Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic Using Optimized EKF-RBFN Trained Prototypes

2019 ◽  
pp. 51-65
Author(s):  
Vincent Adegoke ◽  
Daqing Chen ◽  
Ebad Banissi ◽  
Safia Barsikzai
Keyword(s):  
Breast Cancer ◽  
Prediction Accuracy ◽  
Ensemble Prediction

scholarly journals Breast cancer identification via modeling of peripherally circulating miRNAs

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4551 ◽  
Author(s):  
Xiaomeng Cui ◽  
Zhangming Li ◽  
Yilei Zhao ◽  
Anqi Song ◽  
Yunbo Shi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Accuracy ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
Multiple Linear Regression Model ◽  
Circulating Mirnas ◽  
Independent Validation ◽  
Technical Possibility ◽  
Validation Set ◽  
Prolonged Life Expectancy

Prolonged life expectancy in humans has been accompanied by an increase in the prevalence of cancers. Breast cancer (BC) is the leading cause of cancer-related deaths. It accounts for one-fourth of all diagnosed cancers and affects one in eight females worldwide. Given the high BC prevalence, there is a practical need for demographic screening of the disease. In the present study, we re-analyzed a large microRNA (miRNA) expression dataset (GSE73002), with the goal of optimizing miRNA biomarker selection using neural network cascade (NNC) modeling. Our results identified numerous candidate miRNA biomarkers that are technically suitable for BC detection. We combined three miRNAs (miR-1246, miR-6756-5p, and miR-8073) into a single panel to generate an NNC model, which successfully detected BC with 97.1% accuracy in an independent validation cohort comprising 429 BC patients and 895 healthy controls. In contrast, at least seven miRNAs were merged in a multiple linear regression model to obtain equivalent diagnostic performance (96.4% accuracy in the independent validation set). Our findings suggested that suitable modeling can effectively reduce the number of miRNAs required in a biomarker panel without compromising prediction accuracy, thereby increasing the technical possibility of early detection of BC.

scholarly journals Role of Immune Cell-Specific Hypermethylation Signatures in Classification and Risk Stratification of Breast Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Yong Chen ◽  
Fada Xia ◽  
Bo Jiang ◽  
Wenlong Wang ◽  
Xinying Li
Keyword(s):  
Breast Cancer ◽  
Risk Stratification ◽  
Immune Cells ◽  
Prediction Accuracy ◽  
Prognostic Value ◽  
Clinical Characteristics ◽  
Immune Cell ◽  
Cell Types ◽  
Clinical Model

Background: Epigenetic regulation, including DNA methylation, plays a major role in shaping the identity and function of immune cells. Innate and adaptive immune cells recruited into tumor tissues contribute to the formation of the tumor immune microenvironment (TIME), which is closely involved in tumor progression in breast cancer (BC). However, the specific methylation signatures of immune cells have not been thoroughly investigated yet. Additionally, it remains unknown whether immune cells-specific methylation signatures can identify subgroups and stratify the prognosis of BC patients.Methods: DNA methylation profiles of six immune cell types from eight datasets downloaded from the Gene Expression Omnibus were collected to identify immune cell-specific hypermethylation signatures (IC-SHMSs). Univariate and multivariate cox regression analyses were performed using BC data obtained from The Cancer Genome Atlas to identify the prognostic value of these IC-SHMSs. An unsupervised clustering analysis of the IC-SHMSs with prognostic value was performed to categorize BC patients into subgroups. Multiple Cox proportional hazard models were constructed to explore the role of IC-SHMSs and their relationship to clinical characteristics in the risk stratification of BC patients. Integrated discrimination improvement (IDI) was performed to determine whether the improvement of IC-SHMSs on clinical characteristics in risk stratification was statistically significant.Results: A total of 655 IC-SHMSs of six immune cell types were identified. Thirty of them had prognostic value, and 10 showed independent prognostic value. Four subgroups of BC patients, which showed significant heterogeneity in terms of survival prognosis and immune landscape, were identified. The model incorporating nine IC-SHMSs showed similar survival prediction accuracy as the clinical model incorporating age and TNM stage [3-year area under the curve (AUC): 0.793 vs. 0.785; 5-year AUC: 0.735 vs. 0.761]. Adding the IC-SHMSs to the clinical model significantly improved its prediction accuracy in risk stratification (3-year AUC: 0.897; 5-year AUC: 0.856). The results of IDI validated the statistical significance of the improvement (p < 0.05).Conclusions: Our study suggests that IC-SHMSs may serve as signatures of classification and risk stratification in BC. Our findings provide new insights into epigenetic signatures, which may help improve subgroup identification, risk stratification, and treatment management.

Prediction of Breast Cancer Using Machine Learning

2020 ◽  
Vol 13 (5) ◽  
pp. 901-908
Author(s):  
Somil Jain ◽  
Puneet Kumar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Breast Cancer Dataset

Background:: Breast cancer is one of the diseases which cause number of deaths ever year across the globe, early detection and diagnosis of such type of disease is a challenging task in order to reduce the number of deaths. Now a days various techniques of machine learning and data mining are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like cancer which can save the life’s of the patients suffering from such type of disease. The major concern of this study is to find the prediction accuracy of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest and to suggest the best algorithm. Objective:: The objective of this study is to assess the prediction accuracy of the classification algorithms in terms of efficiency and effectiveness. Methods: This paper provides a detailed analysis of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest in terms of their prediction accuracy by applying 10 fold cross validation technique on the Wisconsin Diagnostic Breast Cancer dataset using WEKA open source tool. Results:: The result of this study states that Support Vector Machine has achieved the highest prediction accuracy of 97.89 % with low error rate of 0.14%. Conclusion:: This paper provides a clear view over the performance of the classification algorithms in terms of their predicting ability which provides a helping hand to the medical practitioners to diagnose the chronic disease like breast cancer effectively.

Development and clinical validation of Lantern Pharma’s AI engine: Response algorithm for drug positioning and rescue (RADR).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3114-3114
Author(s):  
Umesh Kathad ◽  
Yuvanesh Vedaraju ◽  
Aditya Kulkarni ◽  
Gregory Tobin ◽  
Panna Sharma
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Response Prediction ◽  
Supervised Machine Learning ◽  
Clinical Validation ◽  
Tumor Type ◽  
Patient Records ◽  
Number Of Patients

3114 Background: The Response Algorithm for Drug positioning and Rescue (RADR) technology is Lantern Pharma's proprietary Artificial Intelligence (Al)-based machine learning approach for biomarker identification and patient stratification. RADR is a combination of three automated modules working sequentially to generate drug- and tumor type-specific gene signatures predictive of response. Methods: RADR integrates genomics, drug sensitivity and systems biology inputs with supervised machine learning strategies and generates gene expression-based responder/ non-responder profiles for specific tumor indications with high accuracy, in addition to identification of new correlations of genetic biomarkers with drug activity. Pre-treatment patient gene expression profiles along with corresponding treatment outcomes were used as algorithm inputs. Model training was typically performed using an initial set of genes derived from cancer cell line data when available, and further applied to patient data for model tuning, cross-validation and final gene signature development. Model testing and performance computation were carried out on patient records held out as blinded datasets. Response prediction accuracy and sensitivity were among the model performance metrics calculated. Results: On average, RADR achieved a response prediction accuracy of 80% during clinical validation. We present retrospective analyses performed as part of RADR validation using more than 10 independent datasets of patients from selected cancer types treated with approved drugs including chemotherapy, targeted therapy and immunotherapy agents. For an instance, the application of the RADR program to a Paclitaxel trial in breast cancer patients could have potentially reduced the number of patients in the treatment arm from 92 unselected patients to 24 biomarker-selected patients to produce the same number of responders. Also, we cite published evidence correlating genes from RADR derived biomarkers with increased Paclitaxel sensitivity in breast cancer. Conclusions: The value of RADR platform architecture is derived from its validation through the analysis of about ~17 million oncology-specific clinical data points, and ~1000 patient records. By implementing unique biological, statistical and machine learning workflows, Lantern Pharma's RADR technology is capable of deriving robust biomarker panels for pre-selecting true responders for recruitment into clinical trials which may improve the success rate of oncology drug approvals.

scholarly journals Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles

Genetics ◽  
2016 ◽  
Vol 203 (3) ◽  
pp. 1425-1438 ◽  
Author(s):  
Ana I. Vazquez ◽  
Yogasudha Veturi ◽  
Michael Behring ◽  
Sadeep Shrestha ◽  
Matias Kirst ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Prediction Accuracy ◽  
Whole Genome ◽  
Breast Cancer Patients ◽  
Variance Explained

scholarly journals Empirical Processing of Breast Cancer Prediction Strategies using DEFS Algorithm

2020 ◽  
Vol 8 (5) ◽  
pp. 3081-3087
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Differential Evolution ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Selection Process ◽  
False Negative ◽  
Pollution Level ◽  
Survival Ratio ◽  
True Negative

Now-a-days an important threat to women over global manner is Breast-Cancer, which is the major disease cause drastic affection to female especially. Identification of Breast Cancer over earlier stages is must to save one's life and the significant affection range of Breast-Cancer is drastically improved day by day due to the improper food-habits, pollution-level and improper-life style as well as genetic-issues also. The main cause of this disease is the arising of breast-ample over the ‘breast-area, which develops the cancer to women in several cases. If the detection or prediction of such masses over earlier stage will helps to women to get more survival ratio as well as this leads a proportion to researchers to make an systematic process to detect such diseases on initial stages by using intelligent prediction methodologies with high accuracy rates. In this paper, the proposed system handles several stages of processing to make sure the prediction accuracy, such steps are as follows: Data acquisition, Feature vector formation by normalization, Feature Selection by using Differential Evolution based selection methodology, Classification using Subspace Ensemble Learning and different Performance Measures. By using these strategies the entire work assures the proposed system is perfect to predict or identify the breast cancer benign/malignant stages more accurately compare to the classical Margin-Based Feature-Selection process. Compared to the classical biopsy methodology, a systematic diagnosis attains more impact due to its prediction accuracy. This proposed system is powered by a powerful approach called Differential-Evolution Feature’-Selection (“DEFS”) with the association of Subspace Ensemble Learning Classification principle, which provides highest accuracy and prediction rates compare to the classical methodologies. This proposed paper assures effective and robust mining strategies in Breast Cancer identification/prediction as well as efficient decision-making norms. The proposed outcome proves the good accuracy and resulting levels by means of Precision-Recall, Sensitivity and Specificity, True Positive/True Negative, False Positive/False Negative, Accuracy and Time Consumption.

BioDog, biomarker detection for improving identification power of breast cancer histologic grade in methylomics

Epigenomics ◽  
2019 ◽  
Vol 11 (15) ◽  
pp. 1717-1732 ◽  
Author(s):  
Yexian Zhang ◽  
Chaorong Chen ◽  
Meiyu Duan ◽  
Shuai Liu ◽  
Lan Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Prediction Accuracy ◽  
Somatic Mutations ◽  
Bias Reduction ◽  
Histologic Grade ◽  
Biomarker Detection ◽  
Reduction Strategy ◽  
Incremental Feature Selection ◽  
Cancer Types

Aim: Breast cancer histologic grade (HG) is a well-established prognostic factor. This study aimed to select methylomic biomarkers to predict breast cancer HGs. Materials & methods: The proposed algorithm BioDog firstly used correlation bias reduction strategy to eliminate redundant features. Then incremental feature selection was applied to find the features with a high HG prediction accuracy. The sequential backward feature elimination strategy was employed to further refine the biomarkers. A comparison with existing algorithms were conducted. The HG-specific somatic mutations were investigated. Results & conclusions: BioDog achieved accuracy 0.9973 using 92 methylomic biomarkers for predicting breast cancer HGs. Many of these biomarkers were within the genes and lncRNAs associated with the HG development in breast cancer or other cancer types.

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