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Guest Editorial for Special Section on the 15th International conference on Intelligent Computing (ICIC)

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1730-1732页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, M. Michael Tongji Univ Inst Machine Learning & Syst Biol Shanghai 200092 Peoples R China Polytech Univ Bari Elect & Informat Engn Dept I-70126 Bari Italy Indian Inst Technol IIT Madras Dept Biotechnol Chennai 600036 Tamil Nadu India

The papers in this special section were presented at the Fifteenth International conference on Intelligent Computing (ICIC) held on August 3-6, 2019, in Nanchang, Jiangxi Province, China.

关键词： Special issues and sections Meetings bioinformatics computational biolody

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Guest Editorial: Machine Learning for AI-Enhanced Healthcare and Medical Services: New Development and Promising Solution

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 850-851页

作者： Yan, Ke Ji, Zhiwei Jin, Qun Wang, Qing-Guo China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China Natl Univ Singapore Singapore 117566 Singapore Nanjing Agr Univ Coll Artificial intelligence Nanjing 210095 Jiangsu Peoples R China Waseda Univ Dept Human Informat & Cognit Sci Tokorozawa Saitama 3591192 Japan Beijing Normal Univ Hong Kong Baptist Univ United Int Coll UIC Tangjiawan 519087 Zhuhai Peoples R China

The papers in this special section focus on machine learning for artificial intelligent-enhances healthcare and medical services. These services are always among the top concerns for humans, especially under the special situation of COVID-19 pandemic, started from early 2020. In the field of computational biology and bioinformatics, scientists seek various possibilities using computer technologies, especially artificial intelligence (AI) enhanced methods, for healthcare services and medical diagnoses. For example, over the past few years, scientists have been working hard to identify the internal relationships between gene microarrays, cells, tissues, organisms, diseases, etc., and apply the AI, machine learning and deep learning technologies looking for more innovative solutions for new diseases, such as COVID-19. In fact, nowadays, AI technology, such as the convolutional neural network (CNN), is considered has one of the most important computer technologies and has been widely applied in the fields of healthcare engineering, medical research, disease diagnosis, cancer/tumor analysis and etc.

关键词： Special Issues And Sections Machine Learning Medical Services Artificial intelligence computational biology bioinformatics COVID 19 Pandemics Medical Diagnostic Imaging Medical Treatment

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An Overview of Pre-Trained Graph Models for Molecular Structure Analysis

An Overview of Pre-Trained Graph Models for Molecular Struct...

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2024 ieee International conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Hoang, Van Thuy Lee, O-Joun The Catholic University of Korea Dept. of Artificial Intelligence Bucheon Korea Republic of

ISBN: (纸本)9798331530839

Molecules are naturally represented as graphs, which benefit graph models in understanding molecular structures to capture complex relationships and interactions between atoms. However, the limitations of labeled molecules can significantly impact the graph model's ability to generalize different molecular properties and structures, leading to poor performance. Lately, pretraining Graph Neural Networks (GNNs) have emerged as a powerful tool for learning molecular representations, addressing the challenge due to the lack of labeled molec-ular data. This study introduces and examines the pretraining GNNs strategies in learning molecular structure in computational chemistry and biology. We first categorize recent pretraining existing studies into three main groups: node-level strategies, contrastive learning, and graph-level strategies. Furthermore, we also highlight recent studies that significantly enhance model interpretability and prediction accuracy. This comprehensive perspective on the current state and future prospects of GNNs underscores their pivotal role in accelerating drug discovery and material innovation through advanced AI-driven analysis. © 2024 ieee.

关键词： Graph neural networks

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HierMRL: Hierarchical Structure-Aware Molecular Representation Learning for Property Prediction

HierMRL: Hierarchical Structure-Aware Molecular Representati...

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2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Maotao Yang, Yifan Gong, Xu Liu, Li Liu, Qun Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing China Tencent Shenzhen China

ISBN: (纸本)9781665468190

Deep graph neural networks have recently demonstrated powerful representation learning capabilities in bioinformatics. It is still crucial and challenging to design a representation model fusing the fundamental chemistry and biology knowledge. However, most existing representation models not only ignore domain knowledge but are also built on labeled datasets. To address this issue, this paper proposed a hierarchical structure-aware pre-training model that used contrastive learning to improve molecular representations with unlabeled datasets. We conducted comprehensive experiments on 13 molecular benchmark datasets from different application domains. The results demonstrate that our hierarchical structure-aware pre-trained model achieves superior performance against state-of-the-art baselines. © 2022 ieee.

关键词： Graph neural networks

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Application of bioinformatics in Animal Breeding and Genetics: A Review

Application of Bioinformatics in Animal Breeding and Genetic...

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2024 International conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adebayo, Oluwaseyi Modupe Popoola, Mustapha Ayo Kuusu, Doorumun Jacob Fanwo, Roseline Ruth Shoyombo, Ayoola John Ndiomu, Ebiogeh Philip Egbeyan, Jubril A Moses, Ake A. Landmark University Department of Animal Science Nigeria Kogi State Polytechnic Department of Animal Health and Production Technology Lokoja Nigeria Tertiary Education Trust Fund Department of Research and Development Abuja Nigeria Landmark University Department of Animal Science Omu-Aran Nigeria University of Abuja Department of Animal Science Abuja Nigeria James Cook University College of Science and Engineering Australia

ISBN: (纸本)9798350358155

The field of bioinformatics, which serves as a link between biology and computer science, is enabling substantial progress in animal genetics and breeding initiatives. This study explores the significant impact of bioinformatics methodologies on revolutionizing strategies for improving livestock genetics. Genomic selection employs advanced DNA sequencing and statistical algorithms to predict genomic breeding values, thereby accelerating genetic progress in complex traits such as growth and reproduction. Phylogenomic methodologies employ genomic markers to delineate the evolutionary relationships between species, thereby revealing genetic variations that facilitate adaptation to diverse environments. Bioinformatic analyses play a crucial role in identifying the genes that underlie interactions between hosts and pathogens, as well as mechanisms of disease resilience, thereby facilitating marker-assisted selection. The emerging discipline of nutrigenomics employs transcriptomics and metabolomics to unravel how dietary nutrients influence gene expression and metabolism, thereby providing valuable insights into precision animal nutrition. Innovative vaccine development incorporates computational methods from vaccinology, which leverage immune gene expression profiling. By integrating genome-wide association studies (GWAS) with quantitative genetics, scientists have identified genetic loci and polymorphisms that regulate economically significant animal phenotypes such as productivity, fertility, and welfare. Collectively, bioinformatics is spearheading a paradigm shift by applying big data-driven approaches to strategically enhance genetic improvement and ensure the sustainability of production across farm animal populations. This is of utmost importance in the face of climate uncertainties, to ensure food security. © 2024 ieee.

关键词： Livestock

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The Intrinsic Similarity of Topological Structure in Biological Neural Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 3292-3305页

作者： Zhao, Hongfei Shao, Cunqi Shi, Zhiguo He, Shibo Gong, Zhefeng Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Key Lab Collaborat Sensing & Autonomous Unmanned S Hangzhou 310058 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ NHC Hangzhou 310058 Peoples R China Zhejiang Univ CAMS Key Lab Med Neurobiol Hangzhou 310058 Peoples R China Zhejiang Univ MOE Frontier Sci Ctr Brain Sci & Brain machine Int Liangzhu Lab State Key Lab Brain Machine Intelligence Hangzhou 311121 Peoples R China Zhejiang Univ Affiliated Hosp 4 Dept Neurobiol Sch Med Hangzhou 310058 Peoples R China Zhejiang Univ Dept Neurol Affiliated Hosp 4 Sch Med Hangzhou 310058 Peoples R China

Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.

关键词： Biological neural networks Neurons biology Synapses Mice Visual systems Optical fiber networks small-world properties truncated power-law distribution log-normal degree distribution network motifs

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A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems

A Fast Feature Selection for Interpretable Modeling Based on...

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

作者： Andrea Tangherloni Paolo Cazzaniga Nicolò Stranieri Francesca M. Buffa Marco S. Nobile Department of Computing Sciences Bocconi Institute for Data Science and Analytics Bocconi University Milan Italy Department of Human and Social Sciences University of Bergamo Bergamo Italy Department of Computing Sciences Bocconi University Milan Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Large datasets are often beneficial for the generation of predictive models using machine learning approaches. However, it is often the case that not all variables in the dataset contain useful information. In fact, some variables might be useless, redundant, misleading, or even harmful to performance, both in terms of accuracy and computational effort. Because of that, Feature Selection (FS) is one of the most delicate and important steps in machine learning. This is even more relevant in the case of interpretable models based on Fuzzy Inference Systems (FIS). The reasons are two-fold: on the one hand, FIS are generally built on top of a data partitioning based on clustering, which can suffer from high dimensionality; on the other hand, the knowledge base of the FIS, to be concretely understandable, should not contain rules involving too many variables. FS can be performed using multiple approaches, most notably filter and wrapper methods. The latter are often based on evolutionary algorithms, where a population of candidate solutions (each representing a possible set of selected variables) evolves towards the optimal selection. Although wrapper methods can be effective, they are, in general, computationally expensive. In this work, we propose a completely different – and more computationally effective – algorithm based on Random Forest (RF) models. Specifically, we exploit RFs to rank variables according to their importance. Then, we use that information to perform a statistical analysis and determine the minimal set of features necessary to build an accurate FIS. We show the effectiveness of our approach by using two (semi)synthetic datasets built on real-world datasets, and we validate our approach by applying the FS method to a medical dataset.

关键词： Radio frequency Fuzzy logic Accuracy Statistical analysis computational modeling Predictive models Feature extraction Inference algorithms Noise measurement Random forests

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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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Does Meta-Learning Improve EEG Motor Imagery Classification? 44

Does Meta-Learning Improve EEG Motor Imagery Classification?

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-biology-Society (EMBC)

作者： Wu, Xiaoli Chan, Rosa H. M. City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Deep learning has been applied to enhance the performance of EEG-based brain-computer interface applications. However, the cross-subject variations in EEG signals cause domain shifts and negatively affect the model performance and generalization. Meta-learning algorithms have shown fast new domain adaption in various fields, which may help solve the domain shift problems in EEG. Reptile, with satisfactory performance and low computational costs, stands out from other existing meta-learning algorithms. We integrated Reptile with a deep neural network as Reptile-EEG for the EEG motor imagery tasks, and compared Reptile-EEG with other state-of-the-art models in three motor imagery BCI benchmark datasets. Results show that Reptile-EEG does not outperform simple training of deep neural networks in motor imagery BCI tasks.

关键词： Deep learning Training Correlation coefficient computational modeling Transfer learning Neural networks Brain modeling

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Guest Editorial for Selected Papers From ACM-BCB 2019

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第4期19卷 1919-1919页

作者： Ma, Jian Carnegie Mellon Univ Sch Comp Sci Computat Biol Dept Pittsburgh PA 15213 USA

The papers in this special issue were presented at the ACM conference on bioinformatics, computational biology, and Health Informatics (ACM-BCB) that was held in Niagara Falls, NY, on September 7-10, 2019. The conference continued the main focus of fostering scientific interactions of important research questions at the interface of computer science, statistics, biology, biomedicine, and health informatics.

关键词： Special issues and sections Meetings bioinformatics computational biology

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