[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124483-en":3,"doc-seo-124483-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124483,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Prediction by Machine Learning Analysis of Genomic Data - Phenotypic Frost Tolerance in Perccottus glenii","Analyzing the genome sequence of Perccottus glenii, the only known fish species with freezing tolerance, is essential for understanding adaptation to extreme cold. Traditional bioinformatics approaches are slow and limited in accuracy as sequencing data scale and genome complexity increase. This study applies machine learning to identify differential genes, using Neodontobutis hainanens as a comparison group, and evaluates encoding strategies, multiple classifiers, and model interpretability to link key features to frost-tolerance phenotype.","Prediction by Machine Learning Analysis of Genomic Data Phenotypic Frost Tolerance in Perccottus glenii  \nLilin Fan1,2,3,􀀍 , Xuqing Chai1,2,3,􀀍 , Zhixiong Tian1(􀀍), Yihang Qiao1, Zhen Wang1, and  \nYifan Zhang1  \n1 School of Computer Information and Engineering, Henan Normal University, Xinxiang 453000, China  \n2 Henan Engineering Laboratory of Smart Business and Internet of Things Technology  \n3 Henan Normal University High Performance Computing Center  \n[2208283040@stu.htu.edu.cn](2208283040@stu.htu.edu.cn)  \nAbstract. Analyzing the genome sequence of the only fish species known to possess freezing tolerance, Perccottus glenii, is crucial for understanding how organisms adapt to extreme environments. Traditional biological analysis methods are time-consuming and have limited accuracy. Therefore, we will employ machine learning techniques to analyze the genomic sequences of Perccottus glenii, using Neodontobutis hainanens as a comparison group to identify differential genes.  \nFirstly, we propose five gene sequence vectorization methods and a method for handling super-long gene sequences, and we compare three vectorization methods: sequential encoding, One-Hot encoding, and K-mer encoding, to identify the  \noptimal encoding method. Secondly, we construct four classification models: random forest, LightBGM, XGBoost, and decision tree. The dataset used for these models is derived from the NCBI database. We determine the optimal Kvalue for the K-mer encoding method, and extract and vectorize the gene sequence matrix, with the best-performing model, random forest, achieving a classification accuracy of up to 99.98% . Finally, we use SHAP values to perform interpretability analysis on the classification models, and through tenfold crossvalidation and AUC metrics, we identify the top 10 features that contribute the most to the model’s classification accuracy, thereby recognizing the differential genes for freezing tolerance phenotype in Perccottus glenii. We validate the identified differential genes using the biological software BLAST. Conclusion: Our study demonstrates that machine learning methods can replace traditional manual methods for identifying differential genes associated with freezing tolerance phenotype in Perccottus glenii.  \nKeywords: genome sequence, machine learning, vectorization method, SHAP.  \n􀀍 These authors contributed equally to this work and should be considered co-first authors.  \n2 L. Fan et al.  \n1 Introduction  \nCurrently, Perccottus glenii is the only fish known to possess freezing tolerance, while its closely related species Neodontobutis hainanens does not have the ability to withstand cold temperatures. Analyzing the genomic sequences of these two species to identify the phenotypic genes that contribute to the cold tolerance of Perccottus glenii is of great significance for understanding how organisms adapt to extreme environments [1] .  \nThrough bioinformatics analysis of genomic differences between the two species, scientists can gain insights into the mechanisms underlying the cold tolerance of Perccottus glenii, providing valuable genetic resources for future research. However, bioinformatics analysis has its limitations. For instance, with the advancement of sequencing technologies [2,3], the volume of gene sequence data is continually increasing, and the analysis of these large datasets requires efficient computational power and storage capacity. Additionally, bioinformatics analysis has limited accuracy when it comes to the alignment of large or complex genomes. Overcoming these limitations presents a new challenge. Traditional experimental methods for bioinformatics analysis of the genomes of these two fish species are time-consuming and labor-intensive, and they are limited by the experience and skills of the experimenters. The manual operation involved in the analysis process can lead to errors and subjective judgments, resulting in limited accuracy for traditional bioinformatics methods in","cbCaifyftc332YLs","https://ap.wps.com/l/cbCaifyftc332YLs","pdf",3119476,1,18,"English","en",105,"# Abstract\n# Introduction\n## Motivation and challenges\n## Proposed approach and contributions","[{\"question\":\"Why is Perccottus glenii genomic analysis important for freezing tolerance research?\",\"answer\":\"Perccottus glenii is the only fish known to possess freezing tolerance, so identifying its phenotypic genes helps explain how organisms adapt to extreme cold environments.\"},{\"question\":\"What data and comparison group are used to identify differential genes?\",\"answer\":\"The dataset is derived from the NCBI database, and Neodontobutis hainanens is used as the comparison group to find differential genes associated with freezing tolerance.\"},{\"question\":\"How does the study determine which gene sequence encoding and classification models perform best?\",\"answer\":\"It compares five gene sequence vectorization methods and three encoding approaches (sequential, One-Hot, and K-mer), then trains four classifiers (random forest, LightBGM, XGBoost, decision tree). The best model, random forest, reaches up to 99.98% classification accuracy.\"}]","Prediction by Machine Learning Analysis of Genomic Data - Phenotypic Frost Tolerance in Perccottus glenii | PDF",1785822705,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-by-machine-learning-analysis-of-genomic-data-phenotypic-frost-tolerance-in-perccottus-glenii","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-by-machine-learning-analysis-of-genomic-data-phenotypic-frost-tolerance-in-perccottus-glenii/124483/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is Perccottus glenii genomic analysis important for freezing tolerance research?","Question",{"text":75,"@type":76},"Perccottus glenii is the only fish known to possess freezing tolerance, so identifying its phenotypic genes helps explain how organisms adapt to extreme cold environments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and comparison group are used to identify differential genes?",{"text":80,"@type":76},"The dataset is derived from the NCBI database, and Neodontobutis hainanens is used as the comparison group to find differential genes associated with freezing tolerance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study determine which gene sequence encoding and classification models perform best?",{"text":84,"@type":76},"It compares five gene sequence vectorization methods and three encoding approaches (sequential, One-Hot, and K-mer), then trains four classifiers (random forest, LightBGM, XGBoost, decision tree). 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