title: Resistance gene identification from Larimichthys crocea with machine learning techniques.
abstract: The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. It is meaningful to identify and predict R-gene of Larimichthys crocea (L.Crocea). It is friendly for breeding and the marine environment as well. Large amounts of L.Crocea's immune mechanisms have been explored by biological methods. However, much about them is still unclear. In order to break the limited understanding of the L.Crocea's immune mechanisms and to detect new R-gene and R-gene-like genes, this paper came up with a more useful combination prediction method, which is to extract and classify the feature of available genomic data by machine learning. The effectiveness of feature extraction and classification methods to identify potential novel R-gene was evaluated, and different statistical analyzes were utilized to explore the reliability of prediction method, which can help us further understand the immune mechanisms of L.Crocea against pathogens. In this paper, a webserver called LCRG-Pred is available at http://server.malab.cn/rg_lc/.
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China.|State Key Laboratory of Large Yellow Croaker Breeding, Ningde Fufa Fisheries Company Limited, Ningde, 352000, China.
authors.forename: Yinyin
authors.initials: Y
authors.lastname: Cai
authors.affiliation: Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
authors.forename: Zhijun
authors.initials: Z
authors.lastname: Liao
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ju
authors.affiliation: School of Aerospace Engineering, Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Juan
authors.initials: J
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Large Yellow Croaker Breeding, Ningde Fufa Fisheries Company Limited, Ningde, 352000, China.|College of Ocean and Earth Sciences, Xiamen University, Xiamen, 361102, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Mao
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China.|State Key Laboratory of Large Yellow Croaker Breeding, Ningde Fufa Fisheries Company Limited, Ningde, 352000, China.
authors.forename: Xiangrong
authors.initials: X
authors.lastname: Liu
mesh_terms: D000818:Animals; D023181:Antimicrobial Cationic Peptides / Q000235:genetics / Q000276:immunology; D027682:Cation Transport Proteins / Q000235:genetics / Q000276:immunology; D066264:Datasets as Topic; D060467:Disease Resistance / Q000235:genetics*; D005393:Fish Diseases / Q000235:genetics* / Q000276:immunology; D029941:Fish Proteins / Q000235:genetics* / Q000276:immunology; D005786:Gene Expression Regulation; D016678:Genome; D006649:Histocompatibility Antigens / Q000235:genetics / Q000276:immunology; D007113:Immunity, Innate; D007136:Immunoglobulins / Q000235:genetics / Q000378:metabolism; D007372:Interferons / Q000235:genetics / Q000276:immunology; D000069550:Machine Learning; D010473:Perciformes / Q000235:genetics* / Q000276:immunology; D000099173:Natural Resistance-associated Macrophage Protein 1
chemical_list: D023181:Antimicrobial Cationic Peptides; D027682:Cation Transport Proteins; D029941:Fish Proteins; D006649:Histocompatibility Antigens; D007136:Immunoglobulins; D007372:Interferons; D000099173:Natural Resistance-associated Macrophage Protein 1