Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA234317 | ENA |
REPOSITORIES: ENA
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Project description:Based on fuzzy logic selection and classification algorithms, our selection method measures the contribution of each gene for each of two pre-defined classes in order to find the best discrimination. This algorithm extracts and ranks the most pertinent markers, since it is based on feature weighting according to optimal error rate, sensitivity and specificity. We applied the fuzzy logic selection on four breast cancer microarray databases to obtain new gene signatures based on histological grade. To validate these gene signatures, we designed probes for the selected genes on Nimblegen custom microarrays and tested them on a series of 151 consecutive invasive breast carcinomas displaying clinicopathological features similar to those observed in routine practice.
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Project description:Based on fuzzy logic selection and classification algorithms, our selection method measures the contribution of each gene for each of two pre-defined classes in order to find the best discrimination. This algorithm extracts and ranks the most pertinent markers, since it is based on feature weighting according to optimal error rate, sensitivity and specificity. We applied the fuzzy logic selection on four breast cancer microarray databases to obtain new gene signatures based on histological grade. To validate these gene signatures, we designed probes for the selected genes on Nimblegen custom microarrays and tested them on a series of 151 consecutive invasive breast carcinomas displaying clinicopathological features similar to those observed in routine practice. 151 frozen breast cancer tumors from the tumor bank of the Claudius Regaud Institute (ICR Toulouse, France) were selected. This cohort consisted of consecutive invasive breast carcinoma patients treated at Claudius Regaud Institute between 2009 and 2011. All patients included in this cohort signed an informed consent. Clinico-pathological characteristics of the series were similar to those observed in routine clinical practice (i.e. majority of pre-menopausal patients presenting with T1c, node negative, ER+ invasive ductal carcinoma of intermediate grade).
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Project description:MacNamara2012 - Signal transduction
A toy model of signal tranduction to illustrate how different logic formalizms (Boolean, fuzzy logic and differential equations) treat state and time, is described here.
This model was generated from the PKN-ToyPB.sif file available in cellnopt.data 0.7.8
(also on http://www.cellnopt.org (data section) using libSBML
.
This model is described in the article:
State-time spectrum of signal transduction logic models.
MacNamara A, Terfve C, Henriques D, Bernabé BP, Saez-Rodriguez J
Physical Biology [2012, 9(4):045003]
Abstract:
Despite the current wealth of high-throughput data, our understanding of signal transduction is still incomplete. Mathematical modeling can be a tool to gain an insight into such processes. Detailed biochemical modeling provides deep understanding, but does not scale well above relatively a few proteins. In contrast, logic modeling can be used where the biochemical knowledge of the system is sparse and, because it is parameter free (or, at most, uses relatively a few parameters), it scales well to large networks that can be derived by manual curation or retrieved from public databases. Here, we present an overview of logic modeling formalisms in the context of training logic models to data, and specifically the different approaches to modeling qualitative to quantitative data (state) and dynamics (time) of signal transduction. We use a toy model of signal transduction to illustrate how different logic formalisms (Boolean, fuzzy logic and differential equations) treat state and time. Different formalisms allow for different features of the data to be captured, at the cost of extra requirements in terms of computational power and data quality and quantity. Through this demonstration, the assumptions behind each formalism are discussed, as well as their advantages and disadvantages and possible future developments.
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by: MODEL1305240000
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To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
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domain worldwide. Please refer to CC0 Public Domain
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