About: Method of regulatory network that can explore protein regulations for disease classification

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Method of regulatory network that can explore protein regulations for disease classification Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Method of regulatory network that can explore protein regulations for disease classification
Creator	Wang, Hong Cho, William Law, Stephen Ngan, Roger Yip, Timothy Zhu, Hai
Source	Elsevier; Medline; PMC
abstract	Abstract Objective To develop regulatory network to explore and model the regulatory relationships of protein biomarkers and classify different disease groups. Methods Regulatory network is constructed to be a hopfield-like network with nodes representing biomarkers and directional connections to be regulations in between. The input to the network is the measured expression levels of biomarkers, and the output is the summation of regulatory strengths from other biomarkers. The network is optimized towards minimizing the energy function that is defined as the measure of the disagreement between the input and output of the network. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network. Results Two datasets have been used as test beds, one dataset includes patients of nasopharyngeal carcinoma with different responses to chemotherapy drug, and the other consists of patients of severe acute respiratory syndrome, influenza, and control normals. The regulatory networks among protein biomarkers were reconstructed for different disease conditions in each dataset. We demonstrated our methods have better classification capability when comparing with conventional methods including Fisher linear discriminant (FLD), K-nearest neighborhood (KNN), linear support vector machines (linSVM) and radial basis function based support vector machines (rbfSVM). Conclusion The derived networks can effectively capture the unique regulatory patterns of protein markers associated with different patient groups and hence can be used for disease classification. The discovered regulation relationships can potentially provide insights to revealing the molecular signaling pathways. In this paper, a novel technique of regulatory network is proposed on purpose of modeling biomarker regulations and classifying different disease groups. The network is composed of a certain number of nodes that are directionally connected in between in which nodes denote predictors and connections to be the regulation relationship. The network is optimized towards minimizing its energy function with biomarker expression data acquired from a specific patient group, thus the optimized network can model the regulatory relationship of biomarkers under the same circumstance. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network. The regulatory network can extract unique features of each disease condition, thus one immediate application of regulatory network is to classifying different diseases. We demonstrated that regulatory network is capable of performing disease classification through comparing with conventional methods including FLD, KNN, linSVM and rbfSVM on two protein datasets. We believe our method is promising in mining knowledge of protein regulations and be powerful for disease classification.
has issue date	2010-03-31(xsd:dateTime)
bibo:doi	10.1016/j.artmed.2009.07.011
bibo:pmid	19962281
has license	els-covid
sha1sum (hex)	29d374c381ed152a0a2550370fa8ff2234d8533b
schema:url	https://doi.org/10.1016/j.artmed.2009.07.011
resource representing a document's title	Method of regulatory network that can explore protein regulations for disease classification
has PubMed Central identifier	PMC7126395
has PubMed identifier	19962281
schema:publication	Artificial Intelligence in Medicine
resource representing a document's body	covid:29d374c381ed152a0a2550370fa8ff2234d8533b#body_text
is schema:about of	named entity 'REGULATORY' named entity 'PROTEIN ' named entity 'NETWORK' named entity 'DISEASE CLASSIFICATION' named entity 'REGULATIONS' named entity 'METHOD' named entity 'energy function' named entity 'feature space' named entity '1, 2' named entity 'energy function' named entity 'training data' named entity 'biomarker' named entity 'linear programming' named entity 'permutation test' named entity 'SARS' named entity 'non-linear' named entity 'transcriptional' named entity 'standard deviation' named entity 'algorithm' named entity 'surface-enhanced laser desorption/ionization' named entity 'protein' named entity 'regularization' named entity 'energy function' named entity 'protein' named entity 'biomarker' named entity 'biomarkers' named entity 'NRN' named entity 'genome' named entity 'radial basis function kernel' named entity 'expression levels' named entity 'chemotherapy drug' named entity 'objective function' named entity 'regulatory networks' named entity 'probabilistic' named entity 'NRN' named entity 'leave-one-out cross-validation' named entity 'protein' named entity 'KNN' named entity 'biomarkers' named entity 'NRN' named entity 'microarray' named entity 'NRN' named entity 'biomarkers' named entity 'biomarkers' named entity 'chemotherapy drug' named entity 'expression levels' named entity 'K-nearest' named entity 'NRN' named entity 'biomarkers' named entity 'biomarkers' named entity 'regulatory network' named entity 'probability' named entity 'protein' named entity 'classification models' named entity 'protein' named entity 'expression levels' named entity 'biomarkers' named entity 'sensitivity and specificity' named entity 'biomarkers' named entity 'objective function' named entity 'biomarkers' named entity 'peptides' named entity 'support vector machines' named entity 'regulatory network' named entity 'Hopfield network' named entity 'regulatory networks' named entity 'support vector machines' named entity 'signaling pathways' named entity 'biomarkers' named entity '1, 2' named entity 'regulatory network' named entity 'biomarker' named entity 'NRN' named entity 'Elsevier' named entity 'oncology' named entity 'LOOCV'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software