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exp2GO: Improving Prediction of Functions in the Gene Ontology With Expression Data

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 999-1008页

作者： Di Persia, Leandro Lopez, Tiago Arce, Agustin Milone, Diego H. Stegmayer, Georgina Consejo Nacl Invest Cient & Tecn FICH UNL Sinc I Res Inst Signals Syst & Computat Intelligence Ciudad Univ UNLS3000ADQ Santa Fe Argentina Consejo Nacl Invest Cient & Tecn UNL IAL Inst Agrobiotecnol Litoral Ciudad Univ UNLS3000ADQ Santa Fe Argentina

The computational methods for the prediction of gene function annotations aim to automatically find associations between a gene and a set of Gene Ontology (GO) terms describing its functions. Since the hand-made curation process of novel annotations and the corresponding wet experiments validations are very time-consuming and costly procedures, there is a need for computational tools that can reliably predict likely annotations and boost the discovery of new gene functions. This work proposes a novel method for predicting annotations based on the inference of GO similarities from expression similarities. The novel method was benchmarked against other methods on several public biological datasets, obtaining the best comparative results. exp2GO effectively improved the prediction of GO annotations in comparison to state-of-the-art methods. Furthermore, the proposal was validated with a full genome case where it was capable of predicting relevant and accurate biological functions. The repository of this project withh full data and code is available at https://***/sinc-lab/exp2GO.

关键词： Annotations Semantics Ontologies Genomics bioinformatics Proteins Bayes methods Gene ontology semantic distance gene function prediction non negative matrix factorization bayesian inference

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A semantic mediation approach for problems in computational molecular biology 2

A semantic mediation approach for problems in computational ...

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2nd International computational Systems bioinformatics conference

作者： Chagoyen, M Kurul, ME De-Alarcon, PA Santini, S Ludäscher, B Carazo, JM Gupta, A Ctr Nacl Biotecnol Biocomp Unit Madrid Spain

ISBN: (纸本)0769520006

As great amount of data are being produced and accumulated in Life Sciences, information access and consequent integration in analytical and computational methods become critical issues in order to conduct research. In this work we describe how the synergic combination of a high-level programmable planner engine and formal semantics are suitable to resolve many information integration problems in molecular biology.

关键词： Molecular biology

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Guest Editorial: Advanced Machine-Learning Methods for Brain-Machine Interfacing or Brain-Computer Interfacing

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1643-1644页

作者： Xia, Kaijian Jiang, Yizhang Zhang, Yudong Si, Wen China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Soochow Univ Changshu peoples Hosp 1 Affiliated Changshu Hosp Changshu 215500 Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Univ Leicester Dept Informat Leicester LE1 7RH Leics England Univ S Florida Coll Engn Tampa FL 33620 USA

The seven papers in this special section focus on advanced machine learning methods for brain machine interfacing. Particular emphasis is on novel theories and methods using transfer learning and deep learning proposed for Brain-Machine Interfacing (BMI) or Brain-Computer Interfacing (BCI). Our purpose is to review the new progress and achievements on transfer learning, deep learning, and their applications in BMI or BCI in recent years.

关键词： Special issues and sections Machine learning Brain-computer interfaces Transfer learning Deep learning computational intelligence

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Predicting MHC-II Binding Affinity Using Multiple Instance Regression

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2011年第4期8卷 1067-1079页

作者： EL-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Al Azhar Univ Dept Syst & Comp Engn Cairo Egypt Iowa State Univ Dept Genet Dev & Cell Biol Bioinformat & Computat Biol Grad Program Ames IA 50011 USA Iowa State Univ Artificial Intelligence Lab Bioinformat & Computat Biol Grad Program Dept Comp SciCtr Computat Intelligence Learning Ames IA 50011 USA

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark data sets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://***/mhcmir.

关键词： MHC-II peptide prediction multiple instance learning multiple instance regression

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Block Search Stochastic Simulation Algorithm (BlSSSA): A Fast Stochastic Simulation Algorithm for Modeling Large Biochemical Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第4期19卷 2111-2123页

作者： Ghosh, Debraj De, Rajat K. Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

Stochastic simulation algorithms are extensively used for exploring stochastic behavior of biochemical pathways/networks. computational cost of these algorithms is high in simulating real biochemical systems due to their large size, complex structure and stiffness. In order to reduce the computational cost, several algorithms have been developed. It is observed that these algorithms are basically fast in simulating weakly coupled networks. In case of strongly coupled networks, they become slow as their computational cost become high in maintaining complex data structures. Here, we develop Block Search Stochastic Simulation Algorithm (BlSSSA). BlSSSA is not only fast in simulating weakly coupled networks but also fast in simulating strongly coupled and stiff networks. We compare its performance with other existing algorithms using two hypothetical networks, viz., linear chain and colloidal aggregation network, and three real biochemical networks, viz., B cell receptor signaling network, FceRI signaling network and a stiff 1,3-Butadiene Oxidation network. It has been shown that BlSSSA is faster than other algorithms considered in this study.

关键词： Stochastic processes Biological system modeling Mathematical model computational modeling computational efficiency Indexes Statistics Gillespie algorithm B cell receptor signaling network FceRI signaling network Stiff network Colloidal aggregation network and stochastic modeling

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Discovery of MicroRNA markers: An SVM-based multiobjective feature selection approach

Discovery of MicroRNA markers: An SVM-based multiobjective f...

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ieee Symposium on computational intelligence in bioinformatics and computational biology

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Bandyopadhyay, Sanghamitra Department of Computer Science and Engineering University of Kalyani Kalyani-741235 India Department of Computer Science and Engineering Jadavpur University Kolkata-700032 India Machine Intelligence Unit Indian Statistical Institute Kolkata-700108 India

ISBN: (纸本)9781424498970

MicroRNAs (miRNAs) are small non-coding RNAs that have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNAs often results in the development of cancer. In this article, we have utilized a multiobjective genetic algorithm-based feature selection algorithm wrapped with support vector machine (SVM) classifier for selecting promising miRNAs having differential expression in benign and malignant tissue samples. Subsequently, the non-dominated sets of promising miRNAs are aggregated into a single most promising miRNA subset. Finally, the Signal-to-Noise Ratio (SNR) statistic has been applied on the obtained miRNA subset for identifying potential miRNA markers that distinguish the two classes (benign and malignant) of tissue samples. The performance has been demonstrated on four real-life miRNA expression datasets for different SVM kernel functions and the identified miRNA markers are reported. © 2011 ieee.

关键词： Support vector machines

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Guest Editorial Advanced Machine Learning and Artificial intelligence Tools for computational biology: Methodologies and Challenges

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ieee JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第4期28卷 1883-1885页

作者： Khalifa, Fahmi Razzak, Imran Kamal, Mohammad Amjad Soliman, Ahmed Morgan State Univ Sch Engn Elect & Comp Engn Dept Baltimore MD 21251 USA Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Sichuan Univ Inst Syst Genet Frontiers Sci Ctr Dis Related Mol NetworkWest Ch Chengdu Peoples R China Sichuan Univ West China Hosp Frontiers Sci Ctr Dis Related Mol Network West China Sch Nursing Chengdu Peoples R China King Abdulaziz Univ King Fahd Med Res Ctr Jeddah 21589 Saudi Arabia Daffodil Int Univ Fac Hlth & Life Sci Dept Pharm Dhaka 1216 Bangladesh Saveetha Inst Med & Tech Sci Saveetha Med Coll & Hosp Ctr Global Hlth Res Chennai Tamil Nadu India Novel Global Community Educ Fdn Hebersham NSW 2770 Australia Mansoura Univ Fac Engn Mansoura 35516 Egypt

In recent years, the management and analysis of biological data have experienced exponential growth propelled by the relentless advancement of machine learning (ML) and artificial intelligence (AI) technologies. This is driven mainly by the remarkable ability and potentials of AI-based systems to craft sophisticated, yet effective, algorithms and analytical models tailored for the interpretation of biological information;thus, assist in making accurate predictions and/or decisions [1]. The surge in AI adoption is not unfounded;it's a response to the overwhelming increase in both the volume and acquisition rates of biological data.

关键词： Special issues and sections bioinformatics Biomedical monitoring Machine learning Artificial intelligence computational biology Analytical models

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Parameter Estimation of Essential Amino Acids in Arabidopsis thaliana Using Hybrid of Bees Algorithm and Harmony Search 12th

Parameter Estimation of Essential Amino Acids in Arabidopsis...

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12th International conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Aw, Mei Yee Mohamad, Mohd Saberi Chong, Chuii Khim Deris, Safaai Remli, Muhammad Akmal Ismail, Mohd Arfian Manuel Corchado, Juan Omatu, Sigeru Univ Teknol Malaysia Artificial Intelligence & Bioinformat Res Grp Fac Comp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Inst Artificial Intelligence & Big Data City Campus Kota Baharu 16100 Kelantan Malaysia Univ Malaysia Kelantan Fac Bioengn & Technol Jeli CampusLock Bag 100 Jeli 17600 Kelantan Malaysia Univ Malaysia Pahang Soft Comp & Intelligent Syst Res Grp Fac Comp Syst & Software Engn Kuantan 26300 Pahang Malaysia Univ Salamanca Biomed Res Inst Salamanca BISITE Res Grp Salamanca Spain Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan

ISBN: (纸本)9783319987026;9783319987019

Mathematical models of metabolic processes are the cornerstone of computational systems biology. In model building, the task of parameter estimation is difficult due to the huge numbers of kinetics parameters involved. The common way of estimating the parameters is to formulate it as an optimization problem. Global optimization methods can be applied by minimizing the distance between experimental data and predicted models. This paper proposes the Hybrid of Bees Algorithm and Harmony Search (BAHS) to estimate the kinetics parameters of essential amino acid production in the aspartate metabolism for Arabidopsis thaliana. The performance of the BAHS is evaluated and compared with other algorithms. The results show that BAHS performed better as it improved the performance of the original BA by 60%. Meanwhile, it takes less computational time to estimate the kinetics parameters of essential amino acid production for Arabidopsis thaliana.

关键词： Systems biology Parameter estimation Bees Algorithm Harmony Search computational intelligence Arabidopsis thaliana

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Seq-BEL: Sequence-Based Ensemble Learning for Predicting Virus-Human Protein-Protein Interaction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第3期19卷 1322-1333页

作者： Ma, Yingjun He, Tingting Tan, Yuting Jiang, Xingpeng Cent China Normal Univ Sch Comp Wuhan 430079 Hubei Peoples R China Cent China Normal Univ Sch Math & Stat Wuhan 430079 Hubei Peoples R China Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smar Wuhan 430079 Hubei Peoples R China Cent China Normal Univ Sch Math & Stat Wuhan 430079 Peoples R China Hubei Key Lab Math Sci Wuhan 430079 Hubei Peoples R China

Infectious diseases are currently the most important and widespread health problem, and identifying viral infection mechanisms is critical for controlling diseases caused by highly infectious viruses. Because of the lack of non-interactive protein pairs and serious imbalance between positive and negative sample ratios, the supervised learning algorithm is not suitable for prediction. At the same time, due to the lack of information on viral proteins and significant dissimilarity in sequence, some ensemble learning models have poor generalization ability. In this paper, we propose a Sequence-Based Ensemble Learning (Seq-BEL) method to predict the potential virus-human PPIs. Specifically, based on the amino acid sequence of proteins and the currently known virus-human PPI network, Seq-BEL calculates various features and similarities of human proteins and viral proteins, and then combines these similarities and features to score the potential of virus-human PPIs. The computational results show that Seq-BEL achieves success in predicting potential virus-human PPIs and outperforms other state-of-the-art methods. More importantly, Seq-BEL also has good predictive performance for new human proteins and new viral proteins. In addition, the model has the advantages of strong robustness and good generalization ability, and can be used as an effective tool for virus-human PPI prediction.

关键词： Proteins Viruses (medical) Feature extraction Amino acids Predictive models Kernel computational modeling Feature projection neighborhood completion network fusion virus-host interaction protein interaction

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A computational Framework to Study Public Health Epidemiology

A Computational Framework to Study Public Health Epidemiolog...

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International Joint conference on bioinformatics, Systems biology and Intelligent Computing

作者： Corley, Courtney D. Mikler, Armin R. Univ N Texas Dept Comp Sci & Engn Ctr Computat Epidemiol & Response Anal Denton TX 76203 USA

ISBN: (纸本)9780769537399

Public Health professionals require computational support to study disease outbreak dynamics and to facilitate policy and decision-making. Modeling disease outbreaks in large realistic populations is a data-intensive task that requires tremendous computational resources This paper presents a framework of multiple source and computational modules to evaluate what-if scenarios and quantify public health actions.

关键词： Public health

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