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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第2期22卷 537-544页

作者： Sun, Jiawei Qian, Weiye Ma, Nan Liu, Wenjia Yang, Zhiyuan Hangzhou Dianzi Univ Sch Artificial Intelligence Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Elect & Informat Hangzhou 310018 Peoples R China

With the frequent outbreak of viral pandemics, the search for efficient antiviral drugs has become an urgent task. Antiviral peptides (AVPs) have been proven to prevent the infection of host cells by viruses. This study proposes a novel tool called Datt-AVP for AVP prediction based on the peptide sequences. A dual-channel deep learning model of Long Short-Term Memory network and convolutional neural network, along with a pre-trained protein language model as a feature extractor, was applied to capture features in the antiviral peptide sequences simultaneously. Furthermore, self-attention module was added to promote the performance of our prediction results. It showed good recognition ability for antiviral peptides with different lengths. Our tool achieved an accuracy rate of 96.1% on the benchmark dataset, which out-performed state-of-the-art tools.

关键词： Peptides Long short term memory Vectors Encoding Feature extraction Biological system modeling bioinformatics Drugs Training Tensors Antiviral peptide deep learning attention mechanism

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Emotion Detection Mastery: EEG Signal Analysis with Balanced vs. Unbalanced Data

Emotion Detection Mastery: EEG Signal Analysis with Balanced...

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2024 International conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Menaka, Jadov Sreelekshmi, G.I. Poulose, Alwin School of Data Science Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India University of Kerala Department of Computational Biology and Bioinformatics Kerala Thiruvananthapuram695581 India

ISBN: (纸本)9798331540067

Emotion is crucial in daily life, influencing stress levels, decision-making, relationships, and overall mental health. Recognizing emotions accurately is valuable across sectors like healthcare, education, and beyond. This paper presents a comparative study on emotion recognition, applying various deep learning models with the DEAP dataset, which includes EEG recordings from 32 participants who watched 40 short music video clips. We extracted time-domain, frequency-domain, and wavelet features from the preprocessed EEG data to capture nuanced patterns within the signals. The models assessed in this study include convolutional neural networks (CNN), long short-term memory networks (LSTM), gated recurrent units (GRU), and hybrid architectures, each trained on both balanced and unbalanced datasets. To address class imbalance, data was augmented using the Synthetic Minority Oversampling Technique (SMOTE). Results indicate that the Attention GRU model achieved slightly higher performance by utilizing its attention mechanism to capture extended dependencies within the data. Additionally, model accuracy improved by 5-9% when trained on balanced datasets, underscoring the importance of data balancing in enhancing classification outcomes. © 2024 ieee.

关键词： Convolutional neural networks

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Analyzing genomic features with predictive chromatin interaction models: A comprehensive evaluation 9

Analyzing genomic features with predictive chromatin interac...

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9th ieee International conference on bioinformatics and computational biology, ICBCB 2021

作者： Kou, Yi Zhao, Daniel Molecular and Computational Biology University of Southern California Los AngelesCA United States Harvard University Department of Mathematics CambridgeMA United States

ISBN: (纸本)9780738132020

Enhancer-Promoter (EP) interactions reflected by Hi-C technology are crucial to understanding genomic functions. Particularly, identifying 'unique' genomic features that are characteristically important in a specific cell line can further our current understanding of the mechanisms that drive cell differentiation, tissue development, and disease progression. However, classic prediction models such as TargetFinder provide little valuable insight towards the large disparity between important genomic features across different cell lines. To comprehensively approach this question, herein we first evaluated seven classifiers to predict EP interactions using high-resolution Hi-C maps of genome loci across six classic cell lines, surpassing TargetFinder in all benchmark metrics. We then evaluated the model's predictive performance with features provided by seven feature selection methods from the embedded, wrapper and filter categories. Moreover, groups of features were aggregated from the results of two or more feature methods and analyzed based on the model's performance. Finally, we examined the distinguishing features across six cell lines. Our study suggests the existence of 'unique' genomic features that are especially predictive of EP interactions only in specific cell lines. © 2021 ieee.

关键词： Genome

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Virtual Plant Based Training: Exploring Innovative Educational Mode for Agriculture

Virtual Plant Based Training: Exploring Innovative Education...

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Digital Twins and Parallel intelligence (DTPI), ieee International conference on

作者： Haoyu Wang Xiujuan Wang Jing Hua Zhaoxun Wang Zede Shao Mengzhen Kang State Key Laboratory of Multi Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shandong Xingke Intelligent Technology Co. Ltd. Jinan China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automatio Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macau China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Traditional agricultural education mainly relies on text and images, while the teaching of smart agriculture requires new technologies. With the development of virtual reality technology, the training and teaching mode based on plant and environmental modelling are gradually becoming an innovative practice in the field of agricultural education. Based on GreenLab model and an interactive interface, a training software for uderstanding plant behaviour has been developed for the training of agricultural colleges and universities. By analysing the application of computational plant, this paper discusses how this mode can improve learning efficiency, enhance students' practical ability, and provide new ideas and methods for agricultural education.

关键词： Training Smart agriculture Solid modeling Plants (biology) computational modeling Green products Virtual reality Software Digital twins computational efficiency

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Protein-Protein Interaction Network Analysis Reveals Distinct Patterns of Antibiotic Resistance Genes

Protein-Protein Interaction Network Analysis Reveals Distinc...

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2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Moumi, Nazifa Ahmed Brown, Connor L. Vikesland, Peter J. Pruden, Amy Zhang, Liqing Virginia Polytechnic Institute and State University Department of Computer Science BlacksburgVA United States Virginia Polytechnic Institute and State University Genetics Bioinformatics and Computational Biology BlacksburgVA United States Virginia Polytechnic Institute and State University Department of Civil & Environmental Engineering BlacksburgVA United States

ISBN: (纸本)9781665468190

Antibiotic resistance genes (ARGs) are responsible for an increasing number of bacterial infections worldwide. ARGs are challenging to track within bacterial genomes as they are often subject to horizontal gene transfer via mobile genetic elements (MGEs). Complex protein-protein networks can reveal proteins contributing to the spread and persistence. Here, we developed a pipeline that facilitates this process by analyzing features of a Protein-Protein Interaction Network (PPIN). This pipeline uses a random forest model to distinguish ARGs from non-ARGs and explores associations between ARGs and proteins with which they functionally interact. We tested the approach using the PPINs of Escherichia coli and Acinetobacter baumannii, two deadly organisms known to carry ARGs harbored by MGEs, and achieved a macro average accuracy of 85% in ARG identification. The approach also revealed that ARGs are disproportionately associated with MGEs and the neighbors (genes connected with only one edge to the ARGs) are likely to be less mobile. © 2022 ieee.

关键词： Proteins

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Joint Inference of Repeated Evolutionary Trajectories and Patterns of Clonal Exclusivity or Co-occurrence from Tumor Mutation Trees 26th

Joint Inference of Repeated Evolutionary Trajectories and ...

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26th International conference on Research in computational Molecular biology, RECOMB 2022

作者： Luo, Xiang Ge Kuipers, Jack Beerenwinkel, Niko Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland SIB Swiss Institute of Bioinformatics Basel Switzerland

ISBN: (纸本)9783031047480

Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Recent advances in multi-region sequencing, single-cell sequencing, and phylogenetic tree inference empower more precise reconstruction of the mutational history of each tumor. At the same time, the increased resolution also highlights the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering the recurrent evolutionary processes may offer greater opportunities for designing successful therapeutic strategies. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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Ensemble Learning Based Gene Regulatory Network Inference 33

Ensemble Learning Based Gene Regulatory Network Inference

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ieee 33rd International conference on Tools with Artificial intelligence (ICTAI)

作者： Peignier, Sergio Sorin, Baptiste Calevro, Federica Univ Lyon INSA Lyon INRAE BF2IUMR0203 F-69621 Villeurbanne France

ISBN: (纸本)9781665408981

In the machine learning field, the technique known as ensemble learning aims at combining different base learners in order to increase the quality and the robustness of the predictions. Indeed, this approach has widely been applied to tackle, with success, real world problems from different domains, including computational biology. Nevertheless, despite their potential, ensembles combining results from different base learners, have been understudied in the context of gene regulatory network inference. In this paper we applied genetic algorithms and frequent itemset mining, to design small but effective ensembles of gene regulatory network inference methods. These ensembles, were evaluated and compared to well-established single and ensemble methods, on real and synthetic datasets. Results showed that small ensembles, consisting of few but diverse base learners, enhance the exploration of the solution space, and compensate base-learners biases, outperforming state-of-the-art methods. Results advocate for the use of such methods as gene regulatory network inference tools.

关键词： bioinformatics Gene Regulatory Network Inference Ensemble Learning

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A Local Outlier Factor-Based Detection of Copy Number Variations From NGS Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1811-1820页

作者： Yuan, Xiguo Li, Junping Bai, Jun Xi, Jianing Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Shaanxi Prov Peoples Hosp Dept Med Oncol Xian 710068 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710072 Peoples R China

Copy number variation (CNV) is a major type of genomic structural variations that play an important role in human disorders. Next generation sequencing (NGS) has fueled the advancement in algorithm design to detect CNVs at base-pair resolution. However, accurate detection of CNVs of low amplitudes remains a challenging task. This paper proposes a new computational method, CNV-LOF, to identify CNVs of full-range amplitudes from NGS data. CNV-LOF is distinctly different from traditional methods, which mainly consider aberrations from a global perspective and rely on some assumed distribution of NGS read depths. In contrast, CNV-LOF takes a local view on the read depths and assigns an outlier factor to each genome segment. With the outlier factor profile, CNV-LOF uses a boxplot procedure to declare CNVs without the reliance of any distribution assumptions. Simulation experiments indicate that CNV-LOF outperforms five existing methods with respect to F1-measure, sensitivity, and precision. CNV-LOF is further validated on real sequencing samples, yielding highly consistent results with peer methods. CNV-LOF is able to detect CNVs of low and moderate amplitudes where the other existing methods fail, and it is expected to become a routine approach for the discovery of novel CNVs on whole sequencing genome.

关键词： Genomics bioinformatics Sequential analysis Tumors Computer science computational modeling Next generation networking Copy number variation local outlier factor next generation sequencing boxplot procedure

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Optimized Adaptive Based Method for MR Image Denoising

Optimized Adaptive Based Method for MR Image Denoising

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International conference on computational intelligence and Communication Networks (CICN)

作者： Noorbakhsh Amiri Golilarz Iman Dehzangi Keyan Rahimi Department of Computer Science and Engineering Mississippi State University Mississippi State MS USA Department of Computer Science Center for Computational and Integrative Biology (CCIB) Rutgers University Camden NJ USA Department of Computer Science Brown University Providence RI USA

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

In this paper, we aim to utilize the orca optimization algorithm (OOA) as a computational intelligence method combined with a non-linear data-driven function in the optimization process for brain image de-noising. This meta-heuristic population-based optimizer has recently been proposed to solve complex optimization problems and has achieved promising results in various domains. Here, it is used to obtain the optimum threshold value and other parameters for the activation function. We investigate the impact of this novel algorithm compared to other state-of-the-art optimizers and alternative approaches such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), Differential Evolution (DE), Thresholding Neural Network (TNN), Neighboring Wavelet Coefficients (Neishrink), Hard and Soft thresholds. Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are also used to measure the performance analysis of various de-noising approaches. The results show that the proposed method performs better than other alternative optimization techniques.

关键词： Brain PSNR Noise reduction Metaheuristics Particle measurements Performance analysis Particle swarm optimization computational intelligence Image denoising Wavelet coefficients

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Redefining Protein Structure Prediction: Insights from AlphaFold’s Methodology and Applications

Redefining Protein Structure Prediction: Insights from Alpha...

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Learning and Technology conference (L&T)

作者： Reema Abdallah Sarah Alshumayri Lujain Bukassim Computer Science College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

This paper presents a comprehensive analysis of AlphaFold, a pioneering algorithm developed by DeepMind, which has revolutionized the field of protein structure prediction. Since its introduction at CASP13, AlphaFold has achieved remarkable success in predicting protein structures from amino acid sequences with unprecedented accuracy. AlphaFold2 further improved upon its predecessor, integrating deeper biological insights and achieving near-experimental precision in structure predictions. In this study, we explore the methodologies that underpin AlphaFold’s success and investigate its practical applications in drug discovery, disease modeling, and broader scientific research. By replicating key components of AlphaFold’s workflow and excluding homologous structure templates, this research provides hands-on insights into its mechanisms and the challenges of static structure predictions. Furthermore, we propose strategies to address these limitations, focusing on the integration of dynamic protein simulations and advanced machine-learning models. Our findings underscore the transformative role of artificial intelligence in computational biology, with significant implications for the future of protein science and biomedical innovation.

关键词： Proteins Protein engineering Technological innovation Accuracy computational modeling Biological system modeling Reliability bioinformatics Diseases computational biology

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