7 results match your criteria Algorithms[Journal]

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Automated Processing of fNIRS Data-A Visual Guide to the Pitfalls and Consequences.

Algorithms 2018 May 8;11(5). Epub 2018 May 8.

Experimental Imaging Lab, Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4Z6, Canada; (I.K.O.); (C.C.D.); (A.V.C.); (J.F.D.).

With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site. Read More

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http://www.mdpi.com/1999-4893/11/5/67
Publisher Site
http://dx.doi.org/10.3390/a11050067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428450PMC
May 2018
2 Reads

Post-Processing Partitions to Identify Domains of Modularity Optimization.

Algorithms 2017 Sep 19;10(3). Epub 2017 Aug 19.

Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA.

We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition-i.e. Read More

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http://dx.doi.org/10.3390/a10030093DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5642987PMC
September 2017
9 Reads

PMS6MC: A Multicore Algorithm for Motif Discovery.

Algorithms 2013 Nov;6(4):805-823

Department of CSE, University of Connecticut, Storrs, CT 06269, USA,

We develop an efficient multicore algorithm, PMS6MC, for the ()-motif discovery problem in which we are to find all strings of length that appear in every string of a given set of strings with at most mismatches. PMS6MC is based on PMS6, which is currently the fastest single-core algorithm for motif discovery in large instances. The speedup, relative to PMS6, attained by our multicore algorithm ranges from a high of 6. Read More

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http://dx.doi.org/10.3390/a6040805DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193679PMC
November 2013
1 Read

Successive Standardization of Rectangular Arrays.

Algorithms 2012 Feb;5(1):98-112

Department of Health Research and Policy-Biostatistics, HRP Redwood Building, Stanford University School of Medicine, Stanford, CA 94305-5405, USA ; Department of Electrical Engineering, Stanford University, Packard Electrical Engineering Building, 350 Serra Mall, Stanford, CA 94305, USA ; Department of Statistics, Stanford University, Sequoia Hall, 390 Serra Mall, Stanford, CA 94305-4065, USA.

In this note we illustrate and develop further with mathematics and examples, the work on successive standardization (or normalization) that is studied earlier by the same authors in [1] and [2]. Thus, we deal with successive iterations applied to rectangular arrays of numbers, where to avoid technical difficulties an array has at least three rows and at least three columns. Without loss, an iteration begins with operations on columns: first subtract the mean of each column; then divide by its standard deviation. Read More

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http://dx.doi.org/10.3390/a5010098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552472PMC
February 2012
1 Read

Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives.

Authors:
Bin Zheng

Algorithms 2009 Jun;2(2):828-849

Imaging Research Center, Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Room 128, Pittsburgh, PA 15213, USA.

As the rapid advance of digital imaging technologies, the content-based image retrieval (CBIR) has became one of the most vivid research areas in computer vision. In the last several years, developing computer-aided detection and/or diagnosis (CAD) schemes that use CBIR to search for the clinically relevant and visually similar medical images (or regions) depicting suspicious lesions has also been attracting research interest. CBIR-based CAD schemes have potential to provide radiologists with "visual aid" and increase their confidence in accepting CAD-cued results in the decision making. Read More

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http://www.mdpi.com/1999-4893/2/2/828
Publisher Site
http://dx.doi.org/10.3390/a2020828DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2841362PMC
June 2009
2 Reads

Exhaustive Enumeration of Kinetic Model Topologies for the Analysis of Time-Resolved RNA Folding.

Algorithms 2009 Mar;2(1):200-214

Computational and Structural Biology Department, Wadsworth Center, Albany, NY 12208, USA.

Unlike protein folding, the process by which a large RNA molecule adopts a functionally active conformation remains poorly understood. Chemical mapping techniques, such as Hydroxyl Radical (·OH) footprinting report on local structural changes in an RNA as it folds with single nucleotide resolution. The analysis and interpretation of this kinetic data requires the identification and subsequent optimization of a kinetic model and its parameters. Read More

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http://dx.doi.org/10.3390/a2010200DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2768297PMC

Machine Learning: A Crucial Tool for Sensor Design.

Algorithms 2008 Dec;1(2):130-152

Department of Mechanical and Aeronautical Engineering, One Shields Avenue, University of California, Davis, CA 95616, USA.

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. Read More

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http://dx.doi.org/10.3390/a1020130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2828765PMC
December 2008
1 Read
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