Fuzzy Logic Selection as a New Reliable Tool to Identify Gene Signatures in Breast Cancer - the INNODIAG Study
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE53958 | GEO | 2014/01/12
SECONDARY ACCESSION(S): PRJNA234317
REPOSITORIES: GEO
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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).
2014-01-12 | E-GEOD-53958 | biostudies-arrayexpress
Project description:Fuzzy Logic Selection as a New Reliable Tool to Identify Gene Signatures in Breast Cancer - the INNODIAG Study
| PRJNA234317 | ENA
Project description:Comparative analysis of transcriptome profiles of G. arboreum L. cv. and its fuzzy-lintless mutant (ANOI 1960) at 0 and 10 dpa. Cotton is one of the most commercially important fibre crops in the world and used as a source for natural textile fibre and cottonseed oil. The fuzzy-lintless ovules of cotton mutants are ideal source for identifying genes involved in fibre development by comparing with fibre bearing ovules of wild-type. To decipher molecular mechanisms involved in fibre cell development, transcriptome analysis has been carried out by comparing G. arboreum cv. (wild-type) with its fuzzy-lintless mutant (ANOI 1960). Fuzzed-lintless mutant line was generated by back cross breeding between FL and Fl (recurrent parent) lines (personal communication by Dr. I. S. Katageri). Basically Fibre less type was a RIL, first recovered from cross between G.arboreum (linted) and G. anomalum (lint less). This RIL was used as donor parent and crossed with normal arboreum (as recurrent parent) to develop G. arboreum FL and G. arboreum Fl isogenic lines. This G. arboreum Fl line is named as ANOI 1960. Cotton bolls were collected at fibre initiation (0 dpa/days post anthesis) and elongation (10 dpa) and gene expression profiles were analyzed in wild-type and ANOI 1960 mutant using Affymetrix cotton GeneChip Genome array.
2014-11-01 | GSE52432 | GEO
Project description:Comparative analysis of transcriptome profiles of G. arboreum L. cv. and its fuzzy-lintless mutant (ANOI 1960) at 0 and 10 dpa. Cotton is one of the most commercially important fibre crops in the world and used as a source for natural textile fibre and cottonseed oil. The fuzzy-lintless ovules of cotton mutants are ideal source for identifying genes involved in fibre development by comparing with fibre bearing ovules of wild-type. To decipher molecular mechanisms involved in fibre cell development, transcriptome analysis has been carried out by comparing G. arboreum cv. (wild-type) with its fuzzy-lintless mutant (ANOI 1960). Fuzzed-lintless mutant line was generated by back cross breeding between FL and Fl (recurrent parent) lines (personal communication by Dr. I. S. Katageri). Basically Fibre less type was a RIL, first recovered from cross between G.arboreum (linted) and G. anomalum (lint less). This RIL was used as donor parent and crossed with normal arboreum (as recurrent parent) to develop G. arboreum FL and G. arboreum Fl isogenic lines. This G. arboreum Fl line is named as ANOI 1960. Cotton bolls were collected at fibre initiation (0 dpa/days post anthesis) and elongation (10 dpa) and gene expression profiles were analyzed in wild-type and ANOI 1960 mutant using Affymetrix cotton GeneChip Genome array. Cotton plants were grown under field condition. Flowers were tagged and cotton bolls were collected during fibre development stages. Total RNA was isolated from fibre bearing ovules of wild-type (WT) and fuzzy-lintless ovules of mutant (ANOI 1960) collected at various (0 and 10 dpa) fibre development stages using SpectrumTM Plant Total RNA kit (Sigma, USA) according to the manufacturerM-bM-^@M-^Ys protocol. Affymetrix cotton GeneChip Genome array (Affymetrix, USA) having 23,977 probe sets representing 21,854 cotton transcripts was used for transcriptome analysis. Three biological replicates were maintained to test the reproducibility and quality of the chip hybridization. cDNA labeling, array hybridization, staining and washing procedures were carried out as described in the Affymetrix protocols. CEL files having estimated probe intensity values were analyzed with GeneSpring GX-11.5 software (Agilent Technologies, USA) to get differentially expressed transcripts. The Robust Multiarray Average (RMA) algorithm was used for the back ground correction, quantile normalization and median polished probe set summarization to generate single expression value for each probe set. Normalized expression values were log2-transformed and differential expression analysis was performed using unpaired t-test. The p-values were corrected by applying the false discovery rate (FDR) correction (Benjamini and Hochberg, 2000).
2014-11-01 | E-GEOD-52432 | biostudies-arrayexpress
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.
This model is hosted on BioModels Database
and identified
by: MODEL1305240000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to the public
domain worldwide. Please refer to CC0 Public Domain
Dedication
for more information.
2005-01-01 | MODEL1305240000 | BioModels
Project description:We have characterized a maize mutant, fuzzy tassel (fzt) with striking inflorescence and pleiotropic defects. fzt contains a mutation in the maize dicer-like1 homolog, a key enzyme required for microRNA (miRNA) biogenesis. By profiling the small RNA population in fzt mutants, we investigated the impact of fzt mutation on miRNA abundance in seedling and tassel primordia. We also investigated the transcript abundance of potential target genes of miRNA that were impacted in fzt mutant by transcriptome profiling. The result provides an avenue for future research to link specific miRNAs and their targets to discrete phenotypes and developmental roles.
2015-02-20 | GSE52879 | GEO
Project description:We have characterized a maize mutant, fuzzy tassel (fzt) with striking inflorescence and pleiotropic defects. fzt contains a mutation in the maize dicer-like1 homolog, a key enzyme required for microRNA (miRNA) biogenesis. By profiling the small RNA population in fzt mutants, we investigated the impact of fzt mutation on miRNA abundance in seedling and tassel primordia. We also investigated the transcript abundance of potential target genes of miRNA that were impacted in fzt mutant by transcriptome profiling. The result provides an avenue for future research to link specific miRNAs and their targets to discrete phenotypes and developmental roles. Genome-wide small RNA profiling was done by Illumina TruSeq sample preparation followed by high-throughput sequencing with Illumina HiSeq 2000 platform.
2015-02-20 | E-GEOD-52879 | biostudies-arrayexpress
Project description:Establishment and subsequent validation of a aCGH protocol for WGA (whole genome amplification) products originating from single cell or low amount of starting material (i.e. microdissected FFPE tissue samples). The establishment of the protocol involved testing of three DNA labeling protocols. Two labeling protocols were designed specifically for Ampli1(TM) WGA products. Additionally random primed isothermal (Klenow-based) labeling approach was tested (MM-CM-6hlendick et al., PLoS One. 2013 Jun 25;8(6):e67031.). In addition two different types of reference samples were tested and reference based on single-cell WGA products was chosen as most suitable in the end. The validation of the protocol assessed the following aspects: (1) performance of the protocol on primary and reamplified WGA products, (2) accuracy of the protocol in term of sensitivity of the CNA detection, (3) accuracy in terms of recapitulation of complex patterns of CNAs, (4) accuracy in terms of quantitative assessment of the CNAs, (5) ability to detect genomic heterogeneity of single cells (obtained either from in vitro cultures or from clinical patient material), (6) ability to detect minimal regions of aberration within a panel of disseminated cancer cells and corresponding tumor tissues. Establishment and validation of the single-cell aCGH protocol: two condition experiment (i.e. PCR-based labeling technique 1 vs. PCR-based labeling technique 2; PCR-based labeling technique 2 vs. random-primed isothermal (Klenow) labeling approach; reference DNA from cell pool WGA product vs. reference DNA from single-cell WGA products). Validation of the protocol: comparison of the CNA profiles between single-cell WGA products and corresponding bulk DNA. Analysis of the DCC and corresponding FFPE tumor tissue samples: single-condition experiment performed on samples collected at different stages of the disease (DCCs from bone marrow) and/or from different sites (primary tumor-breast; metastasis-lymph nodes; DCCs-bone marrow). Microarray data is corresponding data depicted in the paper manuscript titled: Reliable single cell array CGH for clinical samples
2014-01-21 | E-GEOD-52366 | biostudies-arrayexpress
Project description:Thousands of protein domains encoded in the human genome function by binding up to a million short linear motifs embedded in intrinsically disordered regions of other proteins. How affinity and specificity are encoded in these binding domains and the motifs themselves is not well understood. The evolvability of binding specificity - how rapidly and extensively it can change upon mutation - is also largely unexplored, as is the contribution of ‘fuzzy’ dynamic residues to affinity and specificity in protein-protein interactions. Here we produce the first global map of affinity and specificity encoding in a globular protein domain. Quantifying >200,000 energetic interactions between the domain and ligand allows us to identify 20 major energetically coupled pairs of sites. These are organised into six modules, with the vast majority of mutations in each module only reprogramming specificity for a single position in the ligand. Nine of the major energetic couplings encoding specificity are direct structural contacts and 11 have an allosteric mechanism of action. The dynamic tail of the ligand is more robust to mutation than the structured portion but contributes additively to binding affinity and communicates with structured residues to enable changes in specificity. Our results present how affinity and specificity are encoded in a globular protein domain interacting with a disordered peptide and a direct comparison of the encoding of affinity and specificity in structured and dynamic molecular recognition.
2024-04-25 | GSE265816 | GEO
Project description:Selection among lower taxonomic ranks of bacteria in tropical forest decomposer communities
| PRJEB2570 | ENA