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46th Annual International conference of the ieee Engineering in Medicine and biology Society, EMBC 2024

作者： Ahmed, Md Ashfaq Jung, Ranu Baptist Health South Florida TD-Artificial Intelligence MiamiFL United States FayettevilleAR United States

ISBN: (纸本)9798350371499

A physiologically based computational framework was used to develop a novel gastric motility network model. Along with the widely assumed electrical gap junction coupling between the pacemaker and muscle cells as well as the second messenger molecules, in this model we also included a rostro-caudal linear decreasing gradient of neural stimulus to the pacemakers along the length of the stomach to mimic enteric neurotransmitter release and an increasing gradient of muscarinic receptor density. We find that aberrant responses of different parts of the stomach (corpus, antrum etc.) to exogenous application of excitatory neurotransmitters such as Acetylcholine, can be explained by an interplay between the level of neurotransmitter and density of receptors available. This model can also explain anomalous behavior such as retrograde propagation and functional uncoupling in the entire stomach in response to external neuro-agonists. © 2024 ieee.

关键词： Pacemakers

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Generating Models of Human Gait in Patients with Parkinson’s Disease using Genetic Programming

Generating Models of Human Gait in Patients with Parkinson’...

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

作者： Tristan Navikevicius Lígia Reis Nóbrega Sheridan Houghten Adriano de Oliveira Andrade Adriano Alves Pereira Department of Computer Science Brock University St. Catharines Canada Faculty of Electrical Engineering Federal University of Uberlândia Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Parkinson’s disease is an extremely debilitating condition where the brain is not producing enough dopamine to accurately coordinate movement. One symptom of Parkinson’s disease, freezing of gait, prevents the affected person from either starting to walk or continuing walking. We use a dataset that provides time-series data of volunteers’ gait while performing four different tasks of varying complexity. Symbolic regression through genetic programming is applied to the dataset to create models of the gait of volunteers with and without Parkinson’s disease, including those who may be experiencing freezing of gait, while also factoring in their medication status (ON or OFF). Comparing the gait of volunteers in the different groups, the methodology produced models that were most accurate for the group of PD patients with freezing of gait when off their medication applied to other volunteers in the same group. It was also found that for some volunteers it was not possible to distinguish between ON and OFF medication states, providing a possible indication of issues with their medication.

关键词： Legged locomotion Accuracy computational modeling Biological system modeling Genetic programming Complexity theory bioinformatics computational intelligence Diseases computational biology

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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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A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第4期21卷 912-923页

作者： Wang, Xingbo Li, Shujuan Pun, Chi-Man Guo, Yijing Xu, Feng Gao, Hao Lu, Huimin Nanjing Univ Posts & Commun Coll Automat Nanjing 210049 Peoples R China Nanjing Univ Posts & Commun Coll Artificial Intelligence Nanjing 210049 Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou 311100 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Southeast Univ Dept Neurol Zhongda Hosp Nanjing 210009 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu 8048550 Japan

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.

关键词： Feature extraction Deep learning Residual neural networks Neural networks Signal processing algorithms Diseases Data models Artificial bee colony algorithm deep learning hyperparameter optimization parkinson's auxiliary speech diagnosis

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Predicting Drug-Target Interaction Via Self-Supervised Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 2781-2789页

作者： Chen, Jiatao Zhang, Liang Cheng, Ke Jin, Bo Lu, Xinjiang Che, Chao Dalian Univ Key Lab Adv Design & Intelligent Comp Minist Educ Dalian 116622 Peoples R China Dongbei Univ Finance & Econ Int Business Coll Dalian 116025 Peoples R China Dalian Univ Technol Coll Comp Sci & Technol Dalian 116024 Peoples R China Baidu Res Business Intelligence Lab Beijing 100085 Peoples R China

Recent advances in graph representation learning provide new opportunities for computational drug-target interaction (DTI) prediction. However, it still suffers from deficiencies of dependence on manual labels and vulnerability to attacks. Inspired by the success of self-supervised learning (SSL) algorithms, which can leverage input data itself as supervision,we propose SupDTI, a SSL-enhanced drug-target interaction prediction framework based on a heterogeneous network (i.e., drug-protein, drug-drug, and protein-protein interaction network;drug-disease, drug-side-effect, and protein-disease association network;drug-structure and protein-sequence similarity network). Specifically, SupDTI is an end-to-end learning framework consisting of five components. First, localized and globalized graph convolutions are designed to capture the nodes' information from both local and global perspectives, respectively. Then, we develop a variational autoencoder to constrain the nodes' representation to have desired statistical characteristics. Finally, a unified self-supervised learning strategy is leveraged to enhance the nodes' representation, namely, a contrastive learning module is employed to enable the nodes' representation to fit the graph-level representation, followed by a generative learning module which further maximizes the node-level agreement across the global and local views by learning the probabilistic connectivity distribution of the original heterogeneous network. Experimental results show that our model can achieve better prediction performance than state-of-the-art methods.

关键词： Contrastive learning DTI prediction generative learning graph neural network self-supervised learning

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PACVP: Prediction of Anti-Coronavirus Peptides Using a Stacking Learning Strategy With Effective Feature Representation

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 3106-3116页

作者： Chen, Shouzhi Liao, Yanhong Zhao, Jianping Bin, Yannan Zheng, Chunhou Xinjiang Univ Coll Math & Syst Sci Urumqi 830017 Xinjiang Peoples R China Anhui Univ Inst Phys Sci Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Anhui Peoples R China Anhui Univ Inst Informat Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Anhui Univ Coll Artificial Intelligence Hefei 230601 Anhui Peoples R China

Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework. In the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. Secondly, data normalization and unbalanced data processing are carried out. Next, 12 baseline models are constructed by combining three feature selection methods and four machine learning classification algorithms. In the second layer, we input the optimal probability features into the logistic regression algorithm (LR) to train the final model PACVP. The experiments show that PACVP achieves favorable prediction performance on independent test dataset, with ACC of 0.9208 and AUC of 0.9465. We hope that PACVP will become a useful method for identifying, annotating and characterizing novel ACVPs.

关键词： Anti-coronavirus peptide feature selection feature representation machine learning stacking learning

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27th International conference on Research in computational Molecular biology, RECOMB 2023

27th International Conference on Research in Computational M...

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27th International conference on Research in computational Molecular biology, RECOMB 2023

ISBN: (纸本)9783031291180

The proceedings contain 11 papers. The special focus in this conference is on Research in computational Molecular biology. The topics include: T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy;spectrum Preserving Tilings Enable Sparse and Modular Reference Indexing;statistically Consistent Rooting of Species Trees Under the Multispecies Coalescent Model;sequence to Graph Alignment Using Gap-Sensitive Co-linear Chaining;DM-Net: A Dual-Model Network for Automated Biomedical Image Diagnosis;MTGL-ADMET: A Novel Multi-task Graph Learning Framework for ADMET Prediction Enhanced by Status-Theory and Maximum Flow;CDGCN: Conditional de novo Drug Generative Model Using Graph Convolution Networks;Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response;translation Rate Prediction and Regulatory Motif Discovery with Multi-task Learning.

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A Comparison of Machine Learning Models with Data Augmentation Techniques for Skeleton-based Human Action Recognition 23

A Comparison of Machine Learning Models with Data Augmentati...

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14th ACM conference on bioinformatics, computational biology, and Health Informatics (ACM-BCB)

作者： Xin, Chu Kim, Seokhwan Park, Kyoung Shin Dankook Univ Grad Sch Dept Artificial Intelligence Convergence 152 Jukjeon Ro Yongin 16890 Gyeonggi Do South Korea Farmkit R&D Ctr 50255 Heungdeokjooang Ro Yongin 16953 Gyeonggi Do South Korea Dankook Univ Coll SW Convergence Dept Comp Engn 152 Jukjeon Ro Yongin 16890 Gyeonggi Do South Korea

ISBN: (纸本)9798400701269

3D skeleton motion recognition plays a crucial role in the field of human action recognition (HAR) due to its efficiency and reliability. This paper introduces data augmentation techniques applied to skeleton data with the aim of improving the accuracy of machine learning models and examining the effectiveness of different augmentation methods for different activities. Spatial transformations are applied to generate augmented samples from the original 3D skeleton sequences, while temporal augmentation techniques are employed to capture the temporal differences in motion. We evaluated the effects of spatial and temporal data augmentation at different levels on the MSR Daily Activity 3D dataset using SVM, CNN, LSTM, and CNNLSTM models. The results show that temporal augmentation significantly improves model performance, while spatial augmentation has only a limited effect on model performance. Simultaneously applying both spatial and temporal augmentation further improved model performance, highlighting the importance of temporal information in action sequence data.

关键词： Data augmentation Skeleton-based Human Action Recognition SVM CNN LSTM CNNLSTM

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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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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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