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21st ieee conference on computational Intelligence in bioinformatics and computational Biology, CIBCB 2024

21st IEEE Conference on Computational Intelligence in Bioinf...

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21st ieee conference on computational Intelligence in bioinformatics and computational Biology, CIBCB 2024

ISBN: (纸本)9798350356632

The proceedings contain 38 papers. The topics discussed include: a multiobjective evolutionary algorithm for colon cancer biomarkers identification on gene expression data;machine learning identifies novel microRNA biomarkers predictive for gastric cancer;methods for analyzing swallowing sound in dysphagia care: a telemedicine approach;a fast feature selection for interpretable modeling based on fuzzy inference systems;improving machine learning based sepsis diagnosis using heart rate variability;machine learning and gut microbiome for breast cancer screening;comprehensive modeling and question answering of cancer clinical practice guidelines using LLMs;and predicting metabolic reactions with a molecular transformer for drug design optimization.

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A Comparative Analysis of Machine Learning and Deep Learning Approaches in bioinformatics 4

A Comparative Analysis of Machine Learning and Deep Learning...

引用

4th International conference on Expert Clouds and Applications, ICOECA 2024

作者： Ramar, J. Porkodi, R. Bharathiar University Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350385793

bioinformatics is an interdisciplinary area that combines biology, computer technological know-how, and records to investigate and interpret biological facts. It makes use of computational algorithms, equipment, and databases to understand biological techniques, genetic variation, and relationships among genes, proteins, and different organic molecules. bioinformatics performs a critical role in genomics, structural biology, and drug discovery. bioinformatics uses programming languages like Python, R, and Perl, statistical methods, and device gaining knowledge of techniques to investigate and interpret biological records. All things considered, bioinformatics advances our knowledge of biological systems, helps us comprehend diseases, and helps us create personalized medication and specialized treatment plans. © 2024 ieee.

关键词： Contrastive Learning

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Single-Cell RNA-Seq Data Clustering: Highlighting computational Challenges and Considerations

Single-Cell RNA-Seq Data Clustering: Highlighting Computatio...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Nossier, Mohamad Moussa, Sherin M. Badr, Nagwa Ain Shams University Faculty of Computer and Information Sciences Program of Bioinformatics Department of Information Systems Cairo11566 Egypt Cairo11837 Egypt Ain Shams University Faculty of Computer and Information Sciences Department of Information Systems Cairo11566 Egypt

ISBN: (纸本)9798350337488

Recent advancements in single-cell transcriptomics have revolutionized data generation, resulting in the production of vast amounts of single-cell RNA sequencing (scRNA-seq) data. The intricate biology of tissues and organs may be better understood through the analysis of such data, which aims to not only identify known cell types but also uncover novel ones. In this paper, we outline the standard single-cell RNA-seq data analytic process with particular focus on the critical role of unsupervised clustering, since it has a significant influence over subsequent analyses and the resulting biological insights. However, navigating the computational landscape for clustering single-cell transcriptomic data poses significant challenges. Thus, in this article, we provide a simplified exploration of key computational challenges and considerations within the realm of scRNA-seq data clustering, emphasizing the application and relevance of selected unsupervised methods in current research, while acknowledging the potential for further investigation of the biological aspects in this field. Furthermore, we present recommendations on how such challenges may be addressed. This work not only sheds light on the complexities of scRNA-seq data cluster analysis, but also provides insights into how to effectively address and surmount these challenges. © 2023 ieee.

关键词： Clustering Data Analysis RNA-sequencing Single-cell Transcriptomics Unsupervised learning

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DNA Pattern Matching Algorithms within Sorghum bicolor Genome: A Comparative Study 7

DNA Pattern Matching Algorithms within Sorghum bicolor Genom...

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7th International conference on Informatics and computational Sciences, ICICoS 2024

作者： Yonia, Dwika Lovitasari Hidayati, Shintami Chusnul Sarno, Riyanarto Septiyanto, Abdullah Faqih Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350375886

Sorghum bicolor, a vital cereal crop with significant roles in agriculture and biofuel production, presents a complex genome that poses substantial challenges for genetic analysis and improvement. This study provides a comprehensive comparison of DNA pattern-matching algorithms applied to the Sorghum bicolor genome, focusing on the accuracy, efficiency, and effectiveness of each technique. The algorithms evaluated include the simple Brute Force method, the efficient BoyerMoore algorithm, the linear-time Knuth-Morris-Pratt (KMP) algorithm, the Enhanced First-Last Pattern Matching (EFLPM), and the Enhanced Processor-Aware Pattern Matching (EPAPM). Notably, the EFLPM and EPAPM algorithms excel at accommodating errors and mutations in DNA sequences, with EPAPM additionally leveraging parallel processing techniques to enhance performance. This comparative study highlights the crucial role of temporal complexity in selecting the most suitable DNA pattern-matching algorithm for genomic analysis. © 2024 ieee.

关键词： agricultural technology bioinformatics pattern matching Sorghum bicolor genome

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Assessing Tree-Based Phenotype Prediction on the UK Biobank

Assessing Tree-Based Phenotype Prediction on the UK Biobank

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Melendez, Alex Lopez, Cayetana Bonet, David Sant, Gerard Montserrat, Daniel Mas Abante, Jordi Rivas, Manuel Marques, Ferran Ioannidis, Alexander G. Universitat Politècnica de Catalunya Barcelona Spain Stanford University StanfordCA United States Universitat de Barcelona Barcelona Spain

ISBN: (纸本)9798350337488

Precision medicine relies on the ability to identify associations between genomic data and its phenotypic expression in order to provide personalized predictions. Phenotype prediction using statistical models trained on large-scale genomic and phenotypic data is a critical research area at the intersection of machine learning and genomics. Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships, and the use of nonlinear methods is still partially unexplored. In this work, we evaluate the prediction accuracy and scalability of nine nonlinear decision tree-based algorithms, including ensembling and boosting mechanisms, and compare them to linear prediction models. We assess the prediction performance for 24 anthropometric and disease-related phenotypes present in the UK Biobank. By using random feature selection, we explore how accuracy and computational time vary for each method as a function of the number of genetic variants selected. Our results show that tree-based methods, especially gradientboosted trees, can offer superior predictions with computational times comparable to those of linear methods. Thus, models able to capture nonlinear relationships between genotypes and phenotypes merit consideration for integration in upcoming computational systems for personalized medicine. © 2023 ieee.

关键词： bioinformatics Machine Learning Phenotype Prediction Precision Medicine Tree-based Methods

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Analysis of feedback schemes in proliferating stem cells 63

Analysis of feedback schemes in proliferating stem cells

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63rd ieee conference on Decision and Control, CDC 2024

作者： Borri, A. Palumbo, P. Singh, A. Via dei Taurini 19 Roma Italy University of Milano-Bicocca Department of Biotechnologies and Biosciences Milan20126 Italy University of Delaware Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology NewarkDE19716 United States

ISBN: (纸本)9798350316339

Stem cells have emerged as a pivotal player in biomedical research, offering unprecedented opportunities for regenerative medicine, disease modeling, and drug discovery. These undifferentiated cells possess the unique ability to self-renew and differentiate into various specialized cell types, making them invaluable in tissue repair and regeneration. This note analyzes a stochastic model aiming at unraveling the control machinery providing the proper ratio of stem-versus-differentiated cells, in agreement with a couple of feedback schemes regulating cell proliferation. What emerges from the closed-loop schemes analysis is that in order to have an asymptotically stable, nontrivial accumulation of differentiated cells a negative feedback on cell proliferation is required. Numerical simulations in the stochastic setting validate the approximate theoretical results achieved to relate how random fluctuations propagate with respect to variations of model parameters. © 2024 ieee.

关键词： Stochastic systems

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Quantifying statistics of gene product copy-number fluctuations: A stochastic hybrid systems approach 63

Quantifying statistics of gene product copy-number fluctuati...

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63rd ieee conference on Decision and Control, CDC 2024

作者： Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States University of Delaware Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology NewarkDE19716 United States

ISBN: (纸本)9798350316339

Across diverse cell types, genes are often expressed at low levels resulting in significant stochastic fluctuations (noise) in intracellular copy number of mRNAs and proteins over time. Motivated by single-cell experiments, we capture this stochasticity by considering a given gene that toggles between transcriptionally active and inactive states with the time spent in each state being an arbitrary random variable. mRNAs are synthesized from the active state as per a Poisson process and each molecule degrades after a random lifespan (i.e., random time from birth to death). Modeling this process using the Stochastic Hybrid System (SHS) formalism we derive exact analytical results for the statistical moments of molecular counts. Our results show that for fixed mean mRNA numbers, fluctuations in mRNA levels are amplified with decreasing noise in mRNA lifespan. Relaxing the Poisson process assumption, we next consider a scenario where transcription events occur such that the time between successive events is an arbitrarily distributed random variable. In this case, we show that decreasing noise in mRNA lifespan can both increase/decrease mRNA count fluctuations depending on the underlying transcriptional process. Finally, we extend these results to the protein level, where increasing noise in mRNA counts is sometimes associated with decreasing noise in protein copy numbers. © 2024 ieee.

关键词： Stochastic systems

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Evaluation of GenAI in Biomedical Knowledge Discovery 58

Evaluation of GenAI in Biomedical Knowledge Discovery

引用

58th Hawaii International conference on System Sciences, HICSS 2025

作者： Juris, Radmila Almami, Eiman Almami, Ibtesim ALMAIS United Kingdom MWLLO United Kingdom Qassim University Saudi Arabia

ISBN: (纸本)9780998133188

This paper evaluates the power of GenAI and its generated results and compares them with the more traditional way of computational reasoning for biomedical knowledge discoveries. The experiments conducted in this research focus on the problem of discovering activated/inhibited enzymes involved in diseases, but the same idea of evaluating GenAI can be applied to any problem domain. The traditional way of knowledge discovery through the power of computational logic reasoning is now challenged by the rather unexpected power of LLM and transformer architecture, such as ChatGPT, in almost any discipline of science and humanities. The results from this research are interesting: ChatGPT disappoints in biomedical knowledge discoveries because of the lack of precision and absence of reasoning. However, it is feasible to define pathways, when using GenAI, which can remedy current ChatGPT shortcomings. © 2025 ieee Computer Society. All rights reserved.

关键词： bioinformatics

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A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction 17

A Multi-objective Cluster-based Biased Random-Key Genetic Al...

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17th conference on Computer Science and Intelligence systems (FedCSIS)

作者： Marchi, Felipe Parpinelli, Rafael Stubs Santa Catarina State Univ Appl Comp Grad Program Joinville SC Brazil

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

The protein structure prediction problem is one of the most important bioinformatics problems. computational methods can be used to approach this problem and de novo methods are able to generate protein structures without the need of having known similar structures to the predicted protein. These methods transform the prediction problem into an optimization problem, using optimization models that combine different energy functions and high-level information. These models usually have only a single optimization objective. However, it is known that this single objective optimization approach may harm the optimization search due to the existence of conflicts between the different terms that compose the optimization objective. The proposed model has three objectives: energy function, secondary structure, and contact maps. A multi-objective Biased RandomKey Genetic Algorithm (BRKGA) with online parameter control, named MOBO, is proposed as the optimizer. The final predictor comprises two phases of the MOBO algorithm and selects a final structure using the MUFOLD-CL clustering method. Results obtained demonstrated that the proposed predictor generated highly competitive results with the literature.

关键词： bioinformatics Multi-objective Optimization Evolutionary Computation Clustering Online Parameter Tuning Protein Structure Prediction

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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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