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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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An Approach for Modeling and Analysis of Insulin Biosynthesis using Petri Nets

An Approach for Modeling and Analysis of Insulin Biosynthesi...

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

作者： Maria Leandra Guateque Antonio Basilio de Miranda Marcos Catanho Edward H. Haeusler Sérgio Lifschitz Departamento de Informatica PUC-Rio Rio de Janeiro Brazil Instituto Oswaldo Cruz FIOCRUZ Rio de Janeiro Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Insulin synthesis is a complex and essential biological process involving a series of interconnected subprocesses. We present a model of the translational process and post-translational modifications occurring in insulin biosynthesis. Using Colored Petri Nets (CPNs), we accurately represent sets of interactions among components such as amino acids, polypeptide chains, and other cellular compounds. This CPN framework captures the intricate state changes within biological processes and establishes a theoretical foundation for modeling and analyzing other critical biological processes.

关键词： Analytical models Biological system modeling computational modeling Petri nets Biological processes Process control Amino acids Regulation Insulin In vitro

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Guest Editorial for Selected Papers From BIOKDD 2021

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3068-3069页

作者： Yan, Da Qin, Zhaohui S. Bhattacharya, Debswapna Chen, Jake Y. Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Emory Univ Dept Biostat & Bioinformat Atlanta GA 30322 USA Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Univ Alabama Birmingham Inst Informat Birmingham AL 35294 USA

The papers in this special section were presented at the 20th International Workshop on Data Mining in bioinformatics (BIOKDD 2021) that was held virtually on August 15, 2021. The conference featured the special theme of "Artificial intelligence in Medicine" which particularly welcomed paper submissions and invited talks related to the use of machine learning and data mining techniques for the analysis of large amounts of heterogeneous complex biological and medical data, with a particular focus on deep learning methods that see fast advancement and wider adoption in bioinformatics.

关键词： Special issues and sections Meetings bioinformatics Data mining

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Anonymous Pattern Molecular Fingerprint and its Applications on Property Identification

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第6期20卷 3759-3771页

作者： Liu, Xue Cheng, Qian Sun, Dan Wei, Wei Zheng, Zhiming Beihang Univ Inst Artificial Intelligence Beijing 100191 Peoples R China Beihang Univ Dan Sun are Sch Math Sci Beijing 100191 Peoples R China Minist Educ Key Lab Math Informat & Behav Semant Beijing 100191 Peoples R China Beihang Univ Sch Math Sci Beijing 100191 Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Zhongguancun Lab Beijing 100094 Peoples R China

Molecular fingerprints are significant cheminformatics tools to map molecules into vectorial space according to their characteristics in diverse functional groups, atom sequences, and other topological structures. In this paper, we investigate a novel molecular fingerprint Anonymous-FP that possesses abundant perception about the underlying interactions shaped in small, medium, and large-scale atom chains. In detail, the possible atom chains from each molecule are sampled and extended as anonymous atom chains using an anonymous encoding manner. After that, the molecular fingerprint Anonymous-FP is embedded into vectorial space in virtue of the Natural Language Processing technique PV-DBOW. Anonymous-FP is studied on molecular property identification via molecule classification experiments on a series of molecule databases and has shown valuable advantages such as less dependence on prior knowledge, rich information content, full structural significance, and high experimental performance. During the experimental verification, the scale of the atom chain or its anonymous pattern is found significant to the overall representation ability of Anonymous-FP. Generally, the typical scale r = 8 could enhance the molecule classification performance, and specifically, Anonymous-FP gains the classification accuracy to above 93% on all NCI datasets.

关键词： Molecular fingerprints anonymous pattern walks typical scale property identification molecule classification

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Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Aggregated Problem Information

Protein Structure Prediction Using Dynamic Speciation Evolut...

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

作者： Rafael Stubs Parpinelli Nicholas Wojeicchowski Nilcimar Neitzel Will Graduate Program in Applied Computing State University of Santa Catarina Joinville Brazil Computer Science Department State University of Santa Catarina Joinville Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Protein structure prediction in three dimensions represents a fundamental challenge in Structural bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact maps can significantly enhance the exploration of the search space. In this study, an evolutionary algorithm is introduced, which incorporates such problem information for protein structure prediction. The proposed method employs a dynamic speciation technique alongside fragment insertion to foster population diversity. To ensure a rich variety of fragments, a fragment library is constructed using the Rosetta Quota protocol. Additionally, information from contact maps and secondary structure is integrated into two selection strategies to facilitate a more thorough exploration of the conformational search space. The results of an experimental evaluation involving 9 proteins are presented, demonstrating competitive performance compared to existing literature. Evaluation metrics include RMSD, GDT, and processing time.

关键词： Proteins Measurement Visualization Protocols Heuristic algorithms Evolutionary computation Search problems Libraries Space exploration bioinformatics

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EnsDeepDP: An Ensemble Deep Learning Approach for Disease Prediction Through Metagenomics

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 986-998页

作者： Shen, Yang Zhu, Jinlin Deng, Zhaohong Lu, Wenwei Wang, Hongchao Jiangnan Univ State Key Lab Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China

A growing number of studies show that the human microbiome plays a vital role in human health and can be a crucial factor in predicting certain human diseases. However, microbiome data are often characterized by the limited samples and high-dimensional features, which pose a great challenge for machine learning methods. Therefore, this paper proposes a novel ensemble deep learning disease prediction method that combines unsupervised and supervised learning paradigms. First, unsupervised deep learning methods are used to learn the potential representation of the sample. Afterwards, the disease scoring strategy is developed based on the deep representations as the informative features for ensemble analysis. To ensure the optimal ensemble, a score selection mechanism is constructed, and performance boosting features are engaged with the original sample. Finally, the composite features are trained with gradient boosting classifier for health status decision. For case study, the ensemble deep learning flowchart has been demonstrated on six public datasets extracted from the human microbiome profiling. The results show that compared with the existing algorithms, our framework achieves better performance on disease prediction.

关键词： Diseases Deep learning Predictive models Support vector machines Radio frequency Feature extraction Biological system modeling Human microbiome ensemble deep learning disease prediction scoring strategy metagenomics

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Immunity Vanishing Act: Epidemic Variant and Immunity Analysis via Evolutionary Computation

Immunity Vanishing Act: Epidemic Variant and Immunity Analys...

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

作者： Farhana Yasmeen Michael Dubé Sheridan Houghten Department of Computer Science Brock University St. Catharines Canada Department of Mathematics and Statistics University of Guelph Guelph Canada

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

An evolutionary algorithm is employed to evolve contact networks representing interactions between individuals in a population. These networks play a crucial role in providing insights into epidemic behaviour and how viruses propagate through the population. The networks are evolved using two fitness functions: one focused on maximizing infection severity and one focused on maximizing the total number of infections (spread). Both evaluate potential networks by simulating SIR epidemics in the context of epidemic variants. The impact of different types of immunity and different probabilities of new variants being generated are evaluated with respect to the number and severity of infections, the characteristics of the evolved contact networks, and the length of the epidemic. The evolutionary algorithm successfully created networks likely to result in more severe infections when focusing on epidemic severity and networks likely to result in a higher number of infections of any severity when focusing on spread, although the immunity type and variant probability both had significant impact.

关键词： Epidemics Computer viruses Focusing Evolutionary computation bioinformatics Immune system computational biology

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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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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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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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