Application of self-organizing maps for the detection and classification of human blood plasma lipoprotein lipid profiles on the basis of1H NMR spectroscopy data

Kaartinen, Jouni; Hiltunen, Yrjö; Kovanen, Petri T.; Ala‐Korpela, Mika

doi:10.1002/(sici)1099-1492(199806/08)11:4/5<168::aid-nbm527>3.0.co;2-k

Cited by 35 publications

(12 citation statements)

References 29 publications

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“…When the trained map is applied, the best matching neurons are calculated by using these refer- ence vectors. In this unsupervised methodology, the SOM can be constructed without previous a priori knowledge (5)(6)(7).…”

Section: General Descriptionmentioning

confidence: 99%

Self-Organizing Maps Can Determine Outcome and Match Recipients and Donors at Orthotopic Liver Transplantation

Haydon¹,

Hiltunen²,

Lucey³

et al. 2005

Transplantation

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Background. There is a relative lack of donor organs for liver transplantation. Ideally, to maximize the utility of those livers that are offered, donor and recipient characteristics should be matched to ensure the best possible posttransplant survival of the recipient. Methods. With prospectively collected data on 827 patients receiving a primary liver graft for chronic liver disease, we used a self-organizing map (SOM) (one form of a neural network) to predict outcome after transplantation using both donor and recipient factors. The SOM was then validated using a data set of 2622 patients undergoing transplantation in the United Kingdom at other centers. Results. SOM analysis using 72 inputs and two survival intervals (3 and 12 months) yielded three neurons with either higher or lower probabilities of survival. The model was validated using the independent data set. With 20 patients on the waiting list and 10 sequential donor livers, it was possible to demonstrate that the model could be used to identify which potential recipients were likely to benefit most from each liver offered. Conclusions.With this approach to matching donor livers and recipients, it is possible to inform transplant clinicians about the optimum use of donor livers and thereby effectively make the best use of a scarce resource.

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Section: General Descriptionmentioning

confidence: 99%

Self-Organizing Maps Can Determine Outcome and Match Recipients and Donors at Orthotopic Liver Transplantation

Haydon¹,

Hiltunen²,

Lucey³

et al. 2005

Transplantation

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“…separation techniques or computer analyses such as selforganizing map analysis 10 , multivariate and neural network analysis 11 . Because of no reference materials, the definition of lipoprotein subclasses used in various methods are different, and standardization is required to evaluate the risk of cardiovascular diseases in epidemiological studies based on lipoprotein subclass profiles.…”

mentioning

confidence: 99%

Recent Advances in Analytical Methods on Lipoprotein Subclasses: Calculation of Particle Numbers from Lipid Levels by Gel Permeation HPLC Using “Spherical Particle Model”

Okazaki

Yamashita

2016

J. Oleo Sci.

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“…SOM pattern recognition systems have been used to construct technologies such as car navigation (Pomerleau, 1991), language processing with modular neural networks, and distributed lexicon language processing (Miikkulainen & Dyer, 1991), and mapping neural activity within the visual cortex (e.g., Miikkulainen, Bednar, Choe, & Sirosh, 2005). The SOM also has played an important role in the recognition and grouping of human blood plasma profiles (Kaartinen, Hiltunen, Kovanen, & Ala-Korpela, 1998), the investigation of irregular and potentially dangerous climate changes (Cavazos, 2000), the identification of insulin resistance disorder and related conditions (Valkonen, Kolehmainen, Lakka, & Salonen, 2002), as well as the recognition and classification of gene expression configurations (Tamayo, Slonim, Mesirov, Zhu, Kitareewan, Dmitrovsky, Lander, & Golub, 1999).…”

Section: Articulationmentioning

confidence: 99%

Neural Network and Multivariate Analyses: Pattern Recognition in Academic and Social Research

et al. 2013

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Neural networks are the modern tools that focus most heavily on the logical structure of measurement/assessment, as well as the actual results we attempt to identify by way of scientific inquiry. Employing the Self-Organizing Map (SOM) neural network, we reexamined a well-recognized and commonly employed dataset from a popular applied multivariate statistics text by Stevens (2009). Using this textbook dataset as an exemplar, we provide a preliminary guide to neural networking approaches to the analysis of behavioral outcomes. When employing conventional multivariate procedures only, the sample dataset demonstrated significant familywise error rates; however, these outcomes did not provide sufficient information for identifying the curvilinear patterns that existed within these records. When converted to natural logs and reanalyzed by the SOM, the exemplar dataset showed the actual best fit performance patterns exhibited by all members of the experimental and control groups. The SOM and related neural network algorithms appear to have unique potential in the recognition of nonlinear but unified data patterns frequently exhibited within academic and social outcomes. In particular, the SOM allows the researcher to conduct a "finer grain" analysis identifying critically important similarities and differences that can inform treatment well beyond the probability values derived from conventional statistical techniques.

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Application of self-organizing maps for the detection and classification of human blood plasma lipoprotein lipid profiles on the basis of1H NMR spectroscopy data

Cited by 35 publications

References 29 publications

Self-Organizing Maps Can Determine Outcome and Match Recipients and Donors at Orthotopic Liver Transplantation

Self-Organizing Maps Can Determine Outcome and Match Recipients and Donors at Orthotopic Liver Transplantation

Recent Advances in Analytical Methods on Lipoprotein Subclasses: Calculation of Particle Numbers from Lipid Levels by Gel Permeation HPLC Using “Spherical Particle Model”

Neural Network and Multivariate Analyses: Pattern Recognition in Academic and Social Research

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