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Functional Imaging and Modeling of the Heart - 12th International conference, FIMH 2023, Proceedings

作者： Rosilho de Souza, Giacomo Pezzuto, Simone Krause, Rolf Center for Computational Medicine in Cardiology Euler Institute Università della Svizzera italiana Lugano Switzerland Laboratory of Mathematics for Biology and Medicine Department of Mathematics University of Trento Trento Italy

ISBN: (纸本)9783031353017

Gap junction arrangement is a major determinant of cardiac conduction velocity. Importantly, structural remodeling of the myocardium may lead to pathological CV and a pro-arrhythmic substrate. In this work we aim at quantifying the side-to-side conduction velocity in a sub-micrometer model of the myocardium that accounts for gap junctions. We consider the Extracellular-Membrane-Intracellular (EMI) model, which describes the evolution of the electric potential within each cell and in the extracellular space. For the solution of the model, we propose a boundary integral formulation of the cell-to-cell model that leads to small system of ODEs. We study several configurations of lateral gap junction distribution, as well as different shapes and sizes of the cell-to-cell connection. We find that irregular positioning of gap junctions from cell to cell is of utmost importance to obtain realistic CV values, while gap junction’s shape is of secondary importance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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ImmunoTyper-SR: A Novel computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes Using Short Read Data 26th

ImmunoTyper-SR: A Novel Computational Approach for Genotypi...

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

作者： Ford, Michael Hari, Ananth Rodriguez, Oscar Xu, Junyan Lack, Justin Oguz, Cihan Zhang, Yu Weber, Sarah Magliocco, Mary Barnett, Jason Xirasagar, Sandhya Samuel, Smilee Imberti, Luisa Bonfanti, Paolo Biondi, Andrea Dalgard, Clifton L. Chanock, Stephen Rosen, Lindsey Holland, Steven Su, Helen Notarangelo, Luigi Vishkin, Uzi Watson, Corey Sahinalp, S. Cenk National Cancer Institute NIH BethesdaMD United States National Institute of Allergy and Infectious Diseases NIH BethesdaMD United States Department of Electrical Engineering University of Maryland College ParkMD United States Department of Biochemistry and Molecular Genetics University of Louisville LouisvilleKY United States Uniformed Services University of the Health Sciences BethesdaMD United States Diagnostic Department ASST Spedali Civili di Brescia Brescia Italy University of Milano-Bicocca-Fondazione MBBM Monza Italy

The human immunoglobulin heavy chain (IGH) locus on chromosome 14 includes more than 40 functional copies of the variable gene (IGHV). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland... 详细信息

ISBN: (纸本)9783031047480

关键词： Genes

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The supervised CNN image edge detection algorithm in scotopic vision environment 9

The supervised CNN image edge detection algorithm in scotopi...

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9th ieee International conference on bioinformatics and computational biology, ICBCB 2021

作者： Zhang, Qin Zhou, Xiangling Xu, Xiaowen Xie, Xinhong Zhang, Mengyuan Tao, Yuxiang Li, Ke Zhao, Zhiqiang School of Bioinformatics Chongqing University of Posts and Telecommunications Chongqing China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China College of Automation Chongqing University of Posts and Telecommunications Chongqing China Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9780738132020

Scotopic vision environment images are characterized by low contrast and some details hidden in the image background, that causes human eyes are hard to detect and brings difficulties to the subsequent application of computer vision tasks. In order to solve the problem that many false edges are generated when DexiNed (Dense Extreme Inception Network for Edge Detection) model detects scotopic vision images, an improved DexiNed edge detection model was proposed in this paper. The improved edge detection model retained the backbone network of the DexiNed model. By adding the convolution layers and residual units in the appropriate position of the DexiNed model, the model can eliminate most of the false edges generated by the DexiNed model in the scotopic vision images better. In order to further improve the edge detection accuracy of scotopic vision image by the improved DexiNed model, this paper builds scotopic vision image training set based on edge annotation data set BIPED (Barcelona Images for Perceptual Edge Detection) from RGB and YUV color space respectively. And scotopic vision image test dataset results showed that the effect of scotopic vision image based on RGB color space to have better performance, because edge continuity was better and the edge detection model of MSE (mean square error) index dropped, PSNR (peak signal to noise ratio) and SSIM (structural similarity) index raised. © 2021 ieee.

关键词： Edge detection

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Topsy-Turvy: integrating a global view into sequence-based PPI prediction

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bioinformatics 2022年第SUPPL 1期38卷 264-272页

作者： Singh, Rohit Devkota, Kapil Sledzieski, Samuel Berger, Bonnie Cowen, Lenore MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Tufts Univ Dept Comp Sci Medford MA 02155 USA MIT Dept Math Cambridge MA 02139 USA MIT Schwarzman Coll Comp 77 Massachusetts Ave Cambridge MA 02139 USA

computational methods to predict protein-protein interaction (PPI) typically segregate into sequence-based 'bottom-up' methods that infer properties from the characteristics of the individual protein sequences, or global 'top-down' methods that infer properties from the pattern of already known PPIs in the species of interest. However, a way to incorporate top-down insights into sequence-based bottom-up PPI prediction methods has been elusive. We thus introduce Topsy-Turvy, a method that newly synthesizes both views in a sequence-based, multi-scale, deep-learning model for PPI prediction. While Topsy-Turvy makes predictions using only sequence data, during the training phase it takes a transfer-learning approach by incorporating patterns from both global and molecular-level views of protein interaction. In a cross-species context, we show it achieves state-of-the-art performance, offering the ability to perform genome-scale, interpretable PPI prediction for non-model organisms with no existing experimental PPI data. In species with available experimental PPI data, we further present a Topsy-Turvy hybrid (TT-Hybrid) model which integrates Topsy-Turvy with a purely network-based model for link prediction that provides information about species-specific network rewiring. TT-Hybrid makes accurate predictions for both well- and sparsely-characterized proteins, outperforming both its constituent components as well as other state-of-the-art PPI prediction methods. Furthermore, running Topsy-Turvy and TT-Hybrid screens is feasible for whole genomes, and thus these methods scale to settings where other methods (e.g. AlphaFold-Multimer) might be infeasible. The generalizability, accuracy and genome-level scalability of Topsy-Turvy and TT-Hybrid unlocks a more comprehensive map of protein interaction and organization in both model and non-model organisms.

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An Explainable and Lightweight Deep Learning Model with Attention Mechanism for Efficient Lung Disease Detection

An Explainable and Lightweight Deep Learning Model with Atte...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Qian Tingting Sohaib Asif Yuke Lin Jincao Yao Enyu Wang Vicky Yang Wang Dong Xu Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou China Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Taizhou China Wenling Institute of Big Data and Artificial Intelligence in Medicine Taizhou China Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Chinese Academy of Sciences China Zhejiang Chinese Medical University Hangzhou China Department of Radiology Imaging Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

COVID-19 has rapidly spread across the world as an extremely contagious disease. Therefore, early detection of the virus is essential to effectively control its transmission and prevent further spread. It is widely reported in the literature that convolutional neural networks (CNNs) are commonly utilized for COVID-19 detection. However, the majority of these models require substantial computational resources and possess a large number of parameters, rendering them less feasible for deployment on real-time devices. This study proposes a solution to address COVID-19 detection challenges by introducing LWIA-Net, a rapid and highly efficient lightweight CNN architecture. The LWIA-Net is equipped with channel attention squeeze and excitation (SE) blocks, as well as a naïve inception module, to enhance the network’s learning ability, reduce computational complexity, and maintain a high level of detection performance. By incorporating the naïve inception module, the LWIA-Net enables multi-level feature extraction, while the inclusion of the SE block helps focuses on informative channels. This integration contributes to the production of high-quality image features and effectively reduces redundancy. We performed thorough experiments using both our locally developed dataset and a publicly available dataset. The statistical analysis demonstrate that LWIA-Net achieves superior performance compared to pre-trained CNN architectures, despite its extremely lightweight architecture which demands low computational cost, memory space, and consists of only 1.11 million parameters. We conducted a comprehensive ablation study to gain a deeper understanding of the significance of each component and to verify their individual contributions to the LWIA-Net. Additionally, we utilized Grad-CAM analysis to confirm that the proposed model effectively identifies and emphasizes the most critical regions of interest. Experimental results have shown that LWIA-Net achieves remarkable accuracy rate

关键词： COVID-19 Statistical analysis computational modeling Pulmonary diseases Redundancy Computer architecture Production Rendering (computer graphics) Real-time systems Convolutional neural networks

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How natural language processing derived techniques are used on biological data: a systematic review

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND bioinformatics 2024年第1期13卷 23-23页

作者： Oikonomou, Emmanouil D. Karvelis, Petros Giannakeas, Nikolaos Vrachatis, Aristidis Glavas, Evripidis Tzallas, Alexandros T. Univ Ioannina Dept Informat & Telecommun Human Comp Interact Lab Kostakioi Arta 47150 Greece Univ Ioannina Dept Informat & Telecommun Kostakioi Arta 47150 Greece Ionian Univ Dept Informat Bioinformat & Human Electrophysiol Lab Corfu 49100 Greece Hellen Open Univ Sch Sci & Technol Patras 26335 Greece

The decoding of the human genome, completed two decades ago, marked a revolutionary moment in biology by introducing a vast amount of data. This avalanche of information presented several computational challenges. Machine Learning has become the dominant method to address these challenges, with Natural Language Processing playing a significant role and offering promising results. In this systematic review, we will explore the application of Machine Learning and Natural Language Processing to the study of biological data. On the one hand, Machine Learning is widely used in Artificial intelligence to improve automation, carry out tasks that require no human interaction, and perform analytical and physical activities. It helps advance our understanding of biology and improve healthcare and drug development processes in bioinformatics. On the other hand, improved machine-human language interaction is the aim of Natural Language Processing. Its three main goals are character sequence processing, pattern recognition, and algorithm development. The use of Natural Language Processing is becoming increasingly important for the analysis of omics data using both modern and conventional Machine Learning models, underscoring the necessity for a systematic review. In this work, 82 studies were included following the PRISMA guidelines, sourced from PubMed, Scopus and ieee Xplore on April 4th, 2023. The evaluation of the publications was based on the type of the studied biological data and the employed NLP techniques. Through our in-depth exploration of NLP approaches, we highlight their significance and potential in advancing the field of bioinformatics.

关键词： Artificial intelligence Databases Deep Learning NLP techniques Omics

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ieee/ACM Transactions on computational biology and bioinformatics 2024年

作者： Li, Mengzhen Coskun, Mustafa Koyuturk, Mehmet Case Western Reserve University Department of Computer and Data Sciences ClevelandOH United States Ankara University Department of Artificial Intelligence and Data Engineering Turkey

Graph machine learning algorithms are being commonly applied to a broad range of prediction tasks in systems biology. An important design criterion in this regard is the definition of 'topological similarity' between two nodes in a network, which is used to design convolution matrices for graph convolution or loss functions to evaluate node embeddings. Many measures of topological similarity exist in network science literature (e.g., random walk based proximity, shared neighborhood) and recent comparative studies show that the choice of topological similarity can have a significant effect on the performance and reliability of graph machine learning models. We propose GraphCan, a framework for computing canonical representations for biological networks using a similarity-based Graph Convolutional Network (GCN). GraphCan integrates multiple node similarity measures (Common Neighbor, Adamic Adar, Random Walk with Restart, Von Neumann, Resource Allocation, Hub-Depressed Index, Hub-Promoted Index, and adjacency matrix) to compute canonical node embeddings for a given network. The resulting embeddings can be utilized directly for downstream machine learning tasks. We comprehensively evaluate GraphCan in the context of various link prediction tasks in systems biology. Our results show that the integration of multiple similarity measures improves the robustness of the framework, especially when the input networks are sparse. GraphCan is available as open source at https://***/Meng-zhen-Li/***. © 2004-2012 ieee.

关键词： Graph embeddings

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Estimating confidence intervals on reconstructed cophylogenies using bootstrap resampling

Estimating confidence intervals on reconstructed cophylogeni...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Julia Zheng Yuya Nishida Elizabeth A.C. Heath-Heckman Kevin J. Liu Computer Science and Engineering Michigan State University East Lansing USA Integrative Biology Michigan State University East Lansing USA Integrative Biology Microbiology Genetics & Immunology Michigan State University East Lansing USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Cophylogenies represent coevolutionary histories of two or more sets of coevolved taxa, and are used to study coevolution and other fundamental evolutionary processes. As with traditional phylogenies, cophylogenies are primarily reconstructed using computational analysis of DNA and other biomolecular sequence data. An essential question concerns the reliability of reconstructed phylogenies and *** resampling offers a principled approach to evaluate statistical confidence for these tasks. We therefore apply bootstrap resampling – one of the most widely used non-parametric resampling techniques – to place confidence intervals on a reconstructed cophylogeny, which is the first such method to our knowledge. We validate the performance of the resulting reliability estimates in a simulation study as well as an empirical case study of bobtail squid and its bioluminescent endosymbionts. The utility of statistical resampling to assess cophylogenetic reconstruction reliability in an automated and data-driven manner points the way forward – both for future methods development and wider adoption in studies of symbioses and other forms of coevolution.

关键词： Sequential analysis Statistical analysis Estimation Genomics DNA Phylogeny Reliability History bioinformatics Next generation networking

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Decoding the Functional Interactome of Non-model Organisms with PHILHARMONIC 29th

Decoding the Functional Interactome of Non-model Organisms...

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29th International conference on Research in computational Molecular biology, RECOMB 2025

作者： Sledzieski, Sam Versavel, Charlotte Singh, Rohit Ocitti, Faith Devkota, Kapil Kumar, Lokender Shpilker, Polina Roger, Liza Yang, Jinkyu Lewinski, Nastassja Putnam, Hollie Berger, Bonnie Klein-Seetharaman, Judith Cowen, Lenore Center for Computational Biology Flatiron Institute New YorkNY United States Department of Computer Science Tufts University MedfordMA United States Departments of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Biotechnology Faculty of Applied Sciences and Biotechnology Shoolini University Himachal Pradesh Solan173229 India School of Molecular Sciences and School of Ocean Futures Arizona State University TempeAZ United States Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Chemical and Life Science Engineering Virginia Commonwealth University RichmondVA United States Department of Biological Sciences University of Rhode Island KingstonRI United States Computer Science and Artificial Intelligence Laboratory and Department of Mathematics MIT CambridgeMA United States School of Molecular Sciences and College of Health Solutions Arizona State University PhoenixAZ United States

ISBN: (纸本)9783031902512

We introduce PHILHARMONIC, a computational framework that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our novel clustering approach produces highly informative functional modules by de-noising the predicted network. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We initially perform remote homology-based functional annotation by leveraging hmmscan and GODomainMiner to assign initial functions to proteins at large evolutionary distances;our clusters then enable us to newly assign functions to uncharacterized proteins through "function by association." We validate the ability of PHILHARMONIC to recover gold-standard functional enrichments in the well-annotated fruit fly D. melanogaster, and apply it to investigate stress response in the reef-building coral P. damicornis and its algal symbiont C. goreaui. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Understanding the Dynamics of the Proliferative Phase in Local Burn Wound Healing: A computational Model

Understanding the Dynamics of the Proliferative Phase in Loc...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Roland V. Bumbuc H. Ibrahim Korkmaz P. P. M. van Zuijlen Louis Vermeulen Vivek M. Sheraton Department of Plastic Reconstructive and Hand Surgery Amsterdam Movement Sciences (AMS) Institute Amsterdam UMC Amsterdam The Netherlands Department of Molecular Cell Biology and Immunology Amsterdam Infection and Immunity (AII) Institute Amsterdam UMC Amsterdam The Netherlands Computational Science Lab Informatics Institute University of Amsterdam Amsterdam The Netherlands Center for Experimental and Molecular Medicine (CEMM) Amsterdam UMC Amsterdam The Netherlands Burn Center and Department of Plastic and Reconstructive Surgery Red Cross Hospital Beverwijk The Netherlands Preclinical Research Association of Dutch Burn Centres (ADBC) Beverwijk The Netherlands Paediatric Surgical Centre Emma Children’s Hospital Amsterdam UMC Amsterdam The Netherlands Laboratory for Experimental Oncology and Radiobiology ONCODE Amsterdam UMC Amsterdam The Netherlands

Understanding the dynamic processes that underlie burn wound healing is a complex endeavor that requires advanced computational approaches. The proliferative phase in burn wounds is critical for the closure of the burn wound. We present a novel model that uses a set of coupled ordinary differential equations that predict intricate interactions between different cell types, cytokines, growth factors, and proteins during the proliferative phase of wound healing after burn injury. We simulated the model for 30 weeks and were able to predict cell and molecular dynamics in this intricate phase of burn wound healing. We achieved biologically sensible results and were able to identify fibroblasts, myofibroblasts, and collagen types III and I as crucial parameters that drive the proliferative phase in burn wounds.

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