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.
2014-01-12 | GSE53958 | GEO
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:Due to their role in tumorigenesis and remarkable stability in body fluids, microRNAs (miRNAs) are emerging as a promising diagnostic tool. The aim of this study was to identify tumor miRNA signatures for the discrimination of breast cancer and the intrinsic molecular subtypes, and the study in plasma of the status of the most significant ones in order to identify potential circulating biomarkers for breast cancer detection.
2015-09-03 | GSE58606 | GEO
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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2005-01-01 | MODEL1305240000 | BioModels
Project description:In this study, we successfully defined a highly efficient standardize method to derive hPSCs into a pure population of functional melanocytes. These cells can be maintain for more than 20 passages without observed any senescence. Transcriptomic anlaysis revealed a differentiation process with a logic gene expression profile following the kinetic of melanocyte embryonic development as previously described in mice. Our findings represent a fruitful ground that can serve as a database tool to decipher molecular signatures in normal and pathological conditions
2023-03-30 | GSE222639 | GEO
Project description:Hormone-receptor negative (HR-) breast cancer accounts for approximately one third of all breast tumors and has a worse prognosis compared with hormone receptor-positive disease. Their unfavorable outcome and the lack of hormonal receptors determine the use of adjuvant chemotherapy as part of the standard treatment for these tumors. However, a significant proportion of patients relapse after receiving adjuvant chemotherapy. Classical prognostic factors such as tumor size, lymph node status and grade of differentiation do not suffice to identify patients with a higher risk of relapse. In the last decade, gene profiling has allowed the description of signatures with prognostic value in hormone receptor-positive tumors, but similar information is scarce in HR- disease. Reliable identification of poor-prognosis patients would be important to offer participation in clinical trials with new drugs and to modify follow-up schedules. In this study, we assessed gene expression with quantitative polymerase-chain reaction (RT-qPCR) in HR- early stage breast cancer. RNA was isolated from formalin-fixed paraffin-embedded (FFPE) tissue. Our purpose was to identify a gene signature related to outcome.
2016-02-01 | GSE33669 | GEO
Project description:Axon pathfinding is a key step during the formation of neural circuits but however the transcriptional mechanisms regulating its progression remain poorly understood. The binary decision of crossing or avoiding the midline that neurons make during developmente midline that mammalian retinal ganglion cell (RGC) axons take at the optic chiasm has classically represented a robust model to search for novel mechanisms controlling the selection of axonal trajectories. Here, in order to identify new axon pathfinding regulators, we compared the transcriptome and chromatin accessibility profiles of mammalian retinal ganglion cells RGCs projecting ipsilateral (iRGCs) or contralaterally (cRGCs). Overall, our analyses retrieved dozens of new molecules potentially involved in axon pathfinding and uncovered the regulatory logic behind axon trajectories selection.
2022-08-22 | GSE184275 | GEO
Project description:Purpose: Multiple studies from last decades have shown that the microenvironment of carcinomas plays an important role in the initiation, progression and metastasis of cancer. Our group has previously identified novel cancer stroma gene expression signatures associated with outcome differences in breast cancer by gene expression profiling of two tumors of fibroblasts as surrogates for physiologic stromal expression patterns. The aim of this study is to find additional new types of tumor stroma gene expression patterns. Results: 53 tumors were sequenced by 3SEQ with an average of 29 million reads per sample. Both the elastofibroma (EF) and fibroma of tendon sheath (FOTS) gene signatures demonstrated robust outcome results for survival in the four breast cancer datasets. The EF signature positive breast cancers (20-33% of the cohort) demonstrated significantly better outcome for survival. In contrast, the FOTS signature positive breast cancers (11-35% of the cohort) had a worse outcome. The combined stromal signatures of EF, FOTS, and our previously identified DTF, and CSF1 signatures characterize, in part, the stromal expression profile for the tumor microenvironment for between 74%-90% of all breast cancers. Conclusions: We defined and validated two new stromal signatures in breast cancer (EF and FOTS), which are significantly associated with prognosis. Gene expression profiling by 3SEQ was performed on 8 additional types of fibrous tumors, to identify different fibrous tumor specific gene expression signatures. We then determined the significance of the fibrous tumor gene signatures in four publically available breast cancer datasets (GSE1456, GSE4922, GSE3494, NKI Dataset).
2013-11-21 | E-GEOD-42948 | biostudies-arrayexpress
Project description:Background. The development of reliable gene expression profiling technology is having an increasing impact on our understanding of breast cancer biology. Methods. In this study, microarray analysis was performed in order to establish gene signatures for different breast cancer phenotypes, determine differentially expressed gene sequences at different stages of the disease, and identify sequences with biological significance for tumor progression. Samples were taken from patients before their treatment. After microarray analysis, the expression level of 153 selected genes was studied by qPCR. Results. A number of gene sequences were differentially expressed in tumor versus control samples and were also associated with different breast cancer phenotypes, ER status, tumor histology, and grade of tumor differentiation. In N0 tumors were found a set of genes related to tumor differentiation grade. Conclusion. A number of differentially expressed gene sequences were found at different stages of the breast cancer disease. Key Words: Breast cancer, gene expression signature, tumor invasiveness, microarrays, qPCR In total, 58 samples were studied, 31 tumors and 27 controls. Some of the samples are paired
2009-06-30 | E-GEOD-10810 | biostudies-arrayexpress