A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process.Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme.Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection.Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort.Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.

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SRI: A Scalable Rule Induction Algorithm

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/09544062c18304 ◽

2006 ◽

Vol 220 (4) ◽

pp. 537-552 ◽

Cited By ~ 12

Author(s):

D T Pham ◽

A A Afify

Keyword(s):

Large Data ◽

Rule Induction ◽

Large Data Sets ◽

Small Data ◽

Data Sets ◽

Complex Data ◽

Formidable Challenge ◽

Complex Data Sets ◽

Small Data Sets ◽

Direct Use

Rule induction as a method for constructing classifiers is particularly attractive in data mining applications, where the comprehensibility of the generated models is very important. Most existing techniques were designed for small data sets and thus are not practical for direct use on very large data sets because of their computational inefficiency. Scaling up rule induction methods to handle such data sets is a formidable challenge. This article presents a new algorithm for rule induction that can efficiently extract accurate and comprehensible models from large and noisy data sets. This algorithm has been tested on several complex data sets, and the results prove that it scales up well and is an extremely effective learner.

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Topological data analysis : an overview

10.21079/11681/40943 ◽

2021 ◽

Author(s):

Amy Bednar

Keyword(s):

Data Analysis ◽

Large Data ◽

Large Data Sets ◽

Topological Data Analysis ◽

Background Information ◽

High Dimensional ◽

Data Sets ◽

Complex Data ◽

Topological Network ◽

Topological Data

A growing area of mathematics topological data analysis (TDA) uses fundamental concepts of topology to analyze complex, high-dimensional data. A topological network represents the data, and the TDA uses the network to analyze the shape of the data and identify features in the network that correspond to patterns in the data. These patterns extract knowledge from the data. TDA provides a framework to advance machine learning’s ability to understand and analyze large, complex data. This paper provides background information about TDA, TDA applications for large data sets, and details related to the investigation and implementation of existing tools and environments.

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An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010008794x ◽

1991 ◽

Vol 49 ◽

pp. 726-727

Author(s):

John A. Hunt

Keyword(s):

Quantitative Analysis ◽

Large Data ◽

Difference Spectrum ◽

Large Data Sets ◽

Foil Thickness ◽

Data Sets ◽

Analysis Techniques ◽

Spectrum Imaging ◽

Normal Spectrum ◽

Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100132078 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1488-1489

Author(s):

Thomas W. Shattuck ◽

James R. Anderson ◽

Neil W. Tindale ◽

Peter R. Buseck

Keyword(s):

Cluster Analysis ◽

Chemical Reactivity ◽

Large Data ◽

Large Data Sets ◽

Particle Analysis ◽

Data Sets ◽

Halogen Chemistry ◽

Complete Study ◽

Components Analysis ◽

Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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Faculty Opinions recommendation of Detecting novel associations in large data sets.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13805958.793484294 ◽

2014 ◽

Author(s):

Daniel Lee

Keyword(s):

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Novel Associations

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NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

Visnyk Universytetu “Ukraina” ◽

10.36994/2707-4110-2019-2-23-03 ◽

2019 ◽

Author(s):

Mykhajlo Klymash ◽

Olena Hordiichuk — Bublivska ◽

Ihor Tchaikovskyi ◽

Oksana Urikova

Keyword(s):

Distributed Systems ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Data Decomposition ◽

Distributed Information ◽

Software Model ◽

Computing Performance ◽

Mapreduce Model ◽

Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of computational efficiency on distributed systems were obtained using the MPI messaging protocol and MapReduce node interaction software model. Were analyzed the efficiency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

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Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

Proceedings of the Water Environment Federation ◽

10.2175/193864718824828128 ◽

2018 ◽

Vol 2018 (6) ◽

pp. 38-39

Author(s):

Austa Parker ◽

Yan Qu ◽

David Hokanson ◽

Jeff Soller ◽

Eric Dickenson ◽

...

Keyword(s):

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Statistical Tools

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Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽

10.3390/computers10040047 ◽

2021 ◽

Vol 10 (4) ◽

pp. 47

Author(s):

Fariha Iffath ◽

A. S. M. Kayes ◽

Md. Tahsin Rahman ◽

Jannatul Ferdows ◽

Mohammad Shamsul Arefin ◽

...

Keyword(s):

Source Code ◽

Large Data ◽

Large Data Sets ◽

Detection Technique ◽

Data Sets ◽

Plagiarism Detection ◽

Source Codes ◽

Efficient Detection ◽

Mathematical Problems ◽

Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

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