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International Conference on New Trends in Information and Service Science, NISS

作者： Okkyung Choi Wooyeol Jung Kangseok Kim Hongjin Yeh Department of Knowledge Information Engineering Graduate School of Ajou University Suwon South Korea

ISBN: (纸本)9781467308762

Even though many companies and nations are aware of the need for implementing mobile cloud computing to their business practice, sometimes it is difficult to clarify the objective of cloud computing service. The general trend is to just implement ASP (Active Server Provider) instead. The most noteworthy distributed computing framework in the field of cloud computing is `Hadoop,' an open source tool developed by `Yahoo' for large scale data processing and analysis. This research proposes a mobile cloud computing model, which utilizes open source codes from distributed computing frameworks, like Hadoop, in an attempt to improve the efficiency of business processing. We will also be able to study how to process and analyze unstructured data in parallel to this model, so that in the future, providing customized information for individuals may be possible using unstructured data.

关键词： Cloud Computin data processing and analysis Hadoop

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A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2018年第3期6卷 270-276页

作者： Janowczyk, Andrew Doyle, Scott Gilmore, Hannah Madabhushi, Anant Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA SUNY Buffalo Pathol & Anat Sci Buffalo NY USA Univ Hosp Case Med Ctr Surg Pathol Cleveland OH USA

Deep learning (DL) has recently been successfully applied to a number of image analysis problems. However, DL approaches tend to be inefficient for segmentation on large image data, such as high-resolution digital pathology slide images. For example, typical breast biopsy images scanned at 40x magnification contain billions of pixels, of which usually only a small percentage belong to the class of interest. For a typical naive deep learning scheme, parsing through and interrogating all the image pixels would represent hundreds if not thousands of hours of compute time using high performance computing environments. In this paper, we present a resolution adaptive deep hierarchical (RADHicaL) learning scheme wherein DL networks at lower resolutions are leveraged to determine if higher levels of magnification, and thus computation, are necessary to provide precise results. We evaluate our approach on a nuclear segmentation task with a cohort of 141 ER+ breast cancer images and show we can reduce computation time on average by about 85%. Expert annotations of 12,000 nuclei across these 141 images were employed for quantitative evaluation of RADHicaL. A head-to-head comparison with a naive DL approach, operating solely at the highest magnification, yielded the following performance metrics: .9407 vs .9854 Detection Rate, .8218 vs .8489 F-score, .8061 vs .8364 true positive rate and .8822 vs 0.8932 positive predictive value. Our performance indices compare favourably with state of the art nuclear segmentation approaches for digital pathology images.

关键词： data processing and analysis applications of imaging and visualisation image processing and analysis deep learning digital pathology output generation

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Network Reconstruction and Significant Pathway Extraction Using Phosphoproteomic data from Cancer Cells

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PROTEOMICS 2019年第21-22期19卷 e1800450-e1800450页

作者： Buffard, Marion Naldi, Aurelien Radulescu, Ovidiu Coopman, Peter J. Larive, Romain M. Freiss, Gilles Univ Montpellier INSERM ICM IRCM F-34298 Montpellier France Univ Montpellier CNRS LPHI F-34095 Montpellier France PSL Univ Computat Syst Biol Team Inst Biol Ecole Normale SuperCNRSUMR8197INSERMU1024 F-75230 Montpellier France Univ Montpellier ENSCM CNRS IBMM F-34093 Montpellier France

Protein phosphorylation acts as an efficient switch controlling deregulated key signaling pathway in cancer. Computational biology aims to address the complexity of reconstructed networks but overrepresents well-known proteins and lacks information on less-studied proteins. A bioinformatic tool to reconstruct and select relatively small networks that connect signaling proteins to their targets in specific contexts is developed. It enables to propose and validate new signaling axes of the Syk kinase. To validate the potency of the tool, it is applied to two phosphoproteomic studies on oncogenic mutants of the well-known phosphatidyl-inositol 3-kinase (PIK3CA) and the unfamiliar Src-related tyrosine kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylation sites (SRMS) kinase. By combining network reconstruction and signal propagation, comprehensive signaling networks from large-scale experimental data are built and multiple molecular paths from these kinases to their targets are extracted. Specific paths from two distinct PIK3CA mutants are retrieved, and their differential impact on the HER3 receptor kinase is explained. In addition, to address the missing connectivities of the SRMS kinase to its targets in interaction pathway databases, phospho-tyrosine and phospho-serine/threonine proteomic data are integrated. The resulting SRMS-signaling network comprises casein kinase 2, thereby validating its currently suggested role downstream of SRMS. The computational pipeline is publicly available, and contains a user-friendly graphical interface ().

关键词： data processing and analysis oncogenic signaling phosphoproteomics phosphatidyl-inositol 3-kinase enzyme Src-related tyrosine kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylation sites

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hmSEEKER: Identification of hmSILAC Doublets in MaxQuant Output data

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PROTEOMICS 2019年第5期19卷 1800300-1800300页

作者： Massignani, Enrico Cuomo, Alessandro Musiani, Daniele Jammula, SriGanesh Pavesi, Giulio Bonaldi, Tiziana European Inst Oncol IRCCS IEO Dept Expt Oncol Milan Italy Univ Milan Dept Biosci I-20133 Milan Italy Canc Res UK Cambridge Inst Cambridge CB2 0RE England

Heavy methyl Stable Isotope Labeling with Amino acids in Cell culture (hmSILAC) is a metabolic labeling strategy employed in proteomics to increase the confidence of global identification of methylated peptides by MS. However, to this day, the automatic and robust identification of heavy and light peak doublets from MS-raw data of hmSILAC experiments is a challenging task, for which the choice of computational methods is very limited. Here, hmSEEKER, a software designed to work downstream of a MaxQuant analysis for in-depth search of MS peak pairs that correspond to light and heavy methyl-peptide within MaxQuant-generated tables is described with good sensitivity and specificity. The software is written in Perl, and its code and user manual are freely available at Bitbucket ().

关键词： data processing and analysis heavy methyl SILAC methylation MS-LC-MS/MS posttranslational modifications analysis stable isotope labeling

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Semi-automatic tracking of hyolaryngeal coordinates in videofluoroscopic swallowing studies

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2017年第6期5卷 379-389页

作者： Natarajan, Rajkiran Stavness, Ian Pearson, William, Jr. Univ Saskatchewan Dept Comp Sci 176 Thorvaldson Bldg110 Sci Pl Saskatoon SK S7N 5C9 Canada Georgia Regents Univ Med Coll Georgia Dept Cellular Biol & Anat Augusta GA 30912 USA

The gold standard for assessing dysphagia in the clinical setting is the videofluoroscopic swallowing study. Detailed, quantitative analysis of swallowing videos has significant potential to improve our understanding of healthy and pathological swallows. A key limitation of such analyses is that manual annotation of anatomical landmarks on video frames is tedious, time-consuming and potentially unreliable. In this study, we demonstrate a computer-assisted workflow that uses the Kanade-Lucas-Tomasi tracker to semi-automate the process of collecting coordinate data throughout a swallow cycle. We tested the workflow against eight videofluorographs of normal swallow subjects obtained from a variety of sources. analysis of results showed that this workflow can facilitate the process of accurately determining a variety of hyolaryngeal kinematic variables and generate coordinates useful for biomechanical simulation and morphometric analysis of swallowing.

关键词： applications of imaging and visualization image processing and analysis imaging and visualization in biomechanics data processing and analysis

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Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2017年第5期5卷 339-349页

作者： Margeta, J. Criminisi, A. Lozoya, R. Cabrera Lee, D. C. Ayache, N. Inria Sophia Antipolis Asclepios 2004 Route Lucioles Sophia Antipolis France Microsoft Res Machine Learning & Percept Cambridge England Northwestern Univ Feinberg Cardiovasc Res Inst Feinberg Sch Med Chicago IL 60611 USA

In this paper, we propose a convolutional neural network-based method to automatically retrieve missing or noisy cardiac acquisition plane information from magnetic resonance imaging and predict the five most common cardiac views. We fine-tune a convolutional neural network (CNN) initially trained on a large natural image recognition data-set (Imagenet ILSVRC2012) and transfer the learnt feature representations to cardiac view recognition. We contrast this approach with a previously introduced method using classification forests and an augmented set of image miniatures, with prediction using off the shelf CNN features, and with CNNs learnt from scratch. We validate this algorithm on two different cardiac studies with 200 patients and 15 healthy volunteers, respectively. We show that there is value in fine-tuning a model trained for natural images to transfer it to medical images. Our approach achieves an average F1 score of 97.66% and significantly improves the state-of-the-art of image-based cardiac view recognition. This is an important building block to organise and filter large collections of cardiac data prior to further analysis. It allows us to merge studies from multiple centres, to perform smarter image filtering, to select the most appropriate image processing algorithm, and to enhance visualisation of cardiac data-sets in content-based image retrieval.

关键词： medical imaging and visualization applications of imaging and visualization data processing and analysis computer aided diagnosis image processing and analysis machine learning cardiac magnetic resonance convolutional neural networks

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Bioinformatic analysis of Nucleosome and Histone Variant Positioning

Bioinformatic Analysis of Nucleosome and Histone Variant Pos...

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作者： Jakub Mieczkowski Michael Y. Tolstorukov

Assays profiling nucleosome positioning and occupancy are often coupled with high-throughput sequencing, which results in generation of large data sets. These data sets require processing in specialized computational pipelines to yield useful information. Here, we describe main steps of such a pipeline, and discuss bioinformatic and statistical aspects of assessing data quality, as well as data visualization and further analysis. less

关键词： Next Generation Sequencing High-Throughput Sequencing Bowtie Genomics PCR Genome Visualization ChIP Assay Heat Map Generation MNase-seq Programming Language bedtools data processing and analysis Histone variants MNase Nucleosome positioning

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Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics data Sets with Percolator 3.0

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JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2016年第11期27卷 1719-1727页

作者： The, Matthew MacCoss, Michael J. Noble, William S. Kall, Lukas KTH Royal Inst Technol Sci Life Lab Sch Biotechnol Box 1031 S-17121 Solna Sweden Univ Washington Sch Med Dept Genome Sci Seattle WA 98195 USA Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

Percolator is a widely used software tool that increases yield in shotgun proteomics experiments and assigns reliable statistical confidence measures, such as q values and posterior error probabilities, to peptides and peptide-spectrum matches ( PSMs) from such experiments. Percolator's processing speed has been sufficient for typical data sets consisting of hundreds of thousands of PSMs. With our new scalable approach, we can now also analyze millions of PSMs in a matter of minutes on a commodity computer. Furthermore, with the increasing awareness for the need for reliable statistics on the protein level, we compared several easy-to-understand protein inference methods and implemented the best-performing method-grouping proteins by their corresponding sets of theoretical peptides and then considering only the best-scoring peptide for each protein-in the Percolator package. We used Percolator 3.0 to analyze the data from a recent study of the draft human proteome containing 25 million spectra (PM:24870542). The source code and Ubuntu, Windows, MacOS, and Fedora binary packages are available from http://***/under an Apache 2.0 license.

关键词： Mass spectrometry - LC-MS/MS Statistical analysis data processing and analysis Protein inference Large scale studies

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The Northwestern University Neuroimaging data Archive (NUNDA)

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NEUROIMAGE 2016年第PartB期124卷 1131-1136页

作者： Alpert, Kathryn Kogan, Alexandr Parrish, Todd Marcus, Daniel Wang, Lei Northwestern Univ Feinberg Sch Med Dept Psychiat & Behav Sci Chicago IL 60611 USA Northwestern Univ Feinberg Sch Med Dept Radiol Chicago IL 60611 USA Washington Univ Sch Med NRG St Louis MO USA

The Northwestern University Neuroimaging data Archive (NUNDA), an XNAT-powered data archiving system, aims to facilitate secure data storage;centralized data management;automated, standardized data processing;and simple, intuitive data sharing. NUNDA is a federated data archive, wherein individual project owners regulate access to their data. NUNDA supports multiple methods of data import, enabling data collection in a central repository. data in NUNDA are available by project to any authorized user, allowing coordinated data management and review across sites. With NUNDA pipelines, users capitalize on existing procedures or standardize custom routines for consistent, automated data processing. NUNDA can be integrated with other research databases to simplify data exploration and discovery. And data on NUNDA can be confidently shared for secure collaboration. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Neuroinformatics data storage data processing and analysis data sharing XNAT

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OpenSlice: Quantitative data sharing from HyperPeaks to global ion chromatograms (GICs)

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PROTEOMICS 2016年第18期16卷 2495-2501页

作者： Askenazi, Manor Fenyo, David Biomed Hosting LLC Arlington MA USA NYU Sch Med Inst Syst Genet New York NY USA NYU Sch Med Dept Biochem & Mol Pharmacol New York NY USA

data sharing in the field of MS has advanced greatly thanks to innovations such as the standardized formats, data repositories, and publications guidelines. However, there is currently no data sharing mechanism that enables real-time data browsing and deep linking on a large scale: unrestricted data access (particularly at the quantitative level) ultimately requires the user to download a local copy of the relevant data files (e.g., in order to generate extracted ion chromatograms [XICs]). In this technical resource, we present a set of technologies (collectively termed OpenSlice) that enable the user to quantitatively query hundreds of hours of proteomics discovery data (i.e., nontargeted acquisition) in real time: the user is able to effectively generate XICs for arbitrary masses on the fly and across the entire dataset (so-called global ion chromatograms), interacting with the results through a very intuitive browser-based interface. A key design consideration underlying the OpenSlice approach is the notion that every aspect of the acquired data must be accessible through a RESTful uniform resource locator based application programming interface, up to and including individual chromatographic peaks (hence HyperPeaks). A publicly accessible demonstration of this technology based on the Clinical Proteomics Tumor analysis Consortium CompRef dataset is made available at http://***.

关键词： Bioinformatics databases data mining data processing and analysis

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