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Interpreting and integrating big data in the life sciences

EMERGING TOPICS IN LIFE SCIENCES 2019年第4期3卷 335-341页

作者： Mangul, Serghei Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Univ Calif Los Angeles Inst Quantitat & Computat Biosci Los Angeles CA 90095 USA

Recent advances in omics technologies have led to the broad applicability of computational techniques across various domains of life science and medical research. These technologies provide an unprecedented opportunity to collect the omics data from hundreds of thousands of individuals and to study the gene-disease association without the aid of prior assumptions about the trait biology. Despite the many advantages of modern omics technologies, interpretations of big data produced by such technologies require advanced computational algorithms. I outline key challenges that biomedical researches are facing when interpreting and integrating big omics data. I discuss the reproducibility aspect of big data analysis in the life sciences and review current practices in reproducible research. Finally, I explain the skills that biomedical researchers need to acquire to independently analyze big omics data.

关键词： big data computational algorithms computational reproducibility omics technologies

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Practical Applications of Bayesian Reliability 1

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丛书名： Wiley series in quality & reliability engineering

2019年

作者： Yan Liu Athula I. Abeyratne

ISBN: (数字)9781119287995

ISBN: (纸本)9781119287971

Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers get started quickly, the book presents many Bayesian models that use JAGS and which require fewer than 10 lines of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding. Practical Applications of Bayesian Reliability starts by introducing basic concepts of reliability engineering, including random variables, discrete and continuous probability distributions, hazard function, and censored data. Basic concepts of Bayesian statistics, models, reasons, and theory are presented in the following chapter. Coverage of Bayesian computation, Metropolis-Hastings algorithm, and Gibbs Sampling comes next. The book then goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability; discuss Bayesian Hierarchical Models and their applications; present linear and logistic regression models in Bayesian Perspective; and more. Provides a step-by-step approach for developing advanced reliability models to solve complex problems, and does not require in-depth understanding of statistical methodology Educates managers on the potential of Bayesian reliability models and associated impact Introduces commonly used predictive reliability models and advanced Bayesian models based on real life applications Includes practical guidelines to construct Bayesian reliability models along with computer codes for all of the case studies JAGS and R codes are provided on an accompanying website to enable practit

关键词： reliability Bayesian reliability guide to Bayesian reliability Bayesian reliability guide Bayesian statistics introduction to Bayesian reliability Bayesian reliability applications reliability prediction product reliability Bayesian methods reliability engineering computational algorithms Bayesian models Bayesian modelling Markov Chain Monte Carlo (MCMC) sampling Bayesian analysis solving Bayesian problems engineering prediction problems reliability analysis system reliability estimation reliability testing design capability prediction accelerated life testing Bayesian reasoning Bayesian probability theory Bayes' theorem Bayesian Perspective Bayesian reliability modelling Practical Applications of Bayesian Reliability

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A Study on the Asymmetry of the Human Left and Right Pubic Symphyseal Surfaces Using High-Definition Data Capture and computational Shape Methods

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JOURNAL OF FORENSIC SCIENCES 2019年第2期64卷 494-501页

作者： Stoyanova, Detelina K. Algee-Hewitt, Bridget F. B. Kim, Jieun Slice, Dennis E. Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA Stanford Univ Dept Biol Stanford CA 94305 USA Univ Vienna Dept Anthropol A-1090 Vienna Austria

The pubic symphysis is among the most commonly used bilateral age indicators. Because of potential differences between right and left sides, it is necessary to investigate within-individual asymmetry, which can inflate age estimation error. This study uses 3D laser scans of paired pubic symphyses for 88 documented White males. Scan data are analyzed by numerical shape algorithms, proposed as an alternative to traditional visual assessment techniques. Results are used to quantify the within-individual asymmetry, evaluating if one side produces a better age-estimate. Relationships between the asymmetry and advanced age, weight, and stature are examined. This analysis indicates that the computational, shape-based techniques are robust to asymmetry (>80% of paired differences are within 10 years and >90% are within 15 years). For notably more asymmetric cases, differences in estimates are not associated with life history factors. Based on this study, either side can be used for age-at-death estimation by the computational methods.

关键词： forensic science age-at-death estimation pubic symphysis asymmetry 3D laser scans computational algorithms morphometrics

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computational Prediction of sRNA in

Computational Prediction of sRNA in

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作者： Sankalp Arya Vineet Dubey Deepak Sen Atin Sharma Ranjana Pathania

Small RNAs in bacteria are noncoding RNAs that act as posttranscriptional regulators of gene expression. Over time, they have gained importance as fine-tuners of expression of genes involved in critical biological processes like metabolism, fitness, virulence, and antibiotic resistance. The availability of various high-throughput strategies enable the detection of these molecules but are technically challenging and time-intensive. Thus, to fulfil the need of a simple computational algorithm pipeline to predict these sRNAs in bacterial species, we detail a user-friendly ensemble method with specific application in Acinetobacter spp. The developed algorithms primarily look for intergenic regions in the genome of related Acinetobacter spp., thermodynamic stability, and conservation of RNA secondary structures to generate a model input for the sRNAPredict3 tool which utilizes all this information to generate a list of putative sRNA. We confirmed the accuracy of the method by comparing its output with the RNA-seq data and found the method to be faster and more accurate for Acinetobacter baumannii ATCC 17978. Thus, this method improves the identification of sRNA in Acinetobacter and other bacterial species. less

关键词： RNA-seq Sequence Analysis Pairwise Sequence Alignment GenBank Acinetobacter baumannii Small RNA Noncoding RNA computational algorithms sRNAPredict3

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Power System Structural Vulnerability Assessment Based on an Improved Maximum Flow Approach

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IEEE TRANSACTIONS ON SMART GRID 2018年第2期9卷 777-785页

作者： Fang, Jiakun Su, Chi Chen, Zhe Sun, Haishun Lund, Per Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Huazhong Univ Sci & Technol Wuhan 430074 Hubei Peoples R China Energinet Dk Grid Planning Dept DK-7000 Fredericia Denmark

With the increasing complexity of the power grid and the concerns on major blackouts, there is an urgent need for an efficient and effective tool to assess the power system structural vulnerability. To tackle this challenge, this paper proposes a maximum flow-based complex network approach to identify the critical lines in a system. The proposed method consists of two major steps. First, the power network is modeled as a graph with edges (transmission lines, transformers, etc.) and nodes (buses, substations, etc.). The critical scenarios are identified by using the principal component analysis and convex hull. Then the second step is to use an improved maximum flow-based complex network approach for topology analysis. Weighted vertices in the network are considered, enabling taking the selected operating conditions into consideration when identifying the vulnerable lines. The proposed method is validated using the western Danish power system. The vulnerable lines in the network are ranked. Simulation results demonstrate the effectiveness of the method by intentional attacks and comparison with the planning strategy from the system operator.

关键词： Structural vulnerability complex networks maximum flow computational algorithms

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Dynamically Optimized Unstructured Grid (DOUG) for Analog Ensemble of numerical weather predictions using evolutionary algorithms

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COMPUTERS & GEOSCIENCES 2019年 133卷 104299-000页

作者： Hu, Weiming Cervone, Guido Penn State Univ Geoinformat & Earth Observat Lab Dept Geog University Pk PA 16802 USA Penn State Univ Geoinformat & Earth Observat Lab Inst CyberSci University Pk PA 16802 USA Natl Ctr Atmospher Res Res Applicat Lab POB 3000 Boulder CO 80307 USA

The Analog Ensemble is a statistical technique that generates probabilistic forecasts using a current deterministic prediction, a set of historical predictions, and the associated observations. It generates ensemble forecasts by first identifying the most similar past predictions to the current one, and then summarizing the corresponding observations. This is a computationally efficient solution for ensemble modeling because it does not require multiple numerical weather prediction simulations, but a single model realization. Despite this intrinsic computational efficiency, the required computation can grow very large because atmospheric models are routinely run with increasing resolutions. For example, the North American Mesoscale forecast system contains over 262 792 grid points to generate a 12 km prediction. The North American Mesoscale model generally uses a structured grid to represent the domain, despite the fact that certain physical changes occur non-uniformly across space and time. For example, temperature changes tend to occur more rapidly in mountains than plains. An evolutionary algorithm is proposed to dynamically and automatically learn the optimal unstructured grid pattern. This iterative evolutionary algorithm is guided by Darwinian evolutionary rule generation and instantiation to identify grid vertices. Analog computations are performed only at vertices. Therefore, minimizing the number of vertices and identifying their locations are paramount to optimizing the available computational resources, minimizing queue time, and ultimately achieving better results. The optimal unstructured grid is then reused to guide the predictions for a variety of applications like temperature and wind speed.

关键词： computational algorithms Ensemble modeling Evolutionary algorithms Unstructured grid

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RECOVERING THE INPUT SIGNALS OF DISCRETE DYNAMICAL SYSTEMS

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MEASUREMENT TECHNIQUES 2018年第11期60卷 1073-1080页

作者： Boikov, I. V. Krivulin, N. P. Penza State Univ Penza Russia

Methods for reconstructing input signals of dynamical systems with distributed parameters based on nonparametric and parametric identification methods are considered. The proposed methods enable reconstruction of the ... 详细信息

关键词： identification reduction to an ideal unit dynamic systems impulse transfer function computational algorithms

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Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma 3rd

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3rd International Workshop on Brain-Lesion (BrainLes) held jointly at the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI)

作者： Rathore, Saima Bakas, Spyridon Pati, Sarthak Akbari, Hamed Kalarot, Ratheesh Sridharan, Patmaa Rozycki, Martin Bergman, Mark Tunc, Birkan Verma, Ragini Bilello, Michel Davatzikos, Christos Univ Penn Perelman Sch Med CBICA Dept Radiol Philadelphia PA 19104 USA

ISBN: (纸本)9783319752389;9783319752372

Quantitative research, especially in the field of radio(geno) mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.

关键词： Glioblastoma Open-source software Radiomics Radiogenomics computational algorithms Image analysis Fiber tracking

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Development of new computational methods for identifying segments and estimating the risk of water supply interruption for a segment in water pipe networks

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DESALINATION AND WATER TREATMENT 2017年 99卷 211-219页

作者： Park, Suwan Kim, Kimin Pusan Natl Univ Dept Civil & Environm Engn Busan 609735 South Korea

In this paper, new algorithms for identifying segments and unintended isolations for water pipe networks were developed. The developed algorithms are based on the two topological relation data that are the connectivity relationships between the nodes and pipes and the location information of valves along pipes. The algorithms for identifying unintended isolation specifically utilized the connectivity relationships between the segments. The developed algorithms employ more efficient processes of finding pipes and nodes that are isolated by valve closure than the previous matrix-based algorithms. Moreover, the size of the matrices has been greatly reduced compared with the previous studies for more efficient calculations. The developed algorithms were implemented as computer programs using the Matlab software and applied to identify the segments and unintended isolation of a case study water pipe network. A new water supply risk estimation method that uses the deterioration scores of pipes, nodal demands, water rate, and average repair time was also presented. The risk estimation method was used to sort and prioritize the segments of the case study system for a further maintenance investment decision.

关键词： computational algorithms Risk Segment Unintended isolation Water pipe network

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Approximate Analysis of a Queueing-Inventory System with Early and Delayed Server Vacations

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AUTOMATION AND REMOTE CONTROL 2017年第11期78卷 1991-2003页

作者： Melikov, A. Z. Rustamov, A. M. Ponomarenko, L. A. Azerbaijan Natl Acad Sci Inst Control Syst Baku Azerbaijan Natl Acad Sci Ukraine Int Res & Training Ctr Informat Technol & Syst Kiev Ukraine

We propose a model for a servicing system with perishable inventory and a finite queue of impatient claims where the server can be in one of three states: operational, early and delaying vacations. We develop a method... 详细信息

关键词： queueing-inventory systems perishable inventory server vacation computational algorithms optimization

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