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Estimation of Discriminative Multimodal Brain Network Connectivity Using Message-Passing-Based Nonlinear Network Fusion

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第4期20卷 2398-2406页

作者： Chen, Nan Guo, Man Li, Yongchao Hu, Xiping Yao, Zhijun Hu, Bin Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Guangdong Peoples R China Chinese Acad Sci Joint Res Ctr Cognit Neurosensor Technol Lanzhou U Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci CAS Ctr Excellence Brain Sci & Intelligence Techno Shanghai 200083 Peoples R China Lanzhou Univ Minist Educ Engn Res Ctr Open Source Software & Real Time Syst Lanzhou 730000 Gansu Peoples R China

Effective estimation of brain network connectivity enables better unraveling of the extraordinary complexity interactions of brain regions and helps in auxiliary diagnosis of psychiatric disorders. Considering different modalities can provide comprehensive characterizations of brain connectivity, we propose the message-passing-based nonlinear network fusion (MP-NNF) algorithm to estimate multimodal brain network connectivity. In the proposed method, the initial functional and structural networks were computed from fMRI and DTI separately. Then, we update every unimodal network iteratively, making it more similar to the others in every iteration, and finally converge to one unified network. The estimated brain connectivities integrate complementary information from multiple modalities while preserving their original structure, by adding the strong connectivities present in unimodal brain networks and eliminating the weak connectivities. The effectiveness of the method was evaluated by applying the learned brain connectivity for the classification of major depressive disorder (MDD). Specifically, 82.18% classification accuracy was achieved even with the simple feature selection and classification pipeline, which significantly outperforms the competing methods. Exploration of brain connectivity contributed to MDD identification suggests that the proposed method not only improves the classification performance but also was sensitive to critical disease-related neuroimaging biomarkers.

关键词： Terms-Multimodal brain network connectivity nonlinear network fusion Major depressive disorder

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PocketLG: Protein Binding Pocket Identification Using Protein Language Model and Graph Transformer

PocketLG: Protein Binding Pocket Identification Using Protei...

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2024 ieee International conference on bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Li Chang, Heng Sun, Zehua Li, Wenjie Zhu, Li Wang, Han Changchun University of Technology College of Computer Science and Engineering Changchun China Northeast Normal University School of Information Science and Technology Institute of Computational Biology Changchun China General Hospital Ningxia Medical University The Radiology Department Ningxia China

ISBN: (纸本)9798350386226

Protein pocket is a special region on the surface of a protein that commonly interacts with other molecules, especially small molecule compounds. Accurately identifying and understanding the structure character of protein pockets, that can accelerate the development of new drugs, as well improve existing drugs to reveal disease mechanisms. In this study, we propose a deep learning method named PocketLG, to identify the potential binding pockets integrating a Protein Language model and Graph Transformer. The method constructs the pocket identification task as a regression problem, which can more accurately identify the real pocket boundaries. In addition, We propose a data sampling strategy and construct a pipeline to support the data sampling effort. This strategy has the benefit of augmenting the data, allowing the model to better learn the intrinsic characteristics of the pocket structure, even for pockets with unknown boundaries This method can sample the unknown pocket by dividing different sample sizes and then determine the boundary of the unknown pocket by the regression value, it can be a direct help for downstream tasks. Experimental results show that our deep learning model can identify binding pockets on proteins with 91 percent success rate. This work provides a new technical route for protein binding pocket prediction research, which can greatly contribute to the development of the pharmaceutical industry. PocketLG source code, pre-trained models and data preprocessing code are available at https://***/NENUBioCompute/PocketLG. © 2024 ieee.

关键词： Deep Learning Drug Discovery Protein pockets

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Tractable and Expressive Generative Models of Genetic Variation Data 26th

Tractable and Expressive Generative Models of Genetic Vari...

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26th International conference on Research in computational Molecular biology, RECOMB 2022

作者： Dang, Meihua Liu, Anji Wei, Xinzhu Sankararaman, Sriram Van den Broeck, Guy Department of Computer Science UCLA Los Angeles United States Department of Human Genetics School of Medicine UCLA Los Angeles United States Department of Computational Medicine School of Medicine UCLA Los Angeles United States

ISBN: (纸本)9783031047480

关键词： Genes

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Guest Editorial for the 17th Asia Pacific bioinformatics conference

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2123-2124页

作者： Zhang, Louxin Peng, Shaoliang Chen, Yi-Ping Phoebe Sankoff, David Li, Guoliang Zhang, Hong-Yu Natl Univ Singapore Dept Math Singapore 119076 Singapore Hunan Univ Natl Supercomp Ctr Wuhan 410082 Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Bundoora Vic 3086 Australia Univ Ottawa Dept Math & Stat Ottawa ON K1N 6N5 Canada Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China

The eight papers in this special section were presented at the 17th Asia Pacific bioinformatics conference (APBC), which was held in Wuhan, China, 14-16 January 2019.

关键词： Special issues and sections Meetings bioinformatics

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Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights 9

Machine Learning-Assisted Protein Structure Prediction: An A...

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9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Mahendran, S. Radha Dogra, Akriti Mendhe, Dinesh Babu, S. B. G. Tilak Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology And Advanced Studies Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India Rutgers State University of New Jersey Office of Research Computing New BrunswickNJ United States Aditya Engineering College Department of Ece Surampalem India School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Upums Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350365092

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level. © 2024 ieee.

关键词： Proteins

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Turing Computation with Neural Networks Composed of Synfire Rings

Turing Computation with Neural Networks Composed of Synfire ...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on computational intelligence (ieee WCCI) / International Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Cabessa, Jeremie Univ Paris 2 Pantheon Assas Lab Math Econ & Appl Microecon LEMMA F-75006 Paris France Czech Acad Sci Inst Comp Sci Prague 18207 8 Czech Republic

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Synfire rings are fundamental neural circuits capable of conveying self-sustained activities in a robust and temporally precise manner. We propose a Turing-complete paradigm for neural computation based on synfire rings. More specifically, we provide an algorithmic procedure which, for any fixed-space Turing machine, builds a corresponding Boolean neural network composed of synfire rings capable of simulating it. As a consequence, any fixed-space Turing machine with tapes of length N can be simulated in linear time by some Boolean neural network composed of O(N) rings and cells. The construction can naturally be extended to general Turing machines. Therefore, any Turing machine can be simulated in linear time by some Boolean neural network composed of infinitely many synfire rings. The linear time simulation relies on the possibility to mimic the behavior of the machines. In the long term, these results might contribute to the realization of biological neural computers.

关键词： Computers Turing machines computational modeling Biological system modeling Neural circuits Cells (biology) Logic gates

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Parallel Computation of Conformational Stability for CD4+ T-cell Epitope Prediction

Parallel Computation of Conformational Stability for CD4+ T-...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Bhattacharya, Avik Wrabl, James O. Landry, Samuel J. Mettu, Ramgopal R. Tulane University Department of Computer Science New Orleans United States Johns Hopkins University Department of Biology Baltimore United States Tulane University School of Medicine Department of Biochemistry and Molecular Biology New Orleans United States

ISBN: (纸本)9798350337488

Antigen-presenting cells present peptides derived from foreign pathogens in the form of peptide-MHCII complexes to induce immune response. Two important and interrelated processes that lead to peptide presentation are antigen processing and peptide-MHCII binding. Although the latter phenomenon has been thoroughly studied and incorporated into CD4+ T cell epitope prediction tools, antigen processing has remained under-explored as a means to improve epitope prediction tools. In prior work, our group developed the antigen processing likelihood (APL) metric [1] and showed that it can be used to effectively predict CD4+ T-cell epitopes either alone or in combination with peptide-MHCII binding affinity-based tools. A key component of the APL scoring approach is a residue-wise conformational stability metric known as COREX [2], which estimates the likelihood of unfolding at each residue using a free energy approximation. COREX calculation takes a significant amount of time (6-8 hours) and thus limits our ability to rapidly predict CD4+ T-cell epitopes over large antigen sets. In this paper, we give a parallel algorithm to compute the COREX metric and demonstrate its utility over several benchmarks. We achieve a 196-fold speedup with our parallel algorithm, bringing the overall computation of APL down from hours to minutes. © 2023 ieee.

关键词： antigen processing CD4+ T cell response computational epitope prediction free energy calculation parallel computation protein structure

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A Topological Characterization of DNA Sequences Based on Chaos Geometry and Persistent Homology

A Topological Characterization of DNA Sequences Based on Cha...

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2022 International conference on computational Science and computational intelligence, CSCI 2022

作者： Quan Ngoc Nguyen, Dong Dong Tan Le, Phuong Xing, Lin Lin, Lizhen Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing101408 China University of Waterloo Department of Applied Mathematics Waterloo Canada University of Notre Dame Department of Applied and Computational Mathematics and Statistics Notre Dame United States

ISBN: (纸本)9798350320282

Methods for analyzing similarities among DNA sequences play a fundamental role in computational biology, and have a variety of applications in public health, and in the field of genome analysis. In this paper, a novel geometric and topological method for analyzing similarities among DNA sequences is developed, based on persistent homology from algebraic topology, in combination with chaos geometry in 4-dimensional space as a graphical representation of DNA sequences. Our topological framework for DNA similarity analysis is general, alignment-free, and can deal with DNA sequences of various lengths, while proving first-of-the-kind visualization features for visual inspection of DNA sequences directly, based on topological features of point clouds that represent DNA sequences. As an application, we test our methods on three datasets including genome sequences of different types of Hantavirus, Influenza A viruses, and Human Papillomavirus. © 2022 ieee.

关键词： Geometry

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Data preparation for artificial intelligence in medical imaging: Experiences from the ProCAncer-I initiative

Data preparation for artificial intelligence in medical imag...

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2023 ieee EMBS Special Topic conference on Data Science and Engineering in Healthcare, Medicine and biology, ieeeCONF 2023

作者： Kalokyri, Varvara Tachos, Nikolaos Sfakianakis, Stelios Nikiforaki, Katerina Karatzanis, Ioannis Kondylakis, Haridimos Mazzetti, Simone Regge, Daniele Papanikolaou, Nikolaos Marias, Konstantinos Fotiadis, Dimitrios Tsiknakis, Manolis FORTH-ICS Foundation of Research and Technology Hellas Institute of Computer Science Nik. Plastira 100 Crete Heraklion70013 Greece University Campus of Ioannina FORTH-BRI Ioannina45110 Greece University of Turin Department of Surgical Sciences Turin Italy Candiolo10060 Italy Champalimaud Foundation Champalimaud Research Computational Clinical Imaging Group Lisbon1400-038 Portugal FORTH-BRI Greece The Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department University Campus of Ioannina Ioannina45110 Greece

ISBN: (纸本)9798350383386

The ProCAncer-I project, an important initiative in the field of prostate cancer research, is exploiting the power of Artificial intelligence (AI) to advance prostate cancer diagnosis, prognosis, and treatment. The success of AI in prostate cancer imaging within this context relies on meticulous data preparation, especially when dealing with clinical data that need to be integrated with imaging data. This paper explores the importance of data preparation to implement AI tools for prostate cancer imaging, and outlines the approach followed and challenges needed to be addressed in the context of ProCAncer-I, for ensuring data quality, privacy, and reliability. © 2023 ieee.

关键词： Medical imaging

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An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 87-94页

作者： Nguyen, Trinh-Trung-Duong The-Anh Tran Le, Nguyen-Quoc-Khanh Dinh-Minh Pham Ou, Yu-Yen Yuan Ze Univ Dept Comp Sci & Engn Chungli 32003 Taiwan Viettel Cyber Secur Hanoi 550000 Vietnam Taipei Med Univ Coll Med Artificial Intelligence Med Taipei 106 Taiwan Taipei Med Univ Res Ctr Artificial Intelligence Med Taipei 106 Taiwan Vietnam Acad Sci & Technol Inst Biotechnol Hanoi 100000 Vietnam

It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites in promoters is therefore a crucial step in further understanding of the relationship between promoter methylation and the regulation of mRNA gene expression. High throughput identification of DNA 5mC in wet lab is still time-consuming and labor-extensive. Thus, finding the 5mC site of genome-wide DNA promoters is still an important task. We compared the effectiveness of the most popular and strong machine learning techniques namely XGBoost, Random Forest, Deep Forest, and Deep Feedforward Neural Network in predicting the 5mC sites of genome-wide DNA promoters. A feature extraction method based on k-mers embeddings learned from a language model were also applied. Overall, the performance of all the surveyed models surpassed deep learning models of the latest studies on the same dataset employing other encoding scheme. Furthermore, the best model achieved AUC scores of 0.962 on both cross-validation and independent test data. We concluded that our approach was efficient for identifying 5mC sites of promoters with high performance.

关键词： Natural language processing k-mers embedding DNA 5-methylcytosine site prediction promoter advanced machine learning classifiers

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