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Interlayer antisynchronization in degree-biased duplex networks

Physical Review E 2023年第3期107卷 034313-034313页

作者： Sayantan Nag Chowdhury Sarbendu Rakshit Chittaranjan Hens Dibakar Ghosh Department of Environmental Science and Policy University of California Davis California 95616 USA Technology Innovation Hub (TIH) IDEAS (Institute of Data Engineering Analytics and Science Foundation) Indian Statistical Institute 203 B. T. Road Kolkata 700108 India Department of Mechanical Engineering University of California Riverside California 92521 USA Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Gachibowli Hyderabad 500032 India Physics and Applied Mathematics Unit Indian Statistical Institute 203 B. T. Road Kolkata 700108 India

With synchronization being one of nature's most ubiquitous collective behaviors, the field of network synchronization has experienced tremendous growth, leading to significant theoretical developments. However, most previous studies consider uniform connection weights and undirected networks with positive coupling. In the present article, we incorporate the asymmetry in a two-layer multiplex network by assigning the ratio of the adjacent nodes' degrees as the weights to the intralayer edges. Despite the presence of degree-biased weighting mechanism and attractive-repulsive coupling strengths, we are able to find the necessary conditions for intralayer synchronization and interlayer antisynchronization and test whether these two macroscopic states can withstand demultiplexing in a network. During the occurrence of these two states, we analytically calculate the oscillator's amplitude. In addition to deriving the local stability conditions for interlayer antisynchronization via the master stability function approach, we also construct a suitable Lyapunov function to determine a sufficient condition for global stability. We provide numerical evidence to show the necessity of negative interlayer coupling strength for the occurrence of antisynchronization, and such repulsive interlayer coupling coefficients cannot destroy intralayer synchronization.

关键词： Coupled oscillators Dynamics of networks Synchronization

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From sequence to protein structure and conformational dynamics with AI/ML

arXiv

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arXiv 2025年

作者： Ille, Alexander M. Anas, Emily Mathews, Michael B. Burley, Stephen K. Rutgers Cancer Institute Rutgers The State University of New Jersey NewarkNJ United States College of Computing Georgia Institute of Technology AtlantaGA United States Department of Medicine Rutgers New Jersey Medical School NewarkNJ United States School of Graduate Studies Rutgers The State University of New Jersey NewarkNJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey PiscatawayNJ United States Collaboratory Rutgers The State University of New Jersey PiscatawayNJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New BrunswickNJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California-San Diego La Jolla San DiegoCA United States

The 2024 Nobel Prize in Chemistry was awarded in part for protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and 3D structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments (MSAs). The deterministic relationship between sequence and structure was hypothesized over half a century ago with profound implications for the biological sciences ever since. Based on this relationship, we hypothesize that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting ensembles of protein structures (i.e., distinct conformations). Accordingly, we conceptualized an AI/ML model architecture which may be trained on sequence data in combination with conformationally-sensitive structure data, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Sequence-informed prediction of protein structural dynamics has the potential to emerge as a transformative capability across the biological sciences, and its implementation could very well be on the horizon. © 2025, CC BY-NC-ND.

关键词： Conformations

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Genome data mining approach to identify potential protein in crop plants

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AIP Conference Proceedings 2024年第1期3026卷

作者： Joko Pebrianto Trinugroho Faisal Asadi Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

There is a need to produce more crop plants to meet the future global demand. However, the climate change has affected the global crop yield. Consequently, finding an alternative approach to improve crop yield becomes essential. The development of sequencing techniques, as well as information technologies, have enabled us to perform genome data mining. Using genome data mining approach, it is possible to identify or discover a protein which has a particular characteristic. This study aims to identify a protein, which could potentially improve crop yield, using genome data mining approach. D1 protein was used as the target, as this protein is highly involved in photosynthesis. Then, protein sequences of various crop plants were collected from biological database. After conducting data trimming and filtering, sequence analysis was performed. The analysis was used to construct phylogenetic tree and create a 3D protein model. Sequence analysis displayed variation in amino acid sequence in D1 protein. Protein modelling located the variations, which scattered within D1 protein. Furthermore, we highlighted the amino acid residues that are the targets for genetic engineering. The research findings may provide a reference to improve crop production through genome mining approach.

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Predicting drug response through tumor deconvolution by cancer cell lines

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Patterns 2024年第4期5卷 100949页

作者： Hsu, Yu-Ching Chiu, Yu-Chiao Lu, Tzu-Pin Hsiao, Tzu-Hung Chen, Yidong Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei115 Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei100 Taiwan Greehey Children's Cancer Research Institute University of Texas Health San Antonio San AntonioTX78229 United States Department of Medicine School of Medicine University of Pittsburgh PittsburghPA15261 United States UPMC Hillman Cancer Center University of Pittsburgh PittsburghPA15232 United States Department of Medical Research Taichung Veterans General Hospital Taichung40705 Taiwan Department of Population Health Sciences University of Texas Health San Antonio San AntonioTX78229 United States

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing. © 2024 The Authors

关键词： Tumors

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A greener AI-Based crowd counting via efficient deep learning

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Chrysler R. Gunarso T. Puteri T. W. Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Crowd counting is one of the important AI implementations, which is defined as an AI model that can automatically count humans from CCTV footage. Similar to other implementations of Artificial Intelligence (AI), a crowd counting AI development tends to produce larger models over time. It is caused by the tendency of larger models to have better performance. This trend negatively impacts the environment because the development of larger AI models generates more CO2 that causes global warming. This study instead focuses on developing crowd counting AI that has a small size with competitive performance. Our proposed model achieves competitive performance compared to other state-of-the-art crowd counting models. Currently, our proposed model is also the 3rd best model in terms of performance-size tradeoff among all crowd counting AI with known model size.

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RePhine: An Integrative Method for Identification of Drug Response-related Transcriptional Regulators

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Genomics, Proteomics & bioinformatics 2021年第4期19卷 534-548页

作者： Xujun Wang Zhengtao Zhang Wenyi Qin Shiyi Liu Cong Liu Georgi ZGenchev Lijian Hui Hongyu Zhao Hui Lu SJTU-Yale Joint Center for Biostatistics and Data Science Department of Bioinformatics and BiostatisticsSchool of Life Science and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Cell Biology CAS Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyShanghai 200031China Department of Bioengineering University of Illinois at ChicagoIL 60607USA Department of Genetics School of MedicineYale UniversityNew HavenCT 06511USA Department of Medical Informatics Columbia UniversityNew YorkNY 10032USA Bulgarian Institute for Genomics and Precision Medicine Sofia 1000Bulgaria Department of Biostatistics Yale School of Public Health.New HavenCT 06511USA Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Center for Biomedical informatics Shanghai Children’s HospitalShanghai 200040China

Transcriptional regulators(TRs)participate in essential processes in cancer pathogenesis and are critical therapeutic *** of drug response-related TRs from cell line-based compound screening data is often challenging due to low m RNA abundance of TRs,protein modifications,and other confounders(CFs).In this study,we developed a regression-based pharmacogenomic and Ch IP-seq data integration method(Re Phine)to infer the impact of TRs on drug response through integrative analyses of pharmacogenomic and Ch IP-seq *** Phine was evaluated in simulation and pharmacogenomic data and was applied to pan-cancer datasets with the goal of biological *** simulation data with added noises or CFs and in pharmacogenomic data,Re Phine demonstrated an improved performance in comparison with three commonly used methods(including Pearson correlation analysis,logistic regression model,and gene set enrichment analysis).Utilizing Re Phine and Cancer Cell Line Encyclopedia data,we observed that Re Phinederived TR signatures could effectively cluster drugs with different mechanisms of *** Phine predicted that loss-offunction of EZH2/PRC2 reduces cancer cell sensitivity toward the BRAF inhibitor *** validation confirmed that pharmacological EZH2 inhibition increases the resistance of cancer cells to PLX4720 *** results support that Re Phine is a useful tool for inferring drug response-related TRs and for potential therapeutic *** source code for Re Phine is freely available at https://***/coexps/Re Phine.

关键词： Pharmacogenomics ChIP-seq Transcriptional regulator BRAF inhibitor resistance Drug resistance

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A Diversity Inventory Monitoring System of Riparian Vegetation

A Diversity Inventory Monitoring System of Riparian Vegetati...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Dian Pratama Putra Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology STIPER Agricultural Institute Yogyakarta Indonesia Faculty of Agricultural Technology Faculty of Forestry STIPER Agricultural Institute Yogyakarta Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations.

关键词： Vegetation Manuals Forestry Soil Mobile applications Biodiversity Expert systems

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Nesting of Touching Polygons

arXiv

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arXiv 2024年

作者： Seemann, Carsten R. Stadler, Peter F. Hellmuth, Marc Swarm Intelligence and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 LeipzigD-04109 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Polygons are cycles embedded into the plane;their vertices are associated with x- and y-coordinates and the edges are straight lines. Here, we consider a set of polygons with pairwise non-overlapping interior that may touch along their boundaries. Ideas of the sweep line algorithm by Bajaj and Dey for non-touching polygons are adapted to accommodate polygons that share boundary points. The algorithms established here achieves a running time of O(n + N log N), where n is the total number of vertices and N MSC Codes 05C10 Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning

arXiv

引用

arXiv 2024年

作者： Mateus, Pedro Garst, Swier Yu, Jing Cats, Davy Harms, Alexander G.J. Birhanu, Mahlet Beekman, Marian Slagboom, P. Eline Reinders, Marcel van der Grond, Jeroen Dekker, Andre Jansen, Jacobus F.A. Beran, Magdalena Schram, Miranda T. Visser, Pieter Jelle Moonen, Justine Ghanbari, Mohsen Roshchupkin, Gennady Vojinovic, Dina Bermejo, Inigo Mei, Hailiang Bron, Esther E. GROW School for Oncology and Reproduction Maastricht University Medical Centre+ Maastricht Netherlands Section of Molecular Epidemiology Department of Biomedical Data Sciences Leiden University Medical Center Netherlands Delft Bioinformatics Lab Delft University of Technology Delft Netherlands Biomedical Imaging Group Rotterdam Department of Radiology & Nuclear Medicine Erasmus MC - University Medical Center Rotterdam Rotterdam Netherlands Department of Epidemiology Erasmus MC - University Medical Center Rotterdam Rotterdam Netherlands Department of Radiology and Nuclear Medicine Maastricht University Medical Center Maastricht Netherlands Mental Health & Neuroscience Research Institute Maastricht University Maastricht Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Maastricht University Maastricht Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Netherlands Department of Radiology Leiden University Medical Center Leiden Netherlands

Biological age scores are an emerging tool to characterize aging by estimating chronological age based on physiological biomarkers. Various scores have shown associations with aging-related outcomes. This study assessed the relation between an age score based on brain MRI images (BrainAge) and an age score based on metabolomic biomarkers (MetaboAge). We trained a federated deep learning model to estimate BrainAge in three cohorts. The federated BrainAge model yielded significantly lower error for age prediction across the cohorts than locally trained models. Harmonizing the age interval between cohorts further improved BrainAge accuracy. Subsequently, we compared BrainAge with MetaboAge using federated association and survival analyses. The results showed a small association between BrainAge and MetaboAge as well as a higher predictive value for the time to mortality of both scores combined than for the individual scores. Hence, our study suggests that both aging scores capture different aspects of the aging process. © 2024, CC BY.

关键词： Federated learning

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