Studying combined breast cancer biomarkers using machine learning techniques

Saleh, Dina T.; Attia, Amir N.; Shaker, Olfat G.

doi:10.1109/sami.2016.7423015

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…The increased incidence of breast cancer and higher mortality rate has attracted significant research efforts to unravel its causes, and development of better treatment options [15], [16]. Breast cancer is a heterogeneous disease with varied features, such as morphological appearances, profile, response to therapy, TNM staging, histological grade, etc.…”

Section: Introductionmentioning

confidence: 99%

“…High throughput techniques such as Next Generation Sequencing (NGS) that capture expression of thousands of genes in a single assay can act as powerful analytical tools for capturing breast cancer prognostic signature [17]. We can obtain information about a large number of genes, but their intertwining relationship cannot be captured by traditional techniques like statistical and correlational analyses, hence advanced methods such as machine-learning are important to capture cryptic signatures inherent in these data [15], [16]. Molecular profiling helps in finding predictive information and identifying prognostic biomarkers that can serve as therapeutic targets [17].…”

Section: Introductionmentioning

confidence: 99%

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Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2019

Preprint

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Phone: +91 26743007Fax: +91 26742316 Email -dinesh@icgeb.res.in (DG)Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients.However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of invasive ductal carcinoma. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods.We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying breast ductal carcinoma progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP. Key PointsDifferent supervised machine-learning algorithms such as Random Forest, SVM and Naive Bayes were trained with enriched features of the TCGA RNA-seq datasets selected for the study.We have developed two-class classification models, trained with relevant gene expression profiles to efficiently discriminate between the early and late IDC stages.Finally, we also developed a web server using python scikit-learn to provide freely available GUI based access to the machine learning models developed by us. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…al. proposed a method of machine learning along with ultrasound model which include Doppler and gray-scale effects for identification of breast cancer. In paper [27], the author used different types of classifiers along with different types of biomarkers. Paper [21], the author has done a comparative study, which includes the different types of classifiers and predicted that the SVM without the fast co-relation based filter is providing highest accuracy which is97.9 percent.…”

Section: Rfsvm: a Novel Classification Technique For Breast Cancer DImentioning

confidence: 99%

RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis

Soni*¹,

Bora²,

Ghosh³

et al. 2019

IJITEE

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Cancer is a disease, which develops, in human body due to gene mutation. Due to various factor cells turn into cancerous cell and grow rapidly while damaging normal cells. Many women get affected by breast cancer, which might even cause death if not treated at early stage. Early detection of breast cancer is highly important to increase the survival rate. Machine learning methods and technologies are making it possible to classify and detect the class in an accurate manner. Among other classifiers, random forest and support vector machine are two classifiers that have a good classification power. In this, research a combination of these two classifier i.e. Random Forest and Support Vector Machine (RFSVM) is proposed for early diagnosis of breast cancer cell using Wisconsin Breast Cancer Dataset (WBCD). Using different train-test data ratio experiments are performed and an average of more than 98percentage accuracy is achieved using this hybrid classifier. This paper overcomes the over-fitting problem of random forest and the need of tuning the parameters of Support Vector Machine. Even with limited data available, the classifier tunes its parameters so well to give a highly accurate result.

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“…The increased incidence of breast cancer and higher mortality rate has attracted significant research efforts to unravel its causes, and development of better treatment options 19 . Breast cancer is a heterogeneous disease with varied features, such as morphological appearances, profile, response to therapy, TNM staging, histological grade, etc.…”

mentioning

confidence: 99%

“…High throughput techniques such as Next Generation Sequencing (NGS) that capture expression of thousands of genes in a single assay can act as powerful analytical tools for capturing breast cancer prognostic signature 20 . We can obtain information about a large number of genes, but their intertwining relationship cannot be captured by traditional techniques like statistical and correlational analyses, hence advanced methods such as machine-learning are important to capture cryptic signatures inherent in these data 19 . Molecular profiling helps in finding predictive information and identifying prognostic biomarkers that can serve as therapeutic targets 20 .…”

mentioning

confidence: 99%

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2020

Sci Rep

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Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of IDC. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machinelearning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying IDC progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

show abstract

Studying combined breast cancer biomarkers using machine learning techniques

Cited by 11 publications

References 22 publications

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

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