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High-Throughput 2018, 7(2), 14; https://doi.org/10.3390/ht7020014

Computational Convolution of SELDI Data for the Diagnosis of Alzheimer’s Disease

1
Department of Mathematics and Computer Science, Lawrence Technological University, Southfield, MI 48075, USA
2
Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA
3
William Beaumont Hospital, Royal Oak, MI 48073, USA
*
Author to whom correspondence should be addressed.
Received: 23 February 2018 / Revised: 9 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
(This article belongs to the Special Issue Parallel and Cloud-Based Bioinformatics and Biomedicine)
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Abstract

Alzheimer’s disease is rapidly becoming an endemic for people over the age of 65. A vital path towards reversing this ominous trend is the building of reliable diagnostic devices for definite and early diagnoses in lieu of the longitudinal, usually inconclusive and non-generalize-able methods currently in use. In this article, we present a survey of methods for mining pools of mass spectrometer saliva data in relation to diagnosing Alzheimer’s disease. The computational methods provides new approaches for appropriately gleaning latent information from mass spectra data. They improve traditional machine learning algorithms and are most fit for handling matrix data points including solving problems beyond protein identifications and biomarker discovery. View Full-Text
Keywords: matrix data points; mass spectra SELDI technique; Alzheimer’s disease diagnosis; matrix projection; data transformation matrix data points; mass spectra SELDI technique; Alzheimer’s disease diagnosis; matrix projection; data transformation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Anyaiwe, D.E.O.; Singh, G.B.; Wilson, G.D.; Geddes, T.J. Computational Convolution of SELDI Data for the Diagnosis of Alzheimer’s Disease. High-Throughput 2018, 7, 14.

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