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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Harco Leslie Hendric Spits Warnars Ford Lumban Gaol Yaya Heryadi Agung Trisetyarso Horacio Emilio Perez Sanchez Computer Science Department Bina Nusantara University Jakarta Indonesia Bioinformatics and High Performance Computing Research Group (BIO-HPC) Universidad Católica de Murcia (UCAM) Guadalupe Spain

Attribute Oriented Induction high level Emerging Pattern (AOI-HEP) as proposed new data mining technique has been success to mine frequent and similar patterns. Moreover, the AOI-HEP data mining technique has been improved with confidence mining pattern into two terms, where firstly give confidence mining pattern for each AOI-HEP pattern either frequent or similar pattern. Secondly, give confidence for each dataset as confidence AOI-HEP pattern between frequent and similar patterns. Confidence for each AOI-HEP pattern will show how interested each of AOI-HEP pattern between frequent and similar pattern, whilst confidence per dataset will show how interested each dataset between frequent and similar pattern. In this paper the mining of AOI-HEP has been extended to mine AOI-HEP association rules using the current AOI-HEP frequent pattern, since association rule deal with frequent pattern. Based from previous experiments upon 4 UCI machine learning datasets such as adult, breast cancer, census and IPUMS, there are 9 finding AOI-HEP frequent patterns where 8 from adult and breast cancer datasets respectively. Finally, there are 2 and 1 AOI-HEP association rules from adult and breast cancer datasets respectively.

关键词： Itemsets Data mining Breast cancer Government Conferences Heuristic algorithms

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How random is the random forest ? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative(ADNI) database

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Neural Regeneration research 2018年第6期13卷 962-970页

作者： Stavros I.Dimitriadis Dimitris Liparas Division of Psychological Medicine and Clinical Neurosciences School of Medicine Cardiff University Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University School of Psychology Cardiff University Neuroinformatics Group Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Neuroscience and Mental Health Research Institute Cardiff University MRC Centre for Neuropsychiatric Genetics and Genomics School of Medicine Cardiff University High Performance Computing Center Stuttgart (HLRS) University of Stuttgart Department of Informatics Aristotle University of Thessaloniki

Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines（behavioural neuroscience, genetics, cognitive psychology, etc.） working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest（RF） algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer＇s disease（AD）, the conversion from mild cognitive impairment（MCI） and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1 ^（st） position in an international challenge for automated prediction of MCI from MRI data.

关键词： random forest Alzheimer＇s disease mild cognitive impairment neuroimaging classification machine learning biomarker magnetic resonance imaging

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Editorial

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Journal of Reliable Intelligent Environments 2018年第4期4卷 187-187页

作者： Coronato, Antonio Augusto, Juan Carlos Department of Computer Science Research Group on Development of Intelligent Environments Middlesex University London United Kingdom Institute for High Performance Computing and Networking National Research Council Naples Italy

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The Forex Market as an Elastic Network Model

The Forex Market as an Elastic Network Model

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International Conference on Intelligent Environments (IE)

作者： Antonio V. Contreras Sergio Navarro Antonio Llanes Andrés Muñoz Horacio Pérez-Sánchez José M. Cecilia Artificial Intelligence Talentum Bioinformatics and High Performance Computing Research Group

The efficient market hypothesis (EMH) affirms that asset prices should reveal all available information. Therefore, it is impossible to "beat the market" always on a risk-adjusted basis since market prices should only respond to new information. Here, we propose a new model to validate the EMH that is inspired on an elastic network model. More specifically, we apply this comparison to Foreign Exchange (FOREX) market under some restrictive conditions. In our hypothesis, several interaction potentials are used to characterize the interaction between banks and each particular quotation. This hypothesis comes from the study of several natural systems, such as macromolecules in solution. An algorithm based on the Monte Carlo methods is also presented in order to predict the evolution of the system.

关键词： Springs Market research Computational modeling Prediction algorithms Annealing Predictive models Currencies

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Multi-objective evolutionary algorithm for evaluation of shape and electrostatic similarity

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AIP Conference Proceedings 2019年第1期2070卷

作者： S. Puertas-Martín J. L. Redondo H. Pérez-Sánchez P. M. Ortigosa Supercomputing: Algorithm Research Group Department of Computer Science ceiA3 University of Almería Almería Spain Bioinformatics and High Performance Computing Research Group Department of Computer Science Universidad Católica San Antonio de Murcia Murcia Spain

Information in chemistry field is increasing each year implying new databases and more available information. Different techniques are adopted to learn how to manage that information. Ligand-Based Virtual Screening methods help in this process. It consists on processing a compound against large databases containing up to millions of chemical compounds evaluating one or more properties. After screening, compounds with the most similar descriptors to those of the target compound are chosen for in-vitro analysis. There exist a large number of molecular descriptors to compare molecules, and in literature, they usually analyze them individually. Regarding that, in this work in progress, we propose a multi-objective algorithm where two descriptors are considered, shape and electrostatic potential similarity. Using these two objective functions, the new algorithm aims to achieve an optimal Pareto-front that allows expert eye to select the most suitable compounds according to the properties of the target compound. Different techniques have been developed to assure fast performance keeping high-quality results. The new algorithm has been compared with different algorithms from the state-of-the-art to evaluate the quality of its results using well-known databases.

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Valley times in the Spanish academic network 15

Valley times in the Spanish academic network

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15th IFIP/IEEE International Symposium on Integrated Network and Service Management, IM 2017

作者： Albandea, Sergio García-Dorado, José Luis Muelas, David De Vergara, Jorge E. López Aracil, Javier High Performance Computing and Networking Research Group Escuela Politécnica Superior Universidad Autónoma de Madrid Spain

ISBN: (纸本)9783901882890

The scheduling of bulk transfers such as database distribution, resources replication and security backups, and other non-time-critical tasks has a direct effect on both the performance and cost of a network. We propose to face this problem by studying the valley times - i.e., the minimum use during off-peak periods of a network - as suitable moments to carry out such tasks. To do so, we characterize them considering the valley-hour, which we define as the opposite to the well-known variable busy-hour. Our analysis, based on 6-year-long measurements from 12 points of presence in the Spanish research and Education Network (RedIRIS), has guided us to model the valley-hour as a Gaussian process. After that, we compare its behavior in different points and detect the main factors that explain its variance, finding significant heterogeneity. With the resulting conclusions, we have proposed a system to predict valley-hours in RedIRIS with errors below an hour for most of the cases. © 2017 IFIP.

关键词： Scheduling

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Application of functional feature extraction to the compression of network time series 15

Application of functional feature extraction to the compress...

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15th IFIP/IEEE International Symposium on Integrated Network and Service Management, IM 2017

作者： Muelas, David García-Dorado, José Luis De Vergara, Jorge E. López Aracil, Javier High Performance Computing and Networking Research Group Escuela Politécnica Superior Universidad Autónoma de Madrid Spain

ISBN: (纸本)9783901882890

Network management actions require the retention of data representing the temporal evolution of network state, mainly in the form of time series. Nonetheless, storing and exploiting those measurements is becoming a challenge as the production rate of such data is continuously increasing and data lasting for long time periods are used. To scale up the storage and improve both the analysis and visualization of network measurements, we apply Functional Principal Components Analysis (FPCA) to extract the most meaningful functional features for network time series, pruning those with low informational importance. We compare such algorithm with other state-of-the-art proposals, and show that it achieves lower error for the representation of atypical observations even with higher compression ratios. © 2017 IFIP.

关键词： Time series

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Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization

arXiv

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arXiv 2019年

作者： Yang, Yang Pesavento, Marius Luo, Zhi-Quan Ottersten, Björn Center for High Performance Computing Fraunhofer Institute for Industrial Mathematics Kaiserslautern67663 Germany Communication Systems Group Technische Universität Darmstadt Darmstadt64283 Germany Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States University of Luxembourg LuxembourgL-1855 Luxembourg

In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is selected and updated by solving the original optimization problem with respect to that block variable inexactly. More precisely, a local approximation of the original optimization problem is solved. The proposed algorithm has several attractive features, namely, i) high flexibility, as the approximation function only needs to be strictly convex and it does not have to be a global upper bound of the original function;ii) fast convergence, as the approximation function can be designed to exploit the problem structure at hand and the stepsize is calculated by the line search;iii) low complexity, as the approximation subproblems are much easier to solve and the line search scheme is carried out over a properly constructed differentiable function;iv) guaranteed (subsequence) convergence to a stationary point, even when the objective function does not have a Lipschitz continuous gradient. Interestingly, when the approximation subproblem is solved by a descent algorithm, (subsequence) convergence to a stationary point is still guaranteed even if the approximation subproblem is solved inexactly by terminating the descent algorithm after a finite number of iterations. These features make the proposed algorithm suitable for large-scale problems where the dimension exceeds the memory and/or the processing capability of the existing hardware. These features are also illustrated by several applications in signal processing and machine learning, for instance, network anomaly detection and phase retrieval. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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The chemical evolution of the solar neighbourhood for planet-hosting stars

arXiv

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arXiv 2023年

作者： Pignatari, Marco Trueman, Thomas C.L. Womack, Kate A. Gibson, Brad K. Côté, Benoit Turrini, Diego Sneden, Christopher Mojzsis, Stephen J. Stancliffe, Richard J. Fong, Paul Lawson, Thomas V. Keegans, James D. Pilkington, Kate Passy, Jean-Claude Beers, Timothy C. Lugaro, Maria ELKH Konkoly Thege M. út 15–17 BudapestH-1121 Hungary CSFK MTA Centre of Excellence Konkoly Thege Miklós út 15-17. BudapestH-1121 Hungary E. A. Milne Centre for Astrophysics Department of Physics and Mathematics University of Hull HU6 7RX United Kingdom INAF - Turin Astrophysical Observatory Via Osservatorio 20 Pino Torinese10025 Italy INAF - Institute of Space Astrophysics and Planetology Via Fosso del Cavaliere 100 Rome00133 Italy ICSC National Research Centre for High Performance Computing Big Data and Quantum Computing Via Magnanelli 2 Casalecchio di Reno40033 Italy Department of Astronomy and McDonald Observatory The University of Texas AustinTX78712 United States NuGrid Collaboration Joint Institute for Nuclear Astrophysics Center for the Evolution of the Elements United States Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Pázmány Péter sétány 1/c BudapestH-1117 Hungary H.H. Wills Physics Laboratory University of Bristol Tyndall Avenue BristolBS8 1TL United Kingdom Centre of Excellence for Data Science Artificial Intelligence and Modelling University of Hull HU6 7RX United Kingdom Astrophysics Group Lennard-Jones Laboratories Keele University KeeleST5 5BG United Kingdom School of Physical Sciences The Open University Milton KeynesMK7 6AA United Kingdom Max Planck Institute for Intelligent Systems Tübingen Germany Department of Physics and Astronomy University of Notre Dame Notre DameIN46556 United States ELTE Eötvös Loránd University Institute of Physics Pázmány Péter sétány 1/A Budapest1117 Hungary School of Physics and Astronomy Monash University VIC3800 Australia

Theoretical physical-chemical models for the formation of planetary systems depend on data quality for the Sun’s composition, that of stars in the solar neighbourhood, and of the estimated "pristine" compositions for stellar systems. The effective scatter and the observational uncertainties of elements within a few hundred parsecs from the Sun, even for the most abundant metals like carbon, oxygen and silicon, are still controversial. Here we analyse the stellar production and the chemical evolution of key elements that underpin the formation of rocky (C, O, Mg, Si) and gas/ice giant planets (C, N, O, S). We calculate 198 galactic chemical evolution (GCE) models of the solar neighbourhood to analyse the impact of different sets of stellar yields, of the upper mass limit for massive stars contributing to GCE (Mup) and of supernovae from massive-star progenitors which do not eject the bulk of the iron-peak elements (faint supernovae). Even considering the GCE variation produced via different sets of stellar yields, the observed dispersion of elements reported for stars in the Milky Way disk is not reproduced. Among others, the observed range of super-solar [Mg/Si] ratios, sub-solar [S/N], and the dispersion of up to 0.5 dex for [S/Si] challenge our models. The impact of varying Mup depends on the adopted supernova yields. Thus, observations do not provide a constraint on the Mup parametrization. When including the impact of faint supernova models in GCE calculations, elemental ratios vary by up to 0.1-0.2 dex in the Milky Way disk;this modification better reproduces observations. Copyright © 2023, The Authors. All rights reserved.

关键词： Dispersions

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