Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer – the INNODIAG study

Kempowsky-Hamon, Tatiana; Valle, Carine; Lacroix-Triki, Magali; Hedjazi, Lyamine; Trouilh, Lidwine; Lamarre, Sophie; Labourdette, Delphine; Roger, Laurence; Mhamdi, Loubna; Dalenc, Florence; Filleron, Thomas; Favre, Gilles; François, Jean; Lann, Marie‐Véronique Le; Leberre, Véronique Anton

doi:10.1186/s12920-015-0077-1

Cited by 18 publications

(10 citation statements)

References 65 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The perspective of cloud computing is postulated as a solution to this challenge, as summarized in recent and extensive reviews. 5 , 15 , 16 Nevertheless, due to ethical and legal issues arising in cloud-based scenarios, 17 incorporation of clinical data and processing of sensitive omics are still considered an open question.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Satagopam

Eifes

et al. 2016

Big Data

View full text Add to dashboard Cite

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services—tranSMART, a Galaxy Server, and a MINERVA platform—are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.

show abstract

Section: Related Workmentioning

confidence: 99%

“…However, the speed of data transfer to the cloud, flexibility of the configuration of analytical pipelines, and the issues of privacy and security in data analytics remain challenges to address. 15 , 36 …”

Section: Related Workmentioning

confidence: 99%

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Satagopam

Eifes

et al. 2016

Big Data

View full text Add to dashboard Cite

show abstract

“…Over the last decade, genome-wide array based expression profiling studies have been used as a forceful tool to improve biological understanding of breast cancer. With these techniques, many prognostic signatures of gene expression have been identified as predictive of breast cancer recurrence risk [ 22 ]. Dr. Golub [ 23 ] and his research team successfully selected 50 gene subsets from 7129 genes of leukemia gene expression data by means of signal-noise ratio, which enabled the accurate classification of experimental samples.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

Zhang

Deng

Liang

et al. 2018

BioMed Research International

View full text Add to dashboard Cite

The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature subsets are used to train the SVM classifier for cancer classification. We also use random forest feature selection (RFFS), random forest feature selection and grid search (RFFS-GS), and minimal redundancy maximal relevance (mRMR) algorithm as feature selection methods to compare the effects of the SVM-RFE-PO algorithm. The results showed that the feature subset obtained by feature selection using SVM-RFE-PSO algorithm results has a better prediction performance of Area Under Curve (AUC) in the testing data set. This algorithm not only is time-saving, but also is capable of extracting more representative and useful genes.

show abstract

“…Several attempts have been made to identify the molecular mechanisms underlying the morphological characteristics of HG to improve its objectivity [ 1 , 5 , 6 , 8 , 17 – 19 ]. The continuous progressive model of tumor aggressiveness from low-grade to high-grade tumors has been accepted for the last few decades [ 20 – 22 ].…”

Section: Introductionmentioning

confidence: 99%

Genome and transcriptome delineation of two major oncogenic pathways governing invasive ductal breast cancer development

Aswad

Yenamandra

et al. 2015

Oncotarget

View full text Add to dashboard Cite

Invasive ductal carcinoma (IDC) is a major histo-morphologic type of breast cancer. Histological grading (HG) of IDC is widely adopted by oncologists as a prognostic factor. However, HG evaluation is highly subjective with only 50%–85% inter-observer agreements. Specifically, the subjectivity in the assignment of the intermediate grade (histologic grade 2, HG2) breast cancers (comprising ~50% of IDC cases) results in uncertain disease outcome prediction and sub-optimal systemic therapy. Despite several attempts to identify the mechanisms underlying the HG classification, their molecular bases are poorly understood.We performed integrative bioinformatics analysis of TCGA and several other cohorts (total 1246 patients). We identified a 22-gene tumor aggressiveness grading classifier (22g-TAG) that reflects global bifurcation in the IDC transcriptomes and reclassified patients with HG2 tumors into two genetically and clinically distinct subclasses: histological grade 1-like (HG1-like) and histological grade 3-like (HG3-like). The expression profiles and clinical outcomes of these subclasses were similar to the HG1 and HG3 tumors, respectively. We further reclassified IDC into low genetic grade (LGG = HG1+HG1-like) and high genetic grade (HGG = HG3-like+HG3) subclasses. For the HG1-like and HG3-like IDCs we found subclass-specific DNA alterations, somatic mutations, oncogenic pathways, cell cycle/mitosis and stem cell-like expression signatures that discriminate between these tumors. We found similar molecular patterns in the LGG and HGG tumor classes respectively.Our results suggest the existence of two genetically-predefined IDC classes, LGG and HGG, driven by distinct oncogenic pathways. They provide novel prognostic and therapeutic biomarkers and could open unique opportunities for personalized systemic therapies of IDC patients.

show abstract

Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer – the INNODIAG study

Cited by 18 publications

References 65 publications

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

Genome and transcriptome delineation of two major oncogenic pathways governing invasive ductal breast cancer development

Contact Info

Product

Resources

About