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Sequence-Based Predicting Bacterial Essential ncRNAs Algorithm by Machine Learning

Intelligent Automation & Soft Computing 2023年第6期36卷 2731-2741页

作者： Yuan-Nong Ye Ding-Fa Liang Abraham Alemayehu Labena Zhu Zeng Bioinformatics and Biomedical Big data Mining Laboratory Department of Medical InformaticsSchool of Big HealthGuizhou Medical UniversityGuiyang550025China Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical EngineeringSchool of Biology and EngineeringGuizhou Medical UniversityGuiyang550025China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of EducationGuizhou Medical UniversityGuiyang550025China College of Computational and Natural Sciences Dilla UniversityDilla419Ethiopia

Essential ncRNA is a type of ncRNAwhich is indispensable for the sur-vival of *** essential ncRNAs cannot encode proteins,they are as important as essential coding genes in *** have got wide variety of applications such as antimicrobial target discovery,minimal genome construction and evolution *** present,the number of species required for the deter-mination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming,laborious and *** addition,tra-ditional experimental methods are limited by the organisms as less than 1%of bacteria can be cultured in the ***,it is important and necessary to develop theories and methods for the recognition of essential non-coding *** this paper,we present a novel method for predicting essential ncRNA by using both compositional and derivative features calculated by information theory of ncRNA *** method was developed with Support Vector Machine(SVM).The accuracy of the method was evaluated through cross-species cross-vali-dation and found to be between 0.69 and *** shows that the features we selected have good performance for the prediction of essential ncRNA using ***,the method can be applied for discovering essential ncRNAs in bacteria.

关键词： Bioinformatics biological information theory biomedical informatics

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Integrated Helicobacter Pylori Genome database and Its Analysis 11th

Integrated Helicobacter Pylori Genome Database and Its Analy...

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Zheng, Liufeng Guo, Mujuan Liang, Dingfa Ye, Yuannong Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

The shape of helicobacter pylori (HP) is usually helical or S-shaped, which makes it to have the feature of highly divergent. Besides, its distribution also is different greatly in space and time, which is the factor to lead to multiple infection. In addition, in the same infected person, multiple infection also appeared in different regions of strain gene infection origin. Therefore, it is of great significance to study the genomic diversity and evolutionary analysis of Helicobacter pylori. In this paper, we are to analyze the homology of multiple sequences using ClustalX1.83 and meanwhile to construct the homologous genome evolutionary tree by MEGA7.0 to analyze the its function. The purpose of this study is to compare the genomic evolutionary differences of Helicobacter pylori among populations in different regions, and to discuss the significance of its infection prevalence and genomic variation in geographical changes, so as to conduct further discussion on the molecular level research mechanism of Helicobacter pylori. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genome

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Optimization of Gene Translation Using SD Complementary Sequences and Double Codons 11th

Optimization of Gene Translation Using SD Complementary Sequ...

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Liang, Dingfa Huang, Zhumian Zheng, Liufeng Ye, Yuannong Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

Studies have confirmed that the growth, development and maintenance structure and function of all living organisms are determined by their genes, but the speed of gene translation products is affected by various internal and external factors. Therefore, studying the gene translation speed is extremely important for production and improving the speed through a variety of ways has always been an important subject. In recent years, the found of codon usage bias, tRNA recycling model and SD sequence-like structure provide another way to optimize the translation efficiency. These methods are almost based on codon usage theory to optimize gene sequence to speed up or slow down the translation speed so as to regulate the cell expression and metabolism. This paper is combined the three theories above, aimed at *** to design some reasonable algorithm to optimize gene sequence and thus using codon adaptation index (CAI) to evaluate the optimized effect. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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The Algorithms of Predicting Bacterial Essential Genes and NcRNAs by Machine Learning 11th

The Algorithms of Predicting Bacterial Essential Genes and N...

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Ye, Yuannong Liang, Dingfa Zeng, Zhu Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

Essential genes are indispensable for biological survival. Thus it is of great significance to identify and study essential genes. A machine learning method, K-Nearest Neighbor, is used for development of predicting essential bacterial genes. The homologous features, including sequence homology and functional homology, of the bacterial genomes are extracted for determining essential genes. Based on the features, we use K-Nearest Neighbor algorithm for determining of gene function. And we tune the minimum matching parameter (K) in the essential gene predicted model for building an optimal model of the Escherichia coli specificity model. The corresponding optimal parameter (K) is then extended to other bacterial essential genes predicting models. After cross validation, the highest accuracy is 0.89 while K between 5 and 7. Therefore, the features we extracted can increase the accuracy of the bacterial essential gene prediction. In the premise, we found that the prediction accuracy of the prediction model based on K-Nearest Neighbor was not significantly different in different evolutionary distances between organisms in the database and the investigated species. That means the machine learning model can be extended to more distant species. It wills have a better predictive performance for predicting essential genes of distant species than the usual sequence-based methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Escherichia coli

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The Algorithms for Word Segmentation and Named Entity Recognition of Chinese Medical Records 7th

The Algorithms for Word Segmentation and Named Entity Recogn...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Ye, Yuan-Nong Zheng, Liu-Feng Huang, Meng-Ya Liu, Tao Zeng, Zhu Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9783030786144

A complete inpatient electronic medical record contains a lot of information. In recent years, numerous researchers have carried out research on word segmentation of medical texts. Since the medical record text is written by free text, structuring the medical record text is an important part of the medical record intelligent analysis when these medical named entities are recognized. At present, named entity recognition methods are mainly divided into lexicographical and rule-based methods and machine learning-based methods. The machine learn-based approach takes the named entity recognition task as the annotation problem of sequence data, mainly considering the context information. The features commonly used in feature construction are contextual feature and dictionary features. BERT+LSTM+CRF were used to train the named entity recognition model. Open source CRF++ was adopted as the tool we relied on. We trained the LSTM+F model using the results of the original word segmentation and the information in the context as features. We carried out a 5-fold cross validation. The results showed that the overall F-1 score (MICRO-F) of named entity recognition reached 0.92, which confirmed that the model could accurately complete the task of medical named entity recognition. © 2021, Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Multimodal SVM Classification for Early-stage Alzheimer's Disease Diagnosis Using T1-weighted MR and F-18 FDG PET Imaging

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Journal of Medical Imaging and Radiation Sciences 2024年第3期55卷 101539-101539页

作者： Pamarapa, Mr. Chayanon Keerativittayayut, Dr. Ruedeerat Ekjeen, Dr. Tawatchai Shoombuatong, Dr. Watshara Vichianin, Dr. Yudthaphon School of Radiological Technology Faculty of Health Science Technology Chulabhorn Royal Academy Bangkok Thailand Department of Radiological Technology Faculty of Medical Technology Mahidol University Bangkok Thailand Center of Data mining and Biomedical Informatics Faculty of Medical Technology Mahidol University Bangkok Thailand

Alzheimer's disease is a significant global health challenge characterized by progressive brain degeneration associated with aging. Early detection is crucial for improved prognosis and treatment outcomes. Our study aimed to develop a support vector machine (SVM) classification model using T1-weighted MR and F-18 FDG PET brain imaging to classify cognitive normal (CN) and early-stage Alzheimer's disease, including early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI), in individuals aged 65-75. The study comprised three steps. Firstly, image preprocessing involved uniformity processing, B1 bias field correction for MR images, and FWHM optimization with spatial/intensity normalization for PET images. Secondly, MR images were registered to the MNI305 brain template for structural segmentation, and PET images were normalized using the standardized PET template. Co-registration of normalized PET images to segmented MR images provided anatomical segmented PET uptake volumes. These served as input for the classification model. Thirdly, SVM models classified CN vs. LMCI, EMCI vs. LMCI, and CN vs. EMCI using MRI, PET, and combined PET/MR. Feature sets included all features, clinical-based features, and F1-score ranked features, resulting in 27 classification models. In CN vs. EMCI, combined PET/MR with all features achieved 0.71 AUC, 65.78% accuracy, and 68.67% specificity. For CN vs. LMCI, superior performance was observed with combined PET/MR using F1-score ranked features: 0.82 AUC, 77.78% accuracy, and 77.55% specificity. In EMCI vs. LMCI, PET alone with all features achieved the highest performance: 0.72 AUC, 69.23% accuracy, and 60.32% specificity. In conclusion, PET is pivotal in MCI stage differentiation, and using all features aids in challenging tasks (CN vs. EMCI and EMCI vs. LMCI). The combined PET/MR modality notably distinguishes CN from MCI, emphasizing the potential of multimodal imaging to enhance differentiating cognitive states

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Optimization of Gene Translation Using SD Complementary Sequences and Double Codons

Optimization of Gene Translation Using SD Complementary Sequ...

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作者： Dingfa Liang Zhumian Huang Liufeng Zheng Yuannong Ye Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and EngineeringGuizhou Medical University Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University

Studies have confirmed that the growth, development and maintenance structure and function of all living organisms are determined by their genes, but the speed of gene translation products is affected by various internal and external factors. Therefore, studying the gene translation speed is extremely important for production and improving the speed through a variety of ways has always been an important subject. In recent years, the found of codon usage bias, t RNA recycling model and SD sequence-like structure provide another way to optimize the translation efficiency. These methods are almost based on codon usage theory to optimize gene sequence to speed up or slow down the translation speed so as to regulate the cell expression and metabolism. This paper is combined the three theories above, aimed at *** to design some reasonable algorithm to optimize gene sequence and thus using codon adaptation index(CAI) to evaluate the optimized effect.

关键词： E.coli Optimization of gene translation efficiency Codon usage bias tRNA-recyling model SD sequence-like structure

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Integrated Helicobacter Pylori Genome database and Its Analysis

Integrated Helicobacter Pylori Genome Database and Its Analy...

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作者： Liufeng Zheng Mujuan Guo Dingfa Liang Yuannong Ye Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and EngineeringGuizhou Medical University Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University

The shape of helicobacter pylori(HP) is usually helical or S-shaped,which makes it to have the feature of highly divergent. Besides, its distribution also is different greatly in space and time, which is the factor to lead to multiple infection. In addition, in the same infected person, multiple infection also appeared in different regions of strain gene infection origin. Therefore, it is of great significance to study the genomic diversity and evolutionary analysis of Helicobacter pylori. In this paper, we are to analyze the homology of multiple sequences using Clustal X1.83 and meanwhile to construct the homologous genome evolutionary tree by MEGA7.0 to analyze the its function. The purpose of this study is to compare the genomic evolutionary differences of Helicobacter pylori among populations in different regions, and to discuss the significance of its infection prevalence and genomic variation in geographical changes, so as to conduct further discussion on the molecular level research mechanism of Helicobacter pylori.

关键词： Helicobacter pylori ClustalX MEGA Genome Phylogenetic tree

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The Algorithms of Predicting Bacterial Essential Genes and NcRNAs by Machine Learning

The Algorithms of Predicting Bacterial Essential Genes and N...

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作者： Yuannong Ye Dingfa Liang Zhu Zeng Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and EngineeringGuizhou Medical University Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University

Essential genes are indispensable for biological survival. Thus it is of great significance to identify and study essential genes. A machine learning method, K-Nearest Neighbor, is used for development of predicting essential bacterial genes. The homologous features, including sequence homology and functional homology, of the bacterial genomes are extracted for determining essential genes. Based on the features, we use K-Nearest Neighbor algorithm for determining of gene function. And we tune the minimum matching parameter(K) in the essential gene predicted model for building an optimal model of the Escherichia coli specificity model. The corresponding optimal parameter(K) is then extended to other bacterial essential genes predicting models. After cross validation, the highest accuracy is 0.89 while K between 5 and 7. Therefore, the features we extracted can increase the accuracy of the bacterial essential gene prediction. In the premise, we found that the prediction accuracy of the prediction model based on K-Nearest Neighbor was not significantly different in different evolutionary distances between organisms in the database and the investigated species. That means the machine learning model can be extended to more distant species. It wills have a better predictive performance for predicting essential genes of distant species than the usual sequence-based methods.

关键词： Essential genes Machine learning KNN

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iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

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Pharmacological Research 2022年 175卷 105996-105996页

作者： ElHarouni, Dina Berker, Yannick Peterziel, Heike Gopisetty, Apurva Turunen, Laura Kreth, Sina Stainczyk, Sabine A. Oehme, Ina Pietiäinen, Vilja Jäger, Natalie Witt, Olaf Schlesner, Matthias Oppermann, Sina Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany Hopp Children's Cancer Center (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Division of Neuroblastoma Genomics German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Department of Pediatric Oncology Hematology Immunology and Pulmonology Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://*** © 2021 The Authors

关键词： Asymmetric sensitivity scoring Differential combination sensitivity scoring Drug target networks Interactive drug screen analysis Personalized medicine Therapy response profiling and exploration

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