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arXiv 2021年

作者： Andersen, Jakob Lykke Flamm, Christoph Merkle, Daniel Stadler, Peter F. Department of Mathematics and Computer Science University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Bioinformatics Group Department of Computer Science University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Competence Center for Scalable Data Services and Solutions Dresden-Leipzig University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Leipzig Research Center for Civilization Diseases University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Centre for Biotechnology and Biomedicine University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Autocatalysis is a deceptively simple concept, referring to the situation that a chemical species X catalyzes its own formation. From the perspective of chemical kinetics, autocatalysts show a regime of superlinear growth. Given a chemical reaction network, however, it is not at all straightforward to identify species that are autocatalytic in the sense that there is a sub-network that takes X as input and produces more than one copy of X as output. The difficulty arises from the need to distinguish autocatalysis e.g. from the superposition of a cycle that consumes and produces equal amounts of X and a pathway that produces X. To deal with this issue, a number of competing notions, such as exclusive autocatalysis and autocatalytic cycles, have been introduced. A closer inspection of concepts and their usage by different authors shows, however, that subtle differences in the definitions often makes conceptually matching ideas difficult to bring together formally. In this contribution we make some of the available approaches comparable by translating them into a common formal framework that uses integer hyperflows as a basis to study autocatalysis in large chemical reaction networks. As an application we investigate the prevalence of autocatalysis in metabolic networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Growth kinetics

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Deep Learning for Imbalance data Classification using Class Expert Generative Adversarial Network

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Procedia Computer science 2018年 135卷 60-67页

作者： Fanny Tjeng Wawan Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

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.

关键词： imbalance data classification deep learning generative adversarial network

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A Model of Visual Intelligent System for Genus Identification of Fish in the Siluriformes Order

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Taufik Budhi Pramono R Ardharyan Islamy Saprudin Joni Johanda Putra Teddy Suparyanto Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Aquaculture Study Program Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Aquatic Resources Management Brawijaya University Malang East Java Indonesia Graduate Program Aquatic Resources Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.

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Generalized Space Time Autoregressive of Chili Prices 3

Generalized Space Time Autoregressive of Chili Prices

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Jamilatuzzahro Caraka, Rezzy Eko Herliansyah, Riki Asmawati, S. Sari, Dian Megah Pardamean, Bens Bandung Institute of Technology Actuarial Science Bandung Indonesia Bina Nusantara University Department of Statistics Bioinformatics and Data Science Research Center Bandung Jakarta11480 Indonesia Department of Mathematics Kalimantan Institute of Technology Kalimantan Timur Indonesia Informatics Faculty of Engineering West Sulawesi University Sulawesi Barat Indonesia

ISBN: (纸本)9781538658215

Indonesian people are mostly like spicy. Therefore, Chili becomes an ingredient in cooking that cannot be separated from Indonesian people. In certain months there is too much demand from the public and inversely proportional to the stock of chili. So, appreciate the chili prices to rise. In the heart of statistics, several methods can be used as forecasting as well as data series from univariate, bivariate and multivariate cases. The reason for choosing the method is following the analysis needs and available data. One of the popular methods in the multivariate case is Generalized Spacetime Autoregressive (GSTAR) the advantage of this method is that it can capture the characteristics of location-based data as well as time. For this reason, in this paper, we used chili prices data in 4 major cities in Java Province, Indonesia. Such as Jakarta, Bandung, Semarang, and Yogyakarta. As a result, GSTAR (21) I (1with inverse weighted be the best models because it fulfilled white noise and normal multivariate assumption with the lowest RMSE and MAPE. © 2018 IEEE.

关键词： Costs

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Indirect identification of horizontal gene transfer

arXiv

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arXiv 2020年

作者： Schaller, David Lafond, Manuel Stadler, Peter F. Wieseke, Nicolas Hellmuth, Marc Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Department of Computer Science Université de Sherbrooke 2500 boul. de l’Université SherbrookeQCJ1K 2R1 Canada 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 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 Swarm Intelligence and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 LeipzigD-04109 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Several implicit methods to infer Horizontal Gene Transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question "how much information about all HGT events is contained in LDT graphs" with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs. © 2020, CC BY.

关键词： Phylogenetic

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Health Assistant Wearable-Based data science System Model: A Pilot Study 3

Health Assistant Wearable-Based Data Science System Model: A...

引用

3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Budiarto, Arif Febriana, Trisna Suparyanto, Teddy Caraka, Rezzy Eko Pardamean, Bens Computer Science Department Bina Nusantara University School of Computer Science Jakarta11480 Indonesia Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Indonesia Faculty of Science National University of Malaysia School of Mathematical Science Bangi43000 Malaysia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

The rapid development of Information and Communication Technology (ICT) has an impact on the innovation of new technology that gives benefits for health. One of its latest development is wearable device technology. Nowadays, the growth of this technology market is very positive. But unfortunately the growth of this technology is not followed by the availability of supporting applications that provide maximum benefits for users by utilizing personal health and activity data. This paper is intended to provide a report of our initial research in developing a health assistant solution that utilizes wearable device generated data. The whole research process is executed using Design Thinking approach that put the users as the main consideration. This initial research is started by designing a field research that can potentially gathered users' data generated from wearable device. A series of statistical and machine learning techniques are presented to analyze time series data to create a model. Finally, to implement the model, a mobile application prototype is also proposed. The outcomes from this initial research will be used to develop a fully working solution. © 2018 IEEE.

关键词： Learning systems

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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter 3

An Improved Version of Texture-based Foreground Segmentation...

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

作者： Muchtar, Kahlil Rahman, Faris Cenggoro, Tjeng Wawan Budiarto, Arif Pardamean, Bens Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Textures

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Exploring depression treatment response by using polygenic risk scoring across diverse populations

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The American Journal of Human Genetics 2025年

作者： Sandra Lapinska Aditya Pimplaskar Zhuozheng Shi Yi Ding Clara Frydman-Gani Kangcheng Hou Vidhya Venkateswaran Kristin Boulier Loes M. Olde Loohuis Bogdan Pasaniuc Graduate Group in Genomics and Computational Biology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA USA Division of Population Sciences Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA Data and Technology Advancement Scholar National Institutes of Health Office of Data Science Strategy National Institute of Dental and Craniofacial Research Bethesda MD USA Department of Medicine Division of Cardiology University of California Los Angeles Los Angeles CA USA Center for Neurobehavioral Genetics Semel Institute for Neuroscience and Human Behavior David Geffen School of Medicine at UCLA Los Angeles CA USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Institute for Precision Health University of California Los Angeles Los Angeles CA USA Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Institute of Biomedical Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Treatment-resistant depression (TRD), usually defined as limited or no response to at least two antidepressants, occurs in approximately one-third of individuals diagnosed with major depressive disorder (MDD). Studies of individuals of European ancestry highlight a genetic overlap between TRD and MDD. We analyzed two large and diverse biobanks, the UCLA ATLAS Community Health Study (ATLAS) and the All of Us Research Program (AoU), to test for associations between a polygenic score for major depression (MDD-PGS) and TRD. Compared to treatment responders, TRD individuals have higher MDD-PGS across all ancestries. MDD-PGS was significantly associated with response to selective serotonin reuptake inhibitors in individuals of European and Hispanic/Latin American genetic ancestries in both biobanks. In AoU, a decreased MDD-PGS was observed in response to tricyclics or serotonin modulators in individuals of European American ancestry and in response to serotonin and norepinephrine reuptake inhibitors in individuals of African American ancestry. ATLAS found that MDD-PGS showed lower odds of responding to atypical agents than did TRD in MDD-affected individuals belonging to the Hispanic/Latin American group, MDD-PGS was associated with atypical agents. Overall, by leveraging larger sample sizes from two diverse biobanks, we provide new insights into antidepressant response and treatment specificity for MDD in individuals of diverse genetic ancestries.

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866P Prognostic performance of a genome-wide methylome enrichment platform in head and neck cancer

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Annals of Oncology 2023年 34卷 S561-S561页

作者： G. Liu J. Zhang O. Hall J. Bergener Y. Wang B. Brown J. Min S.Y. Shen M. Pienkowski S.H. Huang L. Ailles A. Polio E. Sosa S. Bratman B. Allen K. Brown A. Licon A-R. Hartman D.D. De Carvalho B. Ho Park Medical Oncology Department UHN - University Health Network - Princess Margaret Cancer Center Toronto ON Canada Biometrics Adela Inc. Foster City CA USA Data Science and Bioinformatics Adela Inc. Foster City CA USA Research & Development Adela Inc. Foster City CA USA Radiation Oncology Department UHN - University Health Network - Princess Margaret Cancer Center Toronto ON Canada Medical Biophysics UHN - University Health Network - Princess Margaret Cancer Center Toronto ON Canada Executive Adela Inc. Foster City CA USA Clinical Development Adela Inc. Foster City CA USA Medical Affairs Department Adela Inc New Haven CA USA Hematology Oncology Vanderbilt Ingram Cancer Center Nashville TN USA

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Optimized memory reveals a survival strategy for a multi-agent competition

arXiv

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arXiv 2019年

作者： Khalighi, Moein Ardalankia, Jamshid Rizi, Abbas Karimi Ebadi, Haleh Jafari, Gholamreza Physics Department Shahid Beheshti University G.C. Evin Tehran19839 Iran Department of Financial Management Shahid Beheshti University G.C. Evin Tehran19839 Iran Bioinformatics Institute for Computer Science Leipzig University Härtelstrasse 16-18 Leipzig04107 Germany Department of Network and Data Science Central European University Budapest1051 Hungary

We present a fractional model to clarify the dynamical evolution of how and under what circumstances-in a multi-agent economic area-newly founded ventures prolong their existence throughout the market. Since the increase in the number of newly-established firms in a market may generally lead to the reduction of the market share of players in the contest, after a while some may be faded out in the market. In this regard, considering a long term evolution, all firms are exposed to be eliminated and to give up their market share. That is why managers have the concern of attracting more customers to satisfy at least a minimum market share, and ideally prolong their firm's lifetime. On the other side, due to the establishment of more newly founded ventures in a certain industry, it is vital to be flexible and run new strategies. The triggered strategies not only must be on time but also must be led to lengthening the additional survival time. In the present study, it is shown that the existence of memory or lack of memory in the evolution may not be the case, but also having a strategy plays a significant role in survival. Hence, in spite of exposure to the risk of missing market share, the recalling of the past offered services and products of the weaker firm-compared with the whole market-may prolong the time-length of surviving in the market. In this regard, managers corresponding to the weaker firm will be able to quantitatively make decisions toward two main concepts, firstly the strategical usage of their firms' reputation and secondly, launching new features and services. The aim behind this optimization is to delay the time of reaching a minimum market share. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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