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21st IEEE Conference on Computational Intelligence in bioinformatics and Computational Biology, CIBCB 2024

作者： Petitjean, Timothe Millet, Hugo Araújo, Mariana F. P. Moioli, Renan C. Vargas, Patricia A. Ranieri, Caetano M. Heriot-Watt University School of Mathematical And Computer Science Edinburgh United Kingdom Health Sciences Center Department of Physiological Sciences Vitoria Brazil Bioinformatics Multidisciplinary Environment Digital Metropolis Institute Natal Brazil Institute of Geosciences and Exact Sciences Rio Claro Brazil

ISBN: (纸本)9798350356632

In this paper, we introduce a new approach to the study of Parkinson's Disease (PD) through the implementation of a neurorobotics model via the integration of computational neuroscience with the latest robotic technology. Using the iCub as our robot platform, we adapted a computational model of the Basal Ganglia-Thalamo-Cortical (BG-T-C) circuit to investigate the efficiency of Deep Brain Stimulation (DBS) in mitigating PD's motor *** to our methodology is the use of closed-loop DBS, a technique that adjusts stimulation parameters in real time based on specific kinematic and neuronal biomarkers of PD severity, such as fluctuations in beta band activity and tremor movements. This dynamic approach allows for a more personalized and efficient treatment regimen compared to traditional, static open-loop systems, which cannot adapt to the patient's changing *** findings of our study corroborate the feasibility of using a neurorobotics model to simulate the motor symptoms of PD and provide evidence that closed-loop DBS can effectively modulate these symptoms. This was achieved by reducing the power spectral density at the beta-band frequency range (8-30 Hz) of the neural activity to below threshold levels and revealing a complex relationship between the severity of the disease and the effectiveness of the treatment. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Computational History:Challenges and Opportunities of Formal Approaches

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Journal of Social Computing 2023年第3期4卷 232-242页

作者： Jürgen Jost Roberto Lalli Manfred D.Laubichler Eckehard Olbrich Jürgen Renn Guillermo Restrepo Peter F.Stadler Dirk Wintergrün the Max Planck Institute for Mathematics in the Sciences Leipzig 04103Germany the Santa Fe Institute Santa FeNM 87501USA the Max Planck Institute for the History of Science Berlin 14195Germany the School of Complex Adaptive Systems and the Global Biosocial Complexity Initiative Arizona State UniversityTempeAZ 85287USA the Department of Computer Science and Interdisciplinary Center for Bioinformatics Leipzig UniversityLeipzig 04109Germany

We propose a program for a computational analysis,based on large scale datasets,of deep conceptual and formal structures,representing the mechanisms of historical transformations in different domains ranging from biological to social,cultural,and knowledge *** conceptualize such systems as consisting of complex multi-layer *** properties of such networks may explain the spreading of *** relations between the dynamics of interacting networks may help to identify *** systems may show path and context *** illustrate our approach by case studies from all those types of systems.

关键词： computational history history of science network analysis big data

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Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks

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Discover Artificial Intelligence 2023年第1期3卷 19页

作者： Siptroth, Julienne Moskalenko, Olga Krumbiegel, Carsten Ackermann, Jörg Koch, Ina Pospisil, Heike High Performance Computing in Life Sciences Technical University of Applied Sciences Wildau Wildau15745 Germany BIOMES NGS GmbH Schwartzkopfstraße 1 Wildau15745 Germany Department of Molecular Bioinformatics Institute of Computer Science Goethe University Frankfurt Frankfurt am Main60325 Germany

Background: Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients. Methods: Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways. Results: It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674). Conclusion: With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the bas

关键词： Neural networks

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Analysis of Ion Atmosphere Around Nucleosomes Using Supercomputer MD Simulations

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2022年第2期9卷 56-67页

作者： Kosarim, Nikita A. Armeev, Grigoriy A. Kirpichnikov, Mikhail P. Shaytan, Alexey K. Department of Biology Lomonosov Moscow State University Moscow Russia Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry Russian Academy of Sciences Moscow Russia Bioinformatics Lab Faculty of Computer Science HSE University Moscow Russia

The nucleosome is the basic unit of eukaryotic DNA compaction. It consists of about 147 base pairs wrapped around an octamer of histone proteins. Nucleosomal dynamics provides the availability of packaged DNA for various factors that carry out the vital processes associated with chromatin. It is not completely known how the structure and dynamics of the nucleosome depends on the ionic environment. The current researches do not give an unambiguous answer and often contradict each other. In this paper, we demonstrate supercomputer molecular dynamics simulations of nucleosome models surrounded by monovalent sodium and potassium cations. Analyzing the trajectories, we have shown the details of the distribution of sodium and potassium ions around the linker DNA, nucleosomal DNA at the sites of nucleosomal opening, and histone residues involved in the process of nucleosomal breathing. We have demonstrated the mobility of DNA linkers and the process of nucleosomal unwrapping in various ionic environments, and also assessed the probable mechanisms of the dependence of nucleosome unwrapping on the type of ions in the system. Our study is intended to emphasize the importance of understanding the role of the ionic environment in the functioning of chromatin. © The Authors 2022. This paper is published with open access at ***

关键词： Molecular dynamics

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach 8

Determining Variables Associated with Annual Oil Palm Yield:...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Elwirehardja, Gregorius Natanael Suparyanto, Teddy Miftakhurrokhmat Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta55282 Indonesia Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 RRʹ. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Machine Learning Oil Palm Yield SHAP XAI XGBoost

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Linking Glycan and Protein Data 14

Linking Glycan and Protein Data

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14th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2023

作者： Hayes, Catherine Daponte, Vincenzo Lisacek, Frederique Proteome Informatics Group SIB Swiss Institute of Bioinformatics Geneva1211 Switzerland Department of Computer Science University of Geneva Geneva1227 Switzerland Section of Biology University of Geneva Geneva1211 Switzerland

Glycosylation is a common and important post-translational modification. Using federated queries it is possible to position glycosylation in useful contexts such as glycoproteins, disease or cellular location. We present example queries to query glycan structures, glycosylated proteins and cellular location using GlySTreeM, GlyConnect and neXtProt. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Glycosylation

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Comparing microbial communities in mucilage and seawater samples: Metagenomic insights into mucilage formation in the Marmara Sea

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Environmental science and Pollution Research 2024年第48期31卷 58363-58374页

作者： Gundogdu, Aycan Nalbantoglu, Ozkan Ufuk Karis, Gizem Sarikaya, Ilknur Erdogan, Meryem Nisa Hora, Mehmet Aslan, Herdem Department of Microbiology and Clinical Microbiology Faculty of Medicine Erciyes University Kayseri Turkey Erciyes University Melikgazi Kayseri38280 Turkey Department of Computer Engineering Faculty of Engineering Erciyes University Kayseri Turkey Department of Molecular Biology and Genetics Gevher Nesibe Genome and Stem Cell Institute Erciyes University Kayseri Turkey Department of Bioinformatics Systems Biology Institute of Health Sciences Erciyes University Kayseri Turkey Enbiosis Biotechnology Istanbul Turkey Department of Biology Faculty of Science Canakkale Onsekiz Mart University Canakkale Turkey

Marine environments are subject to various naturally occurring phenomena, including marine snow and mucilage. In 2021, the rapid emergence of mucilage in the Marmara Sea raised concerns about its environmental impact. This study investigates the microbial communities in mucilage and seawater samples from the Marmara Sea using metagenomic-scale comparative analyses. The results indicate significant differences in microbial composition and diversity, with mucilage samples showing higher levels of polysaccharide biosynthesis-related enzymes. Over 50% of reads in mucilage samples remained unclassified (dark matter), highlighting unknown microbial taxa. Clean seawater was characterized by a higher presence of Euryarchaeota, Proteobacteria, and Rhodothermaeota, while Chlamydiae and Fusobacteria were dominant in mucilage. The study underscores the necessity for comprehensive metagenomic analyses to understand microbial roles in mucilage formation and persistence. Early detection of microbial shifts could serve as a warning system for mucilage outbreaks, aiding in the development of management strategies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Seawater

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Glycan semantic model applications in substructure search 13

Glycan semantic model applications in substructure search

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13th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2022

作者： Daponte, Vincenzo Hayes, Catherine Mariethoz, Julien Lisacek, Frederique Department of Computer Science University of Geneva Geneva1227 Switzerland Proteome Informatics Group SIB Swiss Institute of Bioinformatics Geneva1211 Switzerland

The complexity of glycan structures as branched tree-like molecules presents a challenge when trying to obtain a unique representation. Semantic web technologies proved to be useful in this domain, and thus the GlySTreeM knowledge base has been designed to tackle the search for complex residues substructures. This work aims to present some of the most relevant applications of this semantic platform and to prove its effectiveness through the results obtained. Copyright © 2022 for this paper by its authors.

关键词： Knowledge based systems

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Single-cell transcriptomics and epigenomics point to CD58-CD2 interaction in controlling primary melanoma growth and immunity

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Cancer Communications 2025年第4期45卷 465-470页

作者： Antonia Stubenvoll Maria Schmidt Johanna Moeller Max Alexander Lingner Chango Carolyn Schultz Olga Antoniadou Henry Loeffler-Wirth Stephan Bernhart Florian Groβe Beatrice Thier Annette Paschen Ulf Anderegg Jan CSimon Mirjana Ziemer Clara TSchoeder Hans Binder Manfred Kunz Department of Dermatology Venereology and AllergologyUniversity of Leipzig Medical CenterLeipzigGermany Interdisciplinary Center for Bioinformatics University of LeipzigLeipzigGermany Institute for Drug Discovery University of Leipzig Medical CenterLeipzigGermany Bioinformatics Group Department of Computer ScienceUniversity of LeipzigLeipzigGermany Department of Diagnostics Fraunhofer Institute of Cell Therapy and Immunology(IZI)LeipzigGermany Center for Scalable Data Analytics and Artificial Intelligence(ScaDS.AI)Dresden/Leipzig LeipzigGermany Department of Dermatology University Hospital EssenUniversity Duisburg-Essen and German Cancer Consortium(DKTK)Partner Site Essen/DüsseldorfEssenGermany

Immunotherapy is currently one of the most promising treatment options for malignant melanoma[1].To uncover new immunological targets for future treatment approaches,single-cell transcriptomic and epigenomic analyses ... 详细信息

关键词： malignant melanoma single cell transcriptomics melanocytic nevus immunity immunotherapy primary melanoma epigenomic analyses epigenomics cd cd interaction

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