Bashir Ajay Duseja Arka De Manu Mehta Pramil Tiwari Department of Pharmacy Practice National Institute of Pharmaceutical Education and ResearchS.A.S.NagarMohaliPunjabIndia Department of Hepatology Post Graduate Institute of Medical Education and ResearchChandigarhIndia

Non-alcoholic fatty liver disease(NAFLD),characterized by the accumulation of excessive intrahepatic fat,is a leading metabolic disorder also considered as the hepatic manifestation of metabolic syndrome(MS).Though more commonly observed in obese individuals and those with metabolic risk factors,it also develops in a considerable number of non-obese individuals as well as participants without having any component of *** basic mechanism involved in the development of fatty liver is the imbalance between lipid uptake,synthesis,and metabolism in the liver,normally controlled by several mechanisms to maintain lipid *** a complex progressive liver disorder,the NAFLD pathogenesis is multifactorial,and several new pathogenic phenomena were discovered over *** available literature suggests the role of both genetic and environmental factors and associated metabolic factors;however,the mechanism of progression is not completely *** this review,we discuss different pathogenic mechanisms and their interplay to provide an elaborate idea regarding NAFLD development and *** understanding of pathogenic mechanisms will be useful in finding new treatment for patients with NAFLD.

关键词： Metabolic syndrome(MS) Non-alcoholic fatty liver disease(NAFLD) Non-alcoholic steatohepatitis(NASH) Fatty liver Steatohepatitis Insulin resistance(IR) Lysosomal acid lipase(lal)

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