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3rd International Conference on Computer science and Computational Intelligence, ICCSCI 2018

作者： Fanny Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Generative adversarial networks

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Transfer learning identifies bacterial signatures for cross-regional diagnosis of type 2 diabetes and enable stage-sensitive dietary fiber intervention

iMetaOmics

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iMetaOmics 2025年第2期2卷

作者： Qunye Zhang Nan Wang Fanghua Zhang Bin Chen Yihui Wang Zhongchao Wang Changying Zhao Chuandi Jin Dashuang Sheng Kaile Yue Daifeng Jiang Liaomei Gao Haohong Zhang Zixin Kang Mingyue Cheng Xiaoli Ma Haiyan Wang Dongming Hu Jun Wang Yuantao Liu Chenhong Zhou Minxiu Yao Guoping Zhao Yangang Wang Zhe Wang Kang Ning Lei Zhang Microbiome-X School of Public Health Department of Cardiology Qilu Hospital Cheeloo College of Medicine Shandong University Jinan China Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-Imaging Center of AI Biology Department of Bioinformatics and Systems Biology College of Life Science and Technology Huazhong University of Science and Technology Wuhan China Department of Endocrinology Qingdao Central Hospital University of Health and Rehabilitation Sciences (Qingdao Central Hospital) Qingdao China Qingdao University Medical Group: Department of Endocrinology and Metabolism The Affiliated Hospital of Qingdao University Department of Endocrinology and Metabolism The Third People's Hospital of Qingdao Qingdao China Shandong Academy of Sciences Science and Technology Services Platform (Shandong Academy of Sciences Foreign Students Pioneer Park) Jinan China Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University Jinan China Department of Endocrinology Qingdao Municipal Hospital (Group) Qingdao China Department of Endocrinology The Eighth People's Hospital of Qingdao Qingdao China Department of Endocrinology Qilu Hospital of Shandong University (Qingdao) Qingdao China State Key Laboratory of Microbial Technology Shandong University Qingdao China CAS Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai China Department of Endocrinology & Geriatrics Shandong Provincial Hospital Affiliated to Shandong First Medical University Jinan China

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MODL-22. Establishment of a novel system to specifically trace and ablate quiescent/slow cycling cells in high-grade glioma

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Neuro-Oncology 2022年第SUPPLEMENT_1期24卷 i173–i173页

作者： Antonica, Francesco Santomaso, Lucia Pernici, Davide Petrucci, Linda Aiello, Giuseppe Cutarelli, Alessandro Conti, Luciano Romanel, Alessandro Miele, Evelina Tebaldi, Toma Tiberi, Luca Armenise-Harvard Laboratory of Brain Cancer Department CIBIO University of Trento Trento Italy Laboratory of Stem Cell Biology Department CIBIO University of Trento Trento Italy Laboratory of Bioinformatics and Computational Genomics Department CIBIO University of Trento Trento Italy Department of Paediatric Haematology/Oncology Cell and Gene Therapy Bambino Gesù Children's Hospital Rome Italy Laboratory of RNA and Disease Data Science Department CIBIO University of Trento Trento Italy

Pediatric and adult high-grade gliomas are the most common malignant brain tumors, with poor prognosis due to recurrence and tumor infiltration after surgical removal and chemotherapy. Quiescent/slow cycling stem cells have been proposed to be one of the main players of tumor relapse but their involvement in in the infiltration remain unclear. Despite they have been described in mouse models or after transcriptional profiling of human tumor samples, their direct visualization, targeting and ablation remains a challenge. Here, we identified a population of malignant cells expressing Prominin-1 in a non-proliferating state in pediatric high-grade glioma patients. We next used a fluorescent cell cycle sensor to visualize quiescence tumor cells in mouse brain cancer and human cancer organoids. In particular, we characterized them within the tumor revealing the invasiveness capacity of slow cycling tumor cells. Furthermore, we generated a new system to specifically trace and ablate such cells. Indeed, lineage tracing experiments allowed to trace quiescent Prom1 progeny in the tumors after temozolomide treatment. In addition, the selective ablation of quiescent Prom1+cells in mouse brain cancer reduced tumor infiltration and improved survival. Furthermore, time-lapse experiments showed that slow cycling cells are also able to infiltrate co-cultured human brain cancer organoids. Finally, using our new cancer organoid- and GBM spheroids-based models we identified a drug acting on quiescent cells leading to a reduction in cell invasion. Overall, our data show that quiescence/slow cycling cells are key driver of tumor invasiveness, the major malignant feature of high-grade brain cancer.

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Revolutionizing medical data sharing using advanced privacy enhancing technologies: Technical, legal and ethical synthesis

arXiv

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arXiv 2020年

作者： Scheibner, James Raisaro, Jean Louis Troncoso-Pastoriza, Juan Ramón Ienca, Marcello Fellay, Jacques Vayena, Effy Hubaux, Jean-Pierre Health Ethics and Policy Laboratory Department of Health Sciences and Technology ETH Zürich Zürich Switzerland Precision Medicine Unit Lausanne University Hospital Lausanne Switzerland Data Science Group Lausanne University Hospital Lausanne Switzerland Laboratory for Data Security School of Computer and Communication Sciences EPFL Lausanne Switzerland School of Life Sciences EPFL Lausanne Switzerland Host-Pathogen Genomics Laboratory Swiss Institute of Bioinformatics Lausanne Switzerland

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely on bespoke data sharing contracts. These contracts increase the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between two novel advanced privacy-enhancing technologies (PETs): Homomorphic Encryption and Secure Multiparty Computation (defined together as Multiparty Homomorphic Encryption or MHE). These PETs provide a mathematical guarantee of privacy, with MHE providing a performance advantage over separately using HE or SMC. We argue MHE fulfills legal requirements for medical data sharing under the General data Protection Regulation (GDPR) which has set a global benchmark for data protection. Specifically, the data processed and shared using MHE can be considered anonymized data. We explain how MHE can reduce the reliance on customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research whilst offering additional incentives for healthcare and research institutes to employ common data interoperability standards. © 2020, CC-BY.

关键词： data privacy

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From modular decomposition trees to rooted median graphs

arXiv

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arXiv 2021年

作者： Bruckmann, Carmen Stadler, Peter F. Hellmuth, Marc Helmholtz-Centre for Environmental Research Permoserstraße 15 LeipzigD-04318 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig 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 at Leipzig University Universität Leipzig 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 National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden

The modular decomposition of a symmetric map δ: X ×X → ϒ (or, equivalently, a set of symmetric binary relations, a 2-structure, or an edge-colored undirected graph) is a natural construction to capture key features of δ in labeled trees. A map δ is explained by a vertex-labeled rooted tree (T,t) if the label δ(x,y) coincides with the label of the last common ancestor of x and y in T, i.e., if δ(x,y) = t(lca(x,y)). Only maps whose modular decomposition does not contain prime nodes, i.e., the symbolic ultrametrics, can be exaplained in this manner. Here we consider rooted median graphs as a generalization to (modular decomposition) trees to explain symmetric maps. We first show that every symmetric map can be explained by "extended" hypercubes and half-grids. We then derive a a linear-time algorithm that stepwisely resolves prime vertices in the modular decomposition tree to obtain a rooted and labeled median graph that explains a given symmetric map δ. We argue that the resulting "tree-like" median graphs may be of use in phylogenetics as a model of evolutionary relationships. © 2021, CC BY.

关键词： Undirected graphs

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Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference

arXiv

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arXiv 2019年

作者： Cai, T. Tony Zhang, Anru R. Zhou, Yuchen Department of Statistics & Data Science The Wharton School University of Pennsylvania PhiladelphiaPA19104 United States Departments of Biostatistics & Bioinformatics Computer Science Mathematics and Statistical Science Duke University DurhamNC27710 United States

We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model – an actively studied topic in statistics and machine learning. In the noiseless case, matching upper and lower bounds on sample complexity are established for the exact recovery of sparse vectors and for stable estimation of approximately sparse vectors, respectively. In the noisy case, upper and matching minimax lower bounds for estimation error are obtained. We also consider the debiased sparse group Lasso and investigate its asymptotic property for the purpose of statistical inference. Finally, numerical studies are provided to support the theoretical results. © 2019, CC BY-SA.

关键词： Convex optimization

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Integrated querying and version control of context-specific biological networks

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database 2020年第2020期2020卷 baaa018-baaa018页

作者： Cowman, Tyler Coşkun, Mustafa Grama, Ananth Koyutürk, Mehmet Department of Computer and Data Sciences Case Western Reserve University Cleveland 44106 OH United States Center for Proteomics and Bioinformatics Case Western Reserve University Cleveland 44106 OH United States Department of Computer Engineering Abdullah Gül University Kayseri 38080 Turkey Department of Computer Science Purdue University West Lafayette 47906 IN United States

Motivation: Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. Results: We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. Conclusion: Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. © 2020 The Author(s) 2020. Published by Oxford University Press.

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In-silico structural and functional analysis of nonsynonymous single nucleotide polymorphisms in human FOLH1 gene

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In silico pharmacology 2025年第1期13卷 32页

作者： Abtin Tondar Muhammad Irfan Sergio Sánchez-Herrero Hafsa Athar Aleena Haqqi Asim Kumar Bepari Laura Calvet Liñán David Hervás Marin Department of Computer Science Multimedia and Telecommunication Interuniversity Doctoral Program in Bioinformatics Universitat Oberta de Catalunya Barcelona (UOC) Spain. Stanford Deep Data Research Center Stanford University Stanford USA. Atta-Ur-Rahman School of Applied Biosciences (ASAB) National University of Sciences and Technology (NUST) Islamabad Punjab Pakistan. School of Medical Laboratory Technology Minhaj University Lahore (MUL) Lahore Punjab Pakistan. Department of Pharmaceutical Sciences North South University (NSU) Dhaka Bangladesh. Telecommunications and Systems Engineering Department Universitat Autònoma de Barcelona (UAB) Sabadell Spain. Department of Applied Statistics and Operational Research and Quality Alcoy Universitat Politècnica de València (UPV) Alcoy Spain.

Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual’s vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehensive in-silico analysis to examine the structural and functional implications of nsSNPs within the Folate Hydrolase 1(FOLH1) gene, which encodes the Prostate-Specific Membrane Antigen (PSMA). A total of 504 SNPs were retrieved, and after filtering, 15 pathogenic nsSNPs were identified using five different in-silico tools. Three of these SNPs—R255H (rs375565491), R255C (rs201789325), and G168E (rs267602926)—were consistently predicted to be pathogenic across all in-silico tools. MutPred2 was used to predict the structural and functional consequences of the identified mutations. The analysis revealed multiple alterations in the PSMA protein, including changes in helical conformations, glycosylation patterns, transmembrane properties, and solvent accessibility. Furthermore, I-Mutant 2.0 analysis demonstrated a decrease in protein stability for most nsSNPs, except for rs267602926 (G168E), which was predicted to increase stability. Conservation analysis using ConSurf revealed varying degrees of amino acid conservation, with R255H and R255C identified as highly conserved residues, indicating their potential functional and structural significance. Additionally, post-translational modification (PTM) analysis indicated that while phosphorylation and methylation sites remained unchanged, specific glycosylation sites were lost in two pathogenic mutant variants (R255H and R255C), potentially affecting PSMA function and adversely impacting prostate cancer. Our findings highlight the importance of in silico studies to investigate the structural and functional impacts of FOLH1 nsSNPs on the PSMA protein. Such in silico studies can deepen our understanding of the roles of nsSNPs in prostate cancer onset, progression, and drug resistance.

关键词： FOLH1 In-silico structural and functional analyses PSMA PTM nsSNPs

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WorkflowHub: a registry for computational workflows

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Scientific data 2025年第1期12卷 1-19页

作者： Gustafsson, Ove Johan Ragnar Wilkinson, Sean R. Bacall, Finn Soiland-Reyes, Stian Leo, Simone Pireddu, Luca Owen, Stuart Juty, Nick Fernández, José M. Brown, Tom Ménager, Hervé Grüning, Björn Capella-Gutierrez, Salvador Coppens, Frederik Goble, Carole Australian BioCommons University of Melbourne Melbourne VIC Australia Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak Ridge TN United States Department of Computer Science The University of Manchester Manchester United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Studies Research and Development in Sardinia (CRS4) Cagliari Pula Italy Barcelona Supercomputing Center (BSC) Barcelona Spain Spanish National Bioinformatics Institute (INB) Barcelona Spain Leibniz Institute for Zoo- and Wildlife Research Berlin Germany Institut Pasteur Université Paris Cité Bioinformatics of Biostatistics Hub Paris 75015 France CNRS UMS 3601 Institut Français de Bioinformatique Evry France Albert-Ludwigs-Universität Freiburg Freiburg Germany VIB Data Core VIB Technologies Ghent Belgium

The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 800 workflows registered. © UT-Battelle, LLC and the Authors 2025.

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Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

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ImmunoInformatics 2023年 9卷

作者： Christian A Balbin Janelle Nunez-Castilla Vitalii Stebliankin Prabin Baral Masrur Sobhan Trevor Cickovski Ananda Mohan Mondal Giri Narasimhan Prem Chapagain Kalai Mathee Jessica Siltberg-Liberles Department of Biological Sciences College of Arts Science and Education Florida International University Miami United States Bioinformatics Research Group (BioRG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Department of Physics College of Arts Science and Education Florida International University Miami United States Machine Learning and Data Analytics Group (MLDAG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Biomolecular Sciences Institute Florida International University Miami United States Department of Human and Molecular Genetics Herbert Wertheim College of Medicine Florida International University Miami United States

Upon infection, foreign antigenic proteins stimulate the host's immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antigen called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the Immune Epitope database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in extended or coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.

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