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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoyu Jiang Naoki Nariai Martin Steffen Simon Kasif David Gold Eric D. Kolaczyk Department of Mathematics and Statistics Boston University Boston MA USA Bioinformatics Program Boston University Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA Boston University Boston MA US

The study of gene function is critical in various genomic and proteomic fields. Due to the availability of tremendous amounts of different types of protein data, integrating these datasets to predict function has become a significant opportunity in computational biology. In this paper, to predict protein function we (i) develop a novel Bayesian framework combining relational,hierarchical and structural information with improvement in data usage efficiency over similar methods, and (ii) propose to use it in conjunction with an integrative protein-protein association network, STRING (Search Tool for the Retrieval of INteracting Genes/proteins), which combines information from seven different sources. At the heart of our work is accomplishing protein data integration in a concerted fashion with respect to algorithm and data source. Method performance is assessed by a 5-fold cross-validation in yeast on selected terms from the Molecular Function ontology in the Gene Ontology database. Results show that our combined use of the proposed computational framework and the protein network from STRING offers substantial improvements in prediction. The benefits of using an aggressively integrative network, such as STRING, may derive from the fact that although it is likely that the ultimate gene interaction matrix (including but not limited to protein-protein, genetic, or regulatory interactions) will be sparse, presently it is still known only incompletely in most organisms, and thus the use of multiple distinct data sources is rewarded.

关键词： Ontologies Protein engineering Genomics Bioinformatics Proteomics Computational biology Bayesian methods Information retrieval Heart Fungi

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Monitoring the navy's strategic reserve of ozone-depleting substances

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NAVAL ENGINEERS JOURNAL 2002年第4期114卷 95-104页

作者： Smith, DE Breslin, DA Toms, GS Norton, P DENNIS E. SMITH PH.D. is currently a statistical consultant for GEO-CENTERS INC. He received a B.A. in Mathematics (magna cum laude)from Wabash College and an M.S. in mathematics an M.S. in statistics and a Ph.D. in statistics from the University of Wisconsin. From November 1973 until February 1999 he was president and principal statistician at Desmatics Inc. where he participated in a number of studies involving the application of statistical methods to various technical problems in the Navy's environmental-protection and auxiliary-machinery programs. Dr. Smith was instrumental in developing the statistical procedures underlying the Navy's ODS reserve monitoring plan. DAVID A. BRESLIN P.E. is the director of technical operations for the Naval Surface Warfare Center and chairs the ASNE/SNAME Joint Committee on Environmental Engineering. He received a master of science in aerospace engineering from Virginia Tech a master of engineering administration in industrial and systems engineering from Virginia Tech and a bachelor of engineering in mechanical engineering from Stevens Institute of Technology. From 1993 to 1997 he was the program manager of the Naval Sea Systems Command's CFC and Halon Elimination Program and played a critical role in establishing the Navy's strategic reserve of ozone-depleting substances. Because of his many efforts he received a Stratospheric Ozone Protection Award in 1995 from the U.S. Environmental Protection Agency in recognition of “exceptional contributions to global environmental protection”. GREGORY S. TOMS P.E. is the CFC &Halon Elimination Program Manager for the Naval Sea Systems Command (NAVSEA). He received a master of science degree in mechanical engineering from the University of Maryland and a B.S. degree in mechanical engineering from West Virginia University. From 1984 to 1997 he was employed at the Naval Surface Warfare Center Carderock Division Annapolis Detachment (formerly David Taylor Research Center)where he worked on the development of advanced cent

The domestic production of the most powerful ozone-depleting substances (ODSs) has permanently ceased and the abundant supplies of a number of refrigerants, fire-fighting agents, and solvents, once taken for granted, are now a thing of the past. The Navy has adopted a successful strategy for addressing the threat posed by the unavailability of ODSs. That strategy includes relying on a strategic reserve of ODSs. Considering that the strategic reserve will play a critical role in sustained Fleet operations into the middle of the century, it is important to record the history of the reserve and to document lessons learned in order to educate future generations that may face similar challenges. This is the third of three papers that cover all aspects of the Navy's strategic reserve of ODSs. Breslin (1999) documented the history of the Navy's effort to size, establish, and monitor a reserve designed to support Fleet operations until the middle of the century. Breslin, Smith, and Toms (2000) addressed in detail the process used to estimate the required size of the reserve, including the survey method, statistical analyses, and sizing algorithms. This paper discusses the Navy's plan for monitoring the Navy's strategic reserves of ODSs and describes the methods used in the monitoring process. In particular it discusses the underlying assumptions and statistical techniques associated with the Navy's prediction of the expected drawdown of the reserves. A comparison of actual data, collected since the strategic reserves began operations in 1995, with the predicted reserve drawdown, is included in the paper.

关键词： Marine engineering

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Optimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approach

Mendel

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Mendel 2023年第2期29卷 261-272页

作者： Supriyadi, Didi Purwanto, Purwanto Warsito, Budi Doctorate Program of Information Systems School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia Department of Information System Faculty of Informatics Institut Teknologi Telkom Purwokerto Banyumas Indonesia

The features present in large datasets significantly affect the performance of machine learning models. Redundant and irrelevant features will be rejected and cause a decrease in machine learning model performance. This paper proposes HyFeS-ROS-ANN: Hybrid Feature Selection and Resampling combination method for binary classification using artificial neural network multilayer perceptron (MLP). The first stage of this approach is to use a combination of two feature selection methods to select essential features that are highly correlated with model performance. The second stage of this approach is to use a combination of resampling methods to handle unbalanced data classes. Both approaches are applied to the academic performance classification model using the MLP neural network. This research dataset is obtained using three-dimensional (3D) frameworks such as the Big Five Personality to determine the Personality that affects academic performance from the student dimension, the Family Influence Scale (FIS), which measures factors that affect academic performance from the family dimension, and Higher Education Institutions Service Quality (HEISQUAL) to measure service quality and its influence on academic performance from the Education institution dimension. Previous research shows that the CoR-ANN algorithm has a model accuracy rate of 94%. The research results based on the dataset show that our proposed method can improve accuracy by selecting more relevant and essential features in improving model performance. The results show that the features are reduced from 135 to 108, while the HyFS-ROS-ANN model for binary classification accuracy increases to 100%. © 2023, Brno University of Technology. All rights reserved.

关键词： Feature Selection

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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International Conference on Data and Software engineering (ICODSE)

作者： Anteng Widodo Adian Fatchur Rochim Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Department of Information System Faculty of Engineering Muria Kudus University Kudus Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future.

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A statistical method for estimating the life expectancy of certain shipboard equipment

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NAVAL ENGINEERS JOURNAL 2002年第1期114卷 27-32页

作者： Smith, DE Breslin, DA Toms, GS DENNIS E. SMITH PH.D. is a statistical consultant for Geo-Centers Inc. He received a B.A. in mathe-matics (magna cum laude) from Wabash College and an M.S. in mathematics an M.S. in statistics and a Ph.D. in statistics from the University of Wisconsin. From November 1973 until February 1999 he was President and Principal Statistician at Desmatics Inc. where he participated in a number of studies involving the application of statistical methods to various technical problems in the Navy's environmental-protection and auxiliary-machinery programs. Dr. Smith was instrumental in developing the statistical procedures underlying the Navy's ODS reserve monitoring plan. DAVID A. BRESLIN P.E. is the Director of Technical Operations for the Naval Surface Warfare Center and is the chairperson of ASNE's Committee on Environmental Engineering. He received a master of science degree in aerospace engineering from Virginia Tech a master of engineering administration in indus-trial & systems engineering from Virginia Tech and a bachelor of engineering in mechanical engineering from Stevens Institute of Technology. From 1993 to 1997 he was the program manager of the Naval Sea Systems Command's CFC & Halon Elimination Program and played a critical role in establishing the Navy's strategic reserve of ozone-depleting substances. Because of his many efforts he received a Stratospheric Ozone Protection Award in 1995 from the U.S. Environmental Protection Agency in recogni-tion of “exceptional contributions to global environmental protection”. GREGORY S. TOMS P.E. is the CFC & Halon Elimination Program Manager for the Naval Sea Systems Command (NAVSEA). He received a master of science degree in mechanical engineering from the University of Maryland and a B.S. degree in mechanical engineering from West Virginia University. From 1984 to 1997 he was employed at the Naval Surface Warfare Center Carderock Division Annapolis Detachment (formerly David Taylor Research Center) where he worked on the development of advanced cent

This paper reviews an inexpensive statistical-sampling methodology that can be used to determine the attrition rates of certain types of shipboard equipment. The methodology relies on surveys that are conducted in conjunction with regularly scheduled ship visits over a period of time and a statistical analysis of the survey results. The statistical technique allows for reasonably accurate estimates to be made of attrition rates. The specific example cited in this paper is related to galley-type refrigeration equipment, such as salad chillers, beverage dispensers, et cetera. In the case of galley-type refrigeration equipment, the Navy needs to know whether the Fleet's inventory of equipment relying on Class I Ozone-Depleting Substances (ODSs) will be replaced through natural attrition by the end of fiscal year (FY) 2005, at which time supplies of ODS-based refrigerants will no longer be available for use by ship galleys. This paper outlines the process used to gather data, the analysis of the data, and concludes that the Navy's inventory of galley equipment using Class I ODSs will be replaced through natural attrition by the end of FY05. The methodology reviewed by this paper has wider applications. For various types of shipboard equipment, the methodology can also be used to estimate with reasonable accuracy life expectancies, configurations, conditions, and other items of interest.

关键词： Ship equipment

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Additive Poisson regression via forced categorical covariates and generalized fused Lasso 27

Additive Poisson regression via forced categorical covariate...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Yamamura, Mariko Ohishi, Mineaki Yanagihara, Hirokazu Department of Statistics Radiation Effects Research Foundation 5-2 Hijiyama Park Minami-ku Hiroshima732-0815 Japan Center for Data-driven Science and Artificial Intelligence Tohoku University 41 Kawauchi Aoba-ku Miyagi Sendai980-8576 Japan Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan

In this study, we use the log-linear link function and propose a generalized fused Lasso (GFL) Poisson regression model in which the nonlinear trend is discretely represented by categorical covariates in the additive model. We use the coordinate descent algorithm for the estimation and show that the optimal solution in a coordinate axis can be found explicitly. To demonstrate the proposed approach, we analyze Japanese crime data. Simulation results showed a fitness ratio for true fusion to be more than 90% in total, demonstrating the reliability of the estimates. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Additive Poisson regression Coordinate descent algorithm Generalized fused Lasso Spatio-temporal analysis

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VLSI Design of Tree-Based Inference for Low-Power Learning Applications

VLSI Design of Tree-Based Inference for Low-Power Learning A...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Brunno A. Abreu Mateus Grellert Sergio Bampi Graduate Program in Microelectronics (PGMICRO) INF-UFRGS Federal University of Rio Grande do Sul Porto Alegre Brazil Department of Informatics and Statistics − INE Federal University of Santa Catarina (UFSC) Florianópolis Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

The use of Machine Learning techniques in battery-powered devices has increased in recent years. Therefore, evaluating power-accuracy trade-offs of inference models has become important. This work explores decision trees architectures, analyzing the effects of model complexity and approximation in power and accuracy. By quantizing the inputs to limited widths, we increased the accuracy of the models in up to 8.7% compared to precise cases. In terms of power, the variation in model complexity was more significant than in input width, as we obtained reductions of up to 97% by decreasing the tree depth, compared to 88% when decreasing the width.

关键词： Complexity theory Hardware Integrated circuit modeling Training Analytical models Decision trees Quantization (signal)

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The Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging 23rd

The Constrained Network-Based Statistic: A New Level of Infe...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Noble, Stephanie Scheinost, Dustin Department of Radiology and Biomedical Imaging Yale University New HavenCT United States Department of Biomedical Engineering Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States Interdepartmental Neuroscience Program Yale University New HavenCT United States Child Study Center Yale University New HavenCT United States

ISBN: (纸本)9783030597276

Neuroimaging research aimed at dissecting the network organization of the brain is poised to flourish under major initiatives, but converging evidence suggests more accurate inferential procedures are needed to promote discovery. Inference is typically performed at the cluster level with a network-based statistic (NBS) that boosts power by leveraging known dependence within the local neighborhood. However, existing NBS methods overlook another important form of dependence—shared membership in large-scale brain networks. Here, we propose a new level of inference that pools information within predefined large-scale networks: the Constrained Network-Based Statistic (cNBS). We evaluated sensitivity and specificity of cNBS against existing standard NBS and threshold-free NBS by resampling task data from the largest openly available fMRI database: the Human Connectome Project. cNBS was most sensitive to effect sizes below medium, which accounts for the majority of ground truth effects. In contrast, threshold-free NBS was most sensitive to higher effect sizes. Ground truth maps showed grouping of effects within large-scale networks, supporting the relevance of cNBS. All methods controlled FWER as intended. In summary, cNBS is a promising new level of inference for promoting more valid inference, a critical step towards more reproducible discovery in neuroscience. © 2020, Springer Nature Switzerland AG.

关键词： statistics

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Air Pollution Steady-State Advection-Diffusion Equation: The General Three-Dimensional Solution

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Journal of Environmental Protection 2012年第9期3卷 1124-1134页

作者： Daniela Buske Marco Túllio Vilhena Tiziano Tirabassi Bardo Bodmann Department of Mathematics and Statistics (IFM/DME) Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program in Mechanical Engineering Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Institute of Atmospheric Sciences and Climate (ISAC) National Research Council (CNR) Bologna Italy

Atmospheric air pollution turbulent fluxes can be assumed to be proportional to the mean concentration gradient. This assumption, along with the equation of continuity, leads to the advection-diffusion equation. Many models simulating air pollution dispersion are based upon the solution (numerical or analytical) of the advection-diffusion equation as- suming turbulence parameterization for realistic physical scenarios. We present the general steady three-dimensional solution of the advection-diffusion equation considering a vertically inhomogeneous atmospheric boundary layer for arbitrary vertical profiles of wind and eddy-diffusion coefficients. Numerical results and comparison with experimental data are shown.

关键词： Advection-Diffusion Equation Analytical Solution Laplace Transform Air Pollution Modeling Atmospheric Boundary Layer Eulerian Models

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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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