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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Silvana C. S. Cardoso Douglas S. Domingues Alexandre R. Paschoal Carlos N. Fischer Ricardo Cerri Department of Physics Federal University of São Carlos Brazil Departamento de Genética Escola Superior de Agricultura Luiz de Queiroz Universidade de São Paulo Brazil Department of Computer Science Federal University of Technology Brazil Departamento de Estatística Matemática Aplicada e Computação Universidade Estadual Paulista Brazil Department of Computer Science Instituto de Ciências Matemáticas e de Computaçao ˜ Universidade de São Paulo Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Transposable Elements (TEs) are genetic sequences that can relocate within the genome, promoting genetic diversity. In eukaryotes, TEs are classified into classes, subclasses, orders, superfamilies, families, and subfamilies. LTR retrotransposons (LTR-RT) constitute an order in this taxonomy. The main objective of this study is to investigate the classification of LTR retrotransposons at the superfamily level. Predictive Bi-Clustering Trees (PBCTs) were used to predict interactions between LTR-RT sequences and conserved protein domains to achieve this. Two datasets were used to investigate the relationships among different superfamilies. The first dataset contained LTR retrotransposon sequences assigned to Copia, Gypsy, and Bel-Pao superfamilies, while the second dataset included consensus sequences of the conserved domains for each superfamily. Thus, the PBCT decision tree tests could relate to the LTR-RT sequence and conserved domain attributes. In the classification process, interaction is interpreted as either the presence or absence of a domain in a given LTR-RT sequence. The sequence is then classified into the superfamily with the most predicted domains. Precision-recall curves were adopted as evaluation metrics for the method, and its performance was compared to some of the most commonly used models in the task of transposable element classification. The experiments conducted on D. melanogaster and A. thaliana showed that PBCTs are promising and comparable to other methods, especially in classifying the Gypsy superfamily.

关键词： Proteins Measurement computational modeling Biological system modeling Taxonomy Genomics Proposals Decision trees bioinformatics computational intelligence

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Medical Image Super-Resolution Based on Semantic Perception Transfer Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第4期20卷 2598-2609页

作者： Ren, Sheng Guo, Kehua Zhou, Xiaokang Hu, Bin Zhu, Feihong Luo, Entao Cent South Univ Sch Comp Sci & Engn Changsha 410017 Hunan Peoples R China Hunan Univ Arts & Sci Sch Comp & Elect Engn Changde 415000 Hunan Peoples R China Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Hunan Univ Sci & Technol Sch Informat Engn Xiangtan 411199 Hunan Peoples R China

Medical images are an important basis for doctors to diagnose diseases, but some medical images have low resolution due to hardware technology and cost constraints. Super-resolution technology can reconstruct low-resolution medical images into high-resolution images and enhance the quality of low-resolution images, thus assisting doctors in diagnosing diseases. However, traditional super-resolution methods mainly learn the mapping relationships among modal pixels from low resolution to high resolution, lacking the learning of high-level semantic features, resulting in a lack of understanding and utilization of semantic information, such as reconstructed objects, object attributes, and spatial relationships between two objects. In this paper, we propose a medical image super-resolution method based on semantic perception transfer learning. First, we propose a novel semantic perception super-resolution method that empowers super-resolution models to perceive high-level semantics by transferring features of the image description generation network in natural language processing. Second, we construct a semantic feature extraction network and an image description generation network and comprehensively utilized image and text modal data to learn transferable, high-level semantic features. Third, we train an end-to-end, semantic perception super-resolution model by fusing dynamic perceptual convolution, a semantic extraction network, and distillation polarization self-attention. Experiments show that semantic perception transfer learning can effectively improve the quality of super-resolution reconstruction.

关键词： Semantics Image reconstruction Superresolution Transfer learning Medical diagnostic imaging Feature extraction computational modeling Medical image super-resolution semantic perception transfer learning

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AGED-ViT: A Novel Transformer-Based Framework for Diagnosis of Alzheimer’s Disease by Leveraging Gene Expression Data

AGED-ViT: A Novel Transformer-Based Framework for Diagnosis ...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Albert Guo Megan Fowler Kay C. Wiese School of Computing Science Simon Fraser University Burnaby Canada

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Alzheimer’s disease (AD) is a growing global health concern and correct diagnosis is crucial for effective treatment. In this study, we present a novel method for the detection of AD using gene expression data from blood samples. We normalized and combined four publicly available Alzheimer’s datasets and trained a Vision Transformer (ViT) model. This combined dataset had almost seven times more features than patient samples which can cause models to overfit on the training data. To overcome this issue, we employed Linear Discriminant Analysis (LDA) to reduce the dimensionality of the data and noise injection to encourage generalizability and robustness. We then compared our model to several state-of-the-art models that used Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Deep Neural Networks (DNNs). Our model, AGED-ViT, achieved an average accuracy of 88.4% and area under the curve (AUC) of 0.951 on the combined dataset, outperforming previous methods. Our results demonstrate the importance of preprocessing techniques for data with more features than samples to reduce overfitting, as well as the powerful predictive capabilities of ViTs, establishing a foundation for further exploration and optimization of the transformer architecture in the context of genomic diagnosis. This study can contribute to improving the accuracy of AD diagnosis, thus facilitating intervention and leading to a more promising outcome for patients.

关键词： Support vector machines Computer vision Accuracy Training data Medical services Transformers Gene expression Alzheimer's disease bioinformatics Blood

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ieee CIBCB 2022

IEEE CIBCB 2022

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

ieee CIBCB 2022.

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484633

ieee CIBCB 2022.

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RFL-Score: Random Forest with Lasso Scoring Function for Protein-Ligand Molecular Docking

RFL-Score: Random Forest with Lasso Scoring Function for Pro...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Oscar E. Arrua Andrej Aderhold Adriano V. Werhli Karina Dos Santos Machado Center for Computational Sciences Universidade Federal do Rio Grande Rio Grande RS Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Due to the growth computational power and easy access to vast amounts of biological data, machine learning (ML) models are being increasingly considered within the field of Rational Drug Design (RDD). In RDD, molecular docking methods are employed to predict the optimal binding mode of a small molecule (ligand) in the binding site of a target receptor (typically a protein). Molecular docking software utilizes Scoring Functions (SFs) to estimate the protein-ligand binding affinity. These SFs can be classified according to their development methodology: Physics-based, Empirical, Knowledge-based, and ML-based. ML-based SFs are trained using features extracted from protein-ligand complexes with known experimental affinities, relying heavily on the attributes used during training. Both the input data and the selected features can significantly impact the accuracy of the SFs. Additionally, different ML algorithms can be applied to select the most appropriate features and propose the SF model. In this study we propose RFL-Score, a novel ML-based SF using Random Forest with Lasso regression. RFL-Score is a ready-to-use SF developed with a straightforward methodology and trained exclusively on open-access biomolecular data. Only open-source software was employed for extracting features from the proteins, ligands, and complexes. The proposed SF was evaluated using the well-established CASF-2016 benchmark achieving outstanding performance compared to other SFs. The RFL-Score scoring function and elements used for training are freely available on GitHub at https://***/rflscore.

关键词： Proteins Training Atomic measurements Biological system modeling Training data Feature extraction Receptor (biochemistry) bioinformatics Random forests Bars

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SEFP: Structure-Based Enzyme Function Prediction

IEEE Transactions on Computational Biology and Bioinformatic...

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ieee Transactions on computational biology and bioinformatics 2025年第3期22卷 1211-1221页

作者： Ziqi Zhang Guanqing Yu Zhaohong Deng Chenxi Luo Cheng Cai Wei Zhang Fuping Hu Kup-Sze Choi Zhisheng Wei Lei Wang Jing Wu School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China HL7 Hong Kong Ltd. Hong Kong School of Biotechnology Jiangnan University Wuxi China

Traditional biological experimental methods to determine enzyme properties are time-consuming and costly, leading to an increasing interest in computational models for enzyme function prediction. However, the existing computational methods are insufficient and inefficient to exploit enzyme structure. In this work, we introduce SEFP, a novel method leveraging enzyme point clouds for enzyme function prediction. The structure encoder of SEFP uses a tailored enzyme point cloud network to analyze the three-dimensional arrangement of atoms within the enzyme, integrating hierarchical residue global features through a residue feature adapter to extract detailed enzyme point features. Additionally, the Bio-BCS residue feature encoder extracts enzyme residue features with channel and spatial weights using a specially designed attention mechanism. Finally, SEFP fuses point and residue features to generate the final prediction results. Comparative evaluations show that SEFP outperforms various recent computational methods, demonstrating superior performance. On the RSCB enzyme structure dataset, SEFP achieves an f1-score of 95.85, outperforming two representative structure-based methods, EnzyNet and DeepFri. On the HECNet dataset, SEFP maintains its superiority over all comparison sequence-based methods, yielding an f1-score of 94.29. Ablation studies are conducted to confirm the effectiveness of individual modules within SEFP. These findings underscore the potential of SEFP for reliable and precise enzyme function prediction, offering advancements in bioinformatics and computational biology.

关键词： Enzymes Feature extraction Point cloud compression Training Amino acids Databases Annotations Predictive models bioinformatics Three-dimensional displays

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15th Brazilian symposium on bioinformatics on Advances in bioinformatics and computational biology, BSB 2022

15th Brazilian Symposium on Bioinformatics on Advances in Bi...

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15th Brazilian symposium on bioinformatics on Advances in bioinformatics and computational biology, BSB 2022

ISBN: (纸本)9783031211744

The proceedings contain 17 papers. The special focus in this conference is on bioinformatics on Advances in bioinformatics and computational biology. The topics include: A Non Exhaustive Search of Exhaustiveness;search for Zinc Complexes with High Affinity in Pyrazinamidase from Mycobacterium Tuberculosis Resistant to Pyrazinamide;how bioinformatics Can Aid Biodiversity Description: The Case of a Probable New Species of Orthonychiurus (Collembola, Hexapoda);phylogeny Trees as a Tool to Compare Inference Algorithms of Orthologs;water Pollution Shifts the Soil and Fish Gut Microbiomes Increasing the Circulation of Antibiotic Resistance Genes in the Environment;a 1.375-Approximation Algorithm for Sorting by Transpositions with Faster Running Time;in Silico Analysis of the Genomic Potential for the Production of Specialized Metabolites of Ten Strains of the Bacillales Order Isolated from the Soil of the Federal District, Brazil;scientific Workflow Interactions: An Application to Cancer Gene Identification;Accuracy of RNA Structure Prediction Depends on the Pseudoknot Grammar;comparison of Machine Learning Pipelines for Gene Expression Matrices;evaluating Machine Learning Models for Essential Protein Identification;study on the Complexity of Omics Data: An Analysis for Cancer Survival Prediction;identifying Large Scale Conformational Changes in Proteins Through Distance Maps and Convolutional Networks;clustering Analysis Indicates Genes Involved in Progesterone-Induced Oxidative Stress in Pancreatic Beta Cells: Insights to Understanding Gestational Diabetes;an External Memory Approach for Large Genome De Novo Assembly.

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RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery 9

RL-MD: A Novel Reinforcement Learning Approach for DNA Motif...

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9th ieee International Conference on Data Science and Advanced Analytics (DSAA)

作者： Wang, Wen Wang, Jianzong Si, Shijing Huang, Zhangcheng Xiao, Jing Ping An Technol Shenzhen Co Ltd Shenzhen Peoples R China Shanghai Int Studies Univ Sch Econ & Finance Shanghai Peoples R China

ISBN: (纸本)9781665473309

The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology. Several deep learning-based techniques have recently been introduced to address this issue. However, these algorithms can not be used in real-world situations because of the need for labeled data. Here, we presented RL-MD, a novel reinforcement learning based approach for DNA motif discovery task. RL-MD takes unlabelled data as input, employs a relative informationbased method to evaluate each proposed motif, and utilizes these continuous evaluation results as the reward. The experiments show that RL-MD can identify high-quality motifs in real-world data.

关键词： Biomedical AI Reinforcement Learning computational biology bioinformatics DNA Motif Discovery

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An Oscillator Model for Interbrain Synchrony: Slow Interactional Rhythms Entrain Fast Neural Activity

An Oscillator Model for Interbrain Synchrony: Slow Interacti...

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18th ieee International Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Loh, Chen Lam Froese, Tom Grad Univ Okinawa Inst Sci & Technol Embodied Cognit Sci Unit Onna Son Japan

ISBN: (纸本)9781665401128

Synchronization is a self-organizing process spanning multiple levels of organization. With advances in brain imaging and hyperscanning technologies, the pervasiveness of interbrain neural synchrony in widely different experimental settings is slowly attracting attention in the field. Despite its prevalence, the underlying mechanisms for interbrain neural synchrony remains largely unexplained, with recent interpretations attempting to approach the problem from the theory of mind, mentalization and shared intentionality perspective. One highlighted issue regarding the difficulty in providing a sound explanation is that neural activities are occurring at orders-of-magnitude faster timescales than the social interaction itself. Using two minimal evolutionary models based on Kuramoto coupled oscillators, we provide an alternative perspective as to how interbrain neural synchronization might occur. Borrowing concepts from recent hypotheses, we argue that 1) embodied engagements form a new autonomous "interaction" system, which 2) through its slower dynamics exert constraints on the faster neural dynamics within the heads. If this is true, the key to understanding interbrain neural synchrony could be through understanding the nature of the interaction itself.

关键词： interbrain synchrony computational modelling social interaction

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Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs

Comprehensive Modeling and Question Answering of Cancer Clin...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Bhumika Gupta Pralaypati Ta Keerthi Ram Mohanasankar Sivaprakasam Department of Electrical Engineering Indian Institute of Technology Madras Chennai India Healthcare Technology Innovation Centre Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

The updated recommendations on diagnostic procedures and treatment pathways for a medical condition are documented as graphical flows in Clinical Practice Guidelines (CPGs). For effective use of the CPGs in helping medical professionals in the treatment decision process, it is necessary to fully capture the guideline knowledge, particularly the contexts and their relationships in the graph. While several existing works have utilized these guidelines to create a rule base for Clinical Decision Support Systems, limited work has been done toward directly capturing the full medical knowledge contained in CPGs. This work proposes an approach to create a contextually enriched, faithful digital representation of National Comprehensive Cancer Network (NCCN) Cancer CPGs in the form of graphs using automated extraction and node & relationship classification. We also implement semantic enrichment of the model by using Large Language Models (LLMs) for node classification, achieving an accuracy of 80.86% and 88.47% with zero-shot learning and few-shot learning, respectively. Additionally, we introduce a methodology for answering natural language questions with constraints based on guideline text by leveraging LLMs to extract the relevant subgraph from the guideline knowledge base. By generating natural language answers based on subgraph paths and semantic information, we mitigate the risk of incorrect answers and hallucinations associated with LLMs, ensuring factual accuracy in medical domain question answering.

关键词： Accuracy computational modeling Biological system modeling Natural languages Semantics Knowledge based systems Question answering (information retrieval) Medical diagnostic imaging Guidelines Cancer

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