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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Wentao Cui Shoubo Li Chen Fang Qingqing Long Chengrui Wang Xuezhi Wang Yuanchun Zhou Computer Network Information Center Chinese Academy of Sciences China University of Chinese Academy of Sciences China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences China State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology Chinese Academy of Sciences China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Discovering gene-disease associations is crucial for understanding disease mechanisms, yet identifying these associations remains challenging due to the time and cost of biological experiments. computational methods are increasingly vital for efficient and scalable gene-disease association prediction. Graph-based learning models, which leverage node features and network relationships, are commonly employed for biomolecular predictions. However, existing methods often struggle to effectively integrate node features, heterogeneous structures, and semantic information. To address these challenges, we propose COmprehensive MEtapath-based heterogeneous graph Transformer(COMET) for predicting gene-disease associations. COMET integrates diverse datasets to construct comprehensive heterogeneous networks, initializing node features with BioGPT. We define seven Metapaths and utilize a transformer framework to aggregate Metapath instances, capturing global contexts and long-distance dependencies. Through intra- and inter-metapath aggregation using attention mechanisms, COMET fuses latent vectors from multiple Metapaths to enhance GDA prediction accuracy. Our method demonstrates superior robustness compared to state-of-the-art approaches. Ablation studies and visualizations validate COMET’s effectiveness, providing valuable insights for advancing human health research.

关键词： Comets Visualization Attention mechanisms Biological system modeling Semantics Transformers Vectors Robustness bioinformatics Diseases

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Convolutional Classification of Pathogenicity in H5 Avian Influenza Strains

Convolutional Classification of Pathogenicity in H5 Avian In...

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International conference on Machine Learning and Applications (ICMLA)

作者： Akshay Chadha Rozita Dara Zvonimir Poljak School of Computational Science University of Guelph Guelph Canada School of Computer Science University of Guelph Guelph Canada Dept. of Population Medicine University of Guelph Guelph Canada

Epidemic Avian Influenza (AI) outbreaks pose a considerable threat to poultry and human health. Millions of birds died, or had to be euthanized, during numerous periodic highly pathogenic influenza outbreaks across the globe over the last two decades. This paper presents a study on pathogenicity classification of H5 AI protein sequences using convolutional neural network. We first collected pathogenicity identification labels of 2137 H5 AI sequences and classified pathogenicity of aligned protein sequences with 99.20% mean accuracy using 10-fold cross validation. We also identified the positions in genomic sequences which are supposed to play a role in pathogenicity classification of H5 strains. The positions learned by convolutional network correctly included positions of known pathogenicity markers present at cleavage site of protein sequences. And the network also identified a few other positions present outside of cleavage site which are supposed to play a role in pathogenicity classification of H5 strains.

关键词： Strain bioinformatics Genomics Artificial intelligence Kernel Proteins Influenza

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Assessment of the Binding Characteristics of Human Immunodeficiency Virus Type 1 Glycoprotein120 and Host Cluster of Differentiation4 Using Digital Signal Processing

Assessment of the Binding Characteristics of Human Immunodef...

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ieee Symposium on bioinformatics and Bioengineering (BIBE)

作者： Norbert Nwankwo Huseyin Seker Centre of Computational Intelligence Department of Informatics Faculty of Technology De Montfort University Leicester UK

Protein residues which contribute to bio-recognition and binding interaction between HIV Surface protein also called Glycoprotein120 (gp120) and Cluster of Differentiation4 (CD4) have been identified. However, this was with limited number of isolates. Notwithstanding, the particular HIV isolate that harbors the gp120 with the greatest binding force to the CD4 has not been investigated. In this paper, protein sequences of gp120 from 43 HIV-1 isolates, 5 isolates each from the HIV-2 and SIV as well as the CD4 of 25 HIV host organisms were analyzed using Resonant Recognition Method (RMM). The results re-confirmed that protein sequences of the HIV and CD4 share common spectral features in relation to bio-recognition and binding. From the large dataset of the HIV and SIV isolates used, MFA group M subtype B (HIV-1) isolate was found to have the greatest affinity for the CD4. Furthermore, the CD4 of the human and chimpanzee were established to possess about same level of binding force to the HIV gp120. Also the CD4 of other species offered more attractive force to another protein in such a manner that the approach taken in this study has also shown to be a useful and reliable tool for clear categorization of species. Finally, clinically experimented results were found to correlate with the computationally obtained results as the gp120 of the HIV-2 and SIV which were recognized to circumvent the CD4 during infection were found to have low peak amplitude. This low peak amplitude observed in the HIV-2 and SIV implies that they have weak affinity or attraction for the host CD4.

关键词： Human immunodeficiency virus Digital signal processing Proteins Sequences Amino acids Organisms Resonance Informatics Peptides Drugs

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Using Label-text Correlation and Deviation Punishment for Fine-grained Suicide Risk Detection in Social Media

Using Label-text Correlation and Deviation Punishment for Fi...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Zepeng Li Zhengyi An Wenchuan Cheng Jiawei Zhou Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chines Academy of Sciences Shanghai China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Suicide causes serious harm to individuals, families and society, and becomes a social problem of widespread concern. Therefore, it is necessary to find and intervene individuals at risk of suicide as soon as possible. In recent years, social media data has successfully been leveraged for suicide risk detection. However, for fine-grained suicide risk detection, the existing models ignore the deviation between the predicted results and the real results when making wrong predictions, and do not pay attention to the semantic information contained in the labels. This paper proposes a deep learning model based on Label-Text Correlation and Deviation Punishment (LTC-DP). While learning the semantic relation adequately between the text and the corresponding label, the model can give different punishment adaptively according to the deviation degrees between the predicted results and the real result. The experimental results show that compared with the baseline model, the proposed model has better performance in fine-grained suicide risk detection. In addition, we release a fine-grained suicide risk detection data set based on Weibo, the data set is available at https://***/cxyazy/FGCSD-main.

关键词： Training Deep learning Adaptation models Correlation Social networking (online) computational modeling Biological system modeling

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Getting 'phi psi chi al' with proteins: minimum message length inference of joint distributions of backbone and sidechain dihedral angles

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bioinformatics 2023年第Supplement_1期39卷 I357-I367页

作者： Amarasinghe, Piyumi R. Allison, Lloyd Stuckey, Peter J. de la Banda, Maria Garcia Lesk, Arthur M. Konagurthu, Arun S. Monash Univ Fac Informat Technol Dept Data Sci & Artificial Intelligence Clayton Vic 3800 Australia OPTIMA ARC Ind Training & Transformat Ctr Carlton Vic 3053 Australia Penn State Univ Dept Biochem & Mol Biol University Pk PA 16802 USA

The tendency of an amino acid to adopt certain configurations in folded proteins is treated here as a statistical estimation problem. We model the joint distribution of the observed mainchain and sidechain dihedral angles () of any amino acid by a mixture of a product of von Mises probability distributions. This mixture model maps any vector of dihedral angles to a point on a multi-dimensional torus. The continuous space it uses to specify the dihedral angles provides an alternative to the commonly used rotamer libraries. These rotamer libraries discretize the space of dihedral angles into coarse angular bins, and cluster combinations of sidechain dihedral angles () as a function of backbone conformations. A `good' model is one that is both concise and explains (compresses) observed data. Competing models can be compared directly and in particular our model is shown to outperform the Dunbrack rotamer library in terms of model complexity (by three orders of magnitude) and its fidelity (on average 20% more compression) when losslessly explaining the observed dihedral angle data across experimental resolutions of structures. Our method is unsupervised (with parameters estimated automatically) and uses information theory to determine the optimal complexity of the statistical model, thus avoiding under/over-fitting, a common pitfall in model selection problems. Our models are computationally inexpensive to sample from and are geared to support a number of downstream studies, ranging from experimental structure refinement, de novo protein design, and protein structure prediction. We call our collection of mixture models as PhiSiCal (phi psi chi al).

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Towards cognitive analysis of DNA

Towards cognitive analysis of DNA

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ieee International conference on Cognitive Informatics

作者： Witold Kinsner Experimental Media Research Group Telecommunications Research Laboratories Institute of Industrial Mathematical Sciences Canada Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

Summary form only given. Deoxyribonucleic acid (DNA) has become one of the most examined molecules on the planet. Scientist around the world have been trying to unravel its secrets for many purposes. For example, genetic information is currently used to raise better plants and animals, create enhanced pharmaceuticals for humans, and for gene therapy in medicine. Science as a whole has benefited from the study of genetics because of the increased understanding of biological process that all organisms share. In recent decades, a significant amount of research has been directed towards sequencing and understanding the entire human genome through the Human Genome Project (HGP) launched in 1986. The goal of the HGP was to find the location of the approximately 1×105 human genes, and read all the sequence of human genome (about 3×109 base pairs, bp). An exponential grow rate of that research has resulted in reaching the goal by 2003. Similarly, the speed of finding genes and their locations is also increasing rapidly. On the other hand, the traditional methods of finding genes and their location at chromatosomes through testing their biological function have been inherently slow. Although numerous faster techniques have been developed, there is still a need to augment them with new approaches. Therefore, robust computational solutions to the gene-finding problem could provide a valuable resource for the HGP and for the molecular-biology community. Most of the current research in the deciphering the meaning of DNA sequences is approached from the lowest base-pair level. Its main objective is to search for patterns or correlations existing in the DNA sequence related to codons, amino acids, and proteins. A number of gene-finding systems have been developed in recent decades. These systems use a variety of sophisticated computational data-miming techniques, including neural networks, dynamic programming, rule-based methods, decision trees, probability reasoning, hidden Marko

关键词： DNA Fractals bioinformatics Cognitive informatics Computers Genomics Humans

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Multi-scale Strategy Based 3D Dual-Encoder Brain Tumor Segmentation Network with Attention Mechanism

Multi-scale Strategy Based 3D Dual-Encoder Brain Tumor Segme...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yazhou Zhu Xiang Pan Jing Zhu Lihua Li School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China College of Life Information Science and Instrument Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Magnetic resonance imaging (MRI) is a widely used diagnostic technique in the initial evaluation of patients with primary brain tumor. Automatic segmentation algorithms for brain tumor plays an important role in diagnosis of tumor subregions and treatments for patients. In this paper, we propose a novel 3D convolutional neural network for segmentation of brain tumor, which is based on the traditional encoder-decoder architecture and inspired by multi-scale strategy. The proposed network employs multi-scale strategy with the designed dualencoder structure which can be considered as two scales streams to extract features from two scales inputs respectively. To integrate the output features from two scales encoders, a multiscale attention is model designed to weight and integrated the multi-scale features. Moreover, with the purpose of modeling long-range feature dependencies more efficiently, hidden features from different positions of stream are also fused and then utilized by attention mechanism in each scale encoder. To validate the performance of the proposed network, we conduct experiments on the brain tumor segmentation dataset BraTS2019 against several state-of-art methods. The results show that proposed method has the effective and competitive performance in brain tumor segmentation.

关键词： Feature extraction Convolution Three-dimensional displays Image segmentation Tumors computational modeling Decoding

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Face recognition from one example view

Face recognition from one example view

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International conference on Computer Vision (ICCV)

作者： D. Beymer T. Poggio Artifical Intelligence Laboratory and Center for Biological and Computational Learning Massachusetts Institute of Technology Cambridge MA USA

To create a pose-invariant face recognizer, one strategy is the view-based approach, which uses a set of real example views at different poses. But what if we only have one real view available, such as a scanned passport photo-can we still recognize faces under different poses? Given one real view at a known pose, it is still possible to use the view-based approach by exploiting prior knowledge of faces to generate virtual views, or views of the face as seen from different poses. To represent prior knowledge, we use 2D example views of prototype faces under different rotations. We develop example-based techniques for applying the rotation seen in the prototypes to essentially "rotate" the single real view which is available. Next, the combined set of one real and multiple virtual views is used as example views for a view-based, pose-invariant face recognizer. Oar experiments suggest that among the techniques for expressing prior knowledge of faces, 2D example-based approaches should be considered alongside the more standard 3D modeling techniques.< >

关键词： Face recognition Face detection Laboratories Prototypes Artificial intelligence biology computing Lighting Humans Computer graphics Learning

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Enhancing Accuracy of Mental Fatigue Classification using Advanced computational intelligence in an Electroencephalography System

Enhancing Accuracy of Mental Fatigue Classification using Ad...

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Annual International conference of the ieee Engineering in Medicine and biology Society

作者： Rifai Chai Yvonne Tran Ashley Craig Sai Ho Ling Hung T. Nguyen Key Centre for Health Technologies Faculty of Engineering and Information Technology University of Technology Key Centre for Health Technologies University of Technology Rehabilitation Studies Unit Sydney Medical School The University of Sydney

ISBN: (纸本)9781424479276

A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.

关键词： Fatigue Electroencephalography Accuracy Sensitivity Feature extraction Artificial neural networks Brain modeling

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Prediction of skin ages by means of multi-spectral light sources

Prediction of skin ages by means of multi-spectral light sou...

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Annual International conference of the ieee Engineering in Medicine and biology Society

作者： H. Seker V. Uslan A. B. Orun E. Goodyer G. Smith Bio-Health Informatics Research Group within the Centre for Computational Intelligence De Montfort University

ISBN: (纸本)9781424479276

Assessment of skin aging is a complex biological process that depends on various internal and external factors but has become important due to personalized skin health and cosmetic treatments. Although there are a small number of attempts to assess the skin aging, they identify only one of the previously classified skin aging groups. The methods used to achieve it are also based generally on highly expensive measurement devices. This work therefore proposes novel low-cost skin aging assessment apparatus by using light back-scatter intensity level of Red, Blue, Green and Infrared bands. This is further enhanced by using a machine learning method to accurately predict actual skin age. The proposed method appears to be highly capable of capturing multi-layer cellular changes exhibited by the biological skin aging process and predicting skin ages with a root-mean-square error of as low as 0.160 by using only four features based on the four multi-spectral light sources. This assessment kit seems to be the first of its kind, which is expected to bring great benefit to both personalized skin healthcare and cosmetic sectors.

关键词： Skin Kernel Aging Predictive models Support vector machines Light sources Optimization

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