A web server for automatic analysis and extraction of relevant biological knowledge

Juan Cedano; Mario Huerta; Irene Estrada; Frederic Ballllosera; Oscar Conchillo; Pedro Delicado; Enrique Querol

doi:https://doi.org/10.1016/j.compbiomed.2007.03.008

A web server for automatic analysis and extraction of relevant biological knowledge

Juan Cedano, Mario Huerta, Irene Estrada, Frederic Ballllosera, Oscar Conchillo, Pedro Delicado, Enrique Querol

Research output: Contribution to journal › Article › Research › peer-review

2 Citations (Scopus)

Abstract

Motivation: This application aims at assisting researchers with the extraction of significant medical and biological knowledge from data sets with complex relationships among their variables. Results: Non-hypothesis-driven approaches like Principal Curves of Oriented Points (PCOP) are a very suitable method for this objective. PCOP allows for obtaining of a representative pattern from a huge quantity of data of independent variables in a very flexible and direct way. A web server has been designed to automatically realize 'non-linear pattern' analysis, 'hidden-variable-dependent' clustering, and new samples 'local-dispersion-dependent' classification from the data involving new statistical techniques using the PCOP calculus. The tools facilitate the managing, comparison and visualization of results in a user-friendly graphical interface. Availability: http://ibb.uab.es/revresearch. © 2007 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	1672-1675
Journal	Computers in Biology and Medicine
Volume	37
DOIs	https://doi.org/10.1016/j.compbiomed.2007.03.008
Publication status	Published - 1 Nov 2007

Keywords

Classification
Clustering
Multivariate analysis
Principal curves analysis
Web server

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https://doi.org/10.1016/j.compbiomed.2007.03.008

Cite this

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title = "A web server for automatic analysis and extraction of relevant biological knowledge",

abstract = "Motivation: This application aims at assisting researchers with the extraction of significant medical and biological knowledge from data sets with complex relationships among their variables. Results: Non-hypothesis-driven approaches like Principal Curves of Oriented Points (PCOP) are a very suitable method for this objective. PCOP allows for obtaining of a representative pattern from a huge quantity of data of independent variables in a very flexible and direct way. A web server has been designed to automatically realize 'non-linear pattern' analysis, 'hidden-variable-dependent' clustering, and new samples 'local-dispersion-dependent' classification from the data involving new statistical techniques using the PCOP calculus. The tools facilitate the managing, comparison and visualization of results in a user-friendly graphical interface. Availability: http://ibb.uab.es/revresearch. {\textcopyright} 2007 Elsevier Ltd. All rights reserved.",

keywords = "Classification, Clustering, Multivariate analysis, Principal curves analysis, Web server",

author = "Juan Cedano and Mario Huerta and Irene Estrada and Frederic Ballllosera and Oscar Conchillo and Pedro Delicado and Enrique Querol",

year = "2007",

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doi = "https://doi.org/10.1016/j.compbiomed.2007.03.008",

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T1 - A web server for automatic analysis and extraction of relevant biological knowledge

AU - Cedano, Juan

AU - Huerta, Mario

AU - Estrada, Irene

AU - Ballllosera, Frederic

AU - Conchillo, Oscar

AU - Delicado, Pedro

AU - Querol, Enrique

PY - 2007/11/1

Y1 - 2007/11/1

N2 - Motivation: This application aims at assisting researchers with the extraction of significant medical and biological knowledge from data sets with complex relationships among their variables. Results: Non-hypothesis-driven approaches like Principal Curves of Oriented Points (PCOP) are a very suitable method for this objective. PCOP allows for obtaining of a representative pattern from a huge quantity of data of independent variables in a very flexible and direct way. A web server has been designed to automatically realize 'non-linear pattern' analysis, 'hidden-variable-dependent' clustering, and new samples 'local-dispersion-dependent' classification from the data involving new statistical techniques using the PCOP calculus. The tools facilitate the managing, comparison and visualization of results in a user-friendly graphical interface. Availability: http://ibb.uab.es/revresearch. © 2007 Elsevier Ltd. All rights reserved.

AB - Motivation: This application aims at assisting researchers with the extraction of significant medical and biological knowledge from data sets with complex relationships among their variables. Results: Non-hypothesis-driven approaches like Principal Curves of Oriented Points (PCOP) are a very suitable method for this objective. PCOP allows for obtaining of a representative pattern from a huge quantity of data of independent variables in a very flexible and direct way. A web server has been designed to automatically realize 'non-linear pattern' analysis, 'hidden-variable-dependent' clustering, and new samples 'local-dispersion-dependent' classification from the data involving new statistical techniques using the PCOP calculus. The tools facilitate the managing, comparison and visualization of results in a user-friendly graphical interface. Availability: http://ibb.uab.es/revresearch. © 2007 Elsevier Ltd. All rights reserved.

KW - Classification

KW - Clustering

KW - Multivariate analysis

KW - Principal curves analysis

KW - Web server

U2 - https://doi.org/10.1016/j.compbiomed.2007.03.008

DO - https://doi.org/10.1016/j.compbiomed.2007.03.008

M3 - Article

SN - 0010-4825

VL - 37

SP - 1672

EP - 1675

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

ER -

A web server for automatic analysis and extraction of relevant biological knowledge

Abstract

Keywords

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