Breast Cancer Dataset Research Articles

Abstract IA05: Leveraging preclinical models of triple negative breast cancer for translational research

Abstract We have developed multiple novel preclinical syngeneic triple negative breast cancer(TNBC) genetically engineered mouse (GEM) models, which have been characterized genomically and with respect to their immune microenvironments. By integrating the immunological characterization of murine syngeneic mammary tumor models with analyses of human breast cancer datasets, we have demonstrated a relationship between EMT and myeloid cells, specifically tumor-associated macrophages (TAMs). We also have leveraged our syngeneic GEM models to define the response to immune checkpoint blockade therapy (ICBT) with emphasis on the myeloid cell environment. We have used our GEM models to test single agent drugs along with standard-of-care therapies for appropriate durations. Finally, we have tested these treatment regimens on established metastases with sufficient tumor burdens. Specifically, we will present results illustrating how CSF-1R targeting therapy combined with metronomic chemotherapy enhances a B and T cell response for the treatment of metastatic TNBC. In additional we well discuss how targeting the RNA helicase eIF4A that unwinds the secondary structures in the 5’UTR of mRNAs to facilitate translation initiation triggers an interferon response to suppress TNBC. Citation Format: Jeffrey M Rosen, Diego Pedroza, Na Zhao, Xiang H-F Zhang, Charles M Perou. Leveraging preclinical models of triple negative breast cancer for translational research [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr IA05.

Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes.

Developments in biotechnologies enable multi-platform data collection for functional genomic units apart from the gene. Profiling of non-coding microRNAs (miRNAs) is a valuable tool for understanding the molecular profile of the cell, both for canonical functions and malignant behavior due to complex diseases. We propose a graphical mixed-effects statistical model incorporating miRNA-gene target relationships. We implement an integrative pathway analysis that leverages measurements of miRNA activity for joint analysis with multimodal observations of gene activity including gene expression, methylation, and copy number variation. We apply our analysis to a breast cancer dataset, and consider differential activity in signaling pathways across breast tumor subtypes. We offer discussion of specific signaling pathways and the effect of miRNA integration, as well as publish an interactive data visualization to give public access to the results of our analysis.

Breast Cancer Classification and Predicting Class Labels Using ResNet50

Numerous studies have been conducted using Deep Learning paradigms to detect Breast Cancer. Breast cancer is a medical condition where abnormal cells in the breast grow uncontrollably, forming tumors. If not treated, these tumors can metastasize and spread to other parts of the body, potentially leading to life-threatening consequences. In the year 2020, there were approximately 2.3 million cases of breast cancer diagnosed in women, leading to around 685,000 deaths worldwide. By the close of 2020, there existed 7.8 million women who had been diagnosed with breast cancer within the preceding five years, solidifying it as the most widespread form of cancer globally. Breast cancer is observed across all countries, affecting women at various ages post-puberty, with incidence rates tending to rise in older age groups. The aim of this paper is to classify and predict the class labels of breast cancer. To achieve this, a ResNet50 model is utilized and mammography images are employed to locate cancer within the image and classify it to emphasize the affected area. The ResNet50 identifies mass regions and classifies them as either ductal carcinoma, inflammatory, triple negative or invasive cancer. The experimentation is carried out on breast cancer dataset and achieved 90.6% accuracy both for classification as well as prediction

Comparative analysis of machine learning algorithms for breast cancer prediction

Breast cancer is a global health concern, and early diagnosis is crucial for successful treatment. The objective of this paper is to conduct a comparative analysis of machine-learning algorithms for the prediction of breast cancer. This study used the Wisconsin Diagnostic Breast Cancer Dataset. Data preparation, technique selection, and performance evaluation are included in the study. The inquiry begins by comparing malignant and benign instances according to input factors and diagnostic outcomes. Finding components having an inverse relationship to the diagnosis is prioritized. Next, a careful approach is used to choose attributes to improve the dataset for model construction. The preprocessed data trains and optimizes four well-known machine learning algorithms: Random Forest, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression. The models are evaluated for accuracy, precision, recall, F1-score, and ROC curve. This study aimed to evaluate numerous breast cancer prediction systems to determine their strengths and weaknesses. To provide openness and replicability, the study uses the Jupyter Notebook platform, Python, and data analytic tools. The logistic regression model has a test accuracy percentage of 99.26%, surpassing all other models examined in this study. Furthermore, it has a minimum false positive rate (FPR) of 1 and a false negative rate (FNR) of 4. The model exhibits a higher level of precision in comparison to the studies examined in the literature review. This study is crucial for early diagnosis and therapy development. The effects include lower healthcare expenses, better patient outcomes, and better diagnostics. Machine learning has shown promise in fighting breast cancer, boosting its relevance in healthcare.

Iterative Tuning of Tree-Ensemble-Based Models' parameters Using Bayesian Optimization for Breast Cancer Prediction

The study presents a method for iterative parameter tuning of tree ensemble-based models using Bayesian hyperparameter tuning for states prediction, using breast cancer as an example. The proposed method utilizes three different datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, and the Breast Cancer Coimbra dataset (BCCD), and implements tree ensemble-based models, specifically AdaBoost, Gentle-Boost, LogitBoost, Bag, and RUSBoost, for breast cancer prediction. Bayesian optimization was used to tune the hyperparameters of the models iteratively, and the performance of the models was evaluated using several metrics, including accuracy, precision, recall, and f1-score. Our results show that the proposed method significantly improves the performance of tree ensemble-based models, resulting in higher accuracy, precision, recall, and f1-score. Compared to other state-of-the-art models, the proposed method is more efficient. It achieved perfect scores of 100% for Accuracy, Precision, Recall, and F1-Score on the WDBC dataset. On the SEER BC dataset, the method achieved an accuracy of 95.9%, a precision of 97.6%, a recall of 94.2%, and an F1-Score of 95.9%. For the BCCD dataset, the method achieved an accuracy of 94.7%, a precision of 90%, a recall of 100%, and an F1-Score of 94.7%. The outcomes of this study have important implications for medical professionals, as early detection of breast cancer can significantly increase the chances of survival. Overall, this study provides a valuable contribution to the field of breast cancer prediction using machine learning.

Development of prognostic model and multivariate analysis for breast cancer survival patients using SEER database

Background: Many studies employed machine learning (ML) to forecast the prognosis of breast cancer (BC) patients and discovered that the ML model showed high individualized forecasting ability. Breast cancer is the most frequent kind of carcinoma in women globally and ranks as the leading cause of death in women. Objectives: This study intends to use the Surveillance, Epidemiology, and End Results dataset to categorize breast carcinoma cases’ alive and dead conditions. Deep learning and machine learning have been extensively utilized in clinical studies to address various categorization problems due to their ability to manage massive data sets in an organized manner. Pre-processing the data allows it to be visualized and analyzed for making critical choices. This study describes a realistic machine learning-based strategy for categorizing the SEER breast cancer dataset. Materials and methods: We employed classification and machine learning algorithms to classify breast cancer mortality. Four well-known classification ML algorithms were employed in this study. To identify risk factors, we employed multivariate analysis using the data set. Results: The decision tree performed the best accuracy (0.914) among all the models. T4 stage (β=1.4, p<0.001, OR=4.22, 95% CI (2.06-8.64), N2 stage (β=0.39, p=0.008, OR= 1.49, 95% CI (1.111-1.997) found to be major risk factors for breast cancer mortality using multivariate analysis. Conclusion: The significant prognostic variables affecting the breast carcinoma survival rates reported in the current research are relevant and might be turned into decision support systems in the medical realm.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

Collaborative Transfer Network for Multi-Classification of Breast Cancer Histopathological Images.

The incidence of breast cancer is increasing rapidly around the world. Accurate classification of the breast cancer subtype from hematoxylin and eosin images is the key to improve the precision of treatment. However, the high consistency of disease subtypes and uneven distribution of cancer cells seriously affect the performance of multi-classification methods. Furthermore, it is difficult to apply existing classification methods to multiple datasets. In this article, we propose a collaborative transfer network (CTransNet) for multi-classification of breast cancer histopathological images. CTransNet consists of a transfer learning backbone branch, a residual collaborative branch, and a feature fusion module. The transfer learning branch adopts the pre-trained DenseNet structure to extract image features from ImageNet. The residual branch extracts target features from pathological images in a collaborative manner. The feature fusion strategy of optimizing these two branches is used to train and fine-tune CTransNet. Experiments show that CTransNet achieves 98.29% classification accuracy on the public BreaKHis breast cancer dataset, exceeding the performance of state-of-the-art methods. Visual analysis is carried out under the guidance of oncologists. Based on the training parameters of the BreaKHis dataset, CTransNet achieves superior performance on other two public breast cancer datasets (breast-cancer-grade-ICT and ICIAR2018_BACH_Challenge), indicating that CTransNet has good generalization performance.

Transcriptome-based identification of key actin-binding proteins associated with high metastatic potential in breast cancer.

Actin-binding proteins (ABPs) are essential for the regulation of morphological plasticity required for tumor cells to metastasize. The aim of this study was to perform an unbiased bioinformatic approach to identify the key ABPs significantly associated with the metastatic potential of breast cancer cells. Microarray data from 181 primary breast cancer samples from our hospital were used, and all genes belonging to the Gene Ontology term actin cytoskeleton organization were obtained from QuickGO. Association with metastasis-free survival probability was tested using Cox proportional hazards regression, and pairwise co-expression was tested by Pearson correlations. Differential expression between different subgroups was analyzed using Wilcoxon tests for dichotomous traits and Kruskal-Wallis tests for categorical traits. Validation was performed using four publicly available breast cancer datasets. ARHGAP25 was significantly associated with a low metastatic potential, and CFL1, TMSB15A, and ACTL8 were significantly associated with a high metastatic potential. A significantly higher expression of CFL1, TMSB15A, and ACTL8 mRNA was found in the more aggressive Her2-positive and triple-negative subtypes as well as in ER-negative samples. Also, these genes were co-expressed in the same tumors. However, only mRNA levels of CFL1 were increased in pN1 compared to pN0 patients. External validation revealed that CFL1 and TMSB15A had significant associations with consistent hazard ratios in two breast cancer cohorts, and among these, CFL1 exhibited the highest hazard ratios. CFL1 showed the strongest correlation with the metastatic potential of breast tumors. Thus, targeted inhibition of CFL1 might be a promising approach to treat malignant breast cancer cells.

A bioinformatic analysis found low expression and clinical significance of ATF4 in breast cancer

BackgroundActivating Transcription Factor 4 (ATF4) expression exhibits differential patterns across different types of tumors. Besides, the pathogenesis of breast cancer is complex, and the exact relationship between ATF4 and ATF4 remains uncertain. MethodsThe analysis of ATF4 expression was conducted by utilizing The Cancer Genome Atlas (TCGA) pan-cancer data, while the gene expression profile of breast cancer was checked by the comprehensive database-Gene Expression Omnibus database. In order to gain a more comprehensive understanding of the specific cell types that exhibit ATF4 expression within the microenvironment of breast cancer, we conducted a single-cell analysis of ATF4 using two distinct datasets of human breast cancer (GSE114717 and GSE11088, respectively). The spatial distribution of ATF4 within a tissue was demonstrated based on datasets obtained from the Human Protein Atlas (HPA) and SpatialDB. The clinical prognostic significance of ATF4 was assessed by analyzing clinical survival data obtained from TCGA, GSE4830, and GSE25055 datasets. We used the R package clusterProfiler to carry out an enrichment analysis of ATF4. We assessed how ATF4 impacts the growth and movement of breast cancer cell lines. We manipulated ATF4 levels using plasmid transfection techniques. ResultsThe expression of ATF4 was found to be suboptimal and demonstrated a significant correlation with enhanced disease-specific survival (p = 0.012) and overall survival (p = 0.032) in breast cancer as well as other malignancies. We conducted an analysis to investigate the interaction between the infiltration level of immune cells and the expression of ATF4, using samples obtained from TCGA with known immune cell infiltration scores. Furthermore, a notable positive correlation exists between the elevated expression of ATF4 and immune-related genomes, specifically those associated with chemokine as well as immunity. Subsequent examination revealed a notable augmentation in the cytodifferentiation of T cells into regulatory T (Treg) cells within tissues exhibiting elevated levels of ATF4 expression. ATF4 exhibits notable upregulation in the MDA-MB-231 cell, thereby exerting a substantial impact on cell proliferation and migration upon its knockdown. Conversely, the overexpression of ATF4 in the MCF7 Luminal A breast cancer cell line can also modulate cellular function. ConclusionsOur study suggests that ATF4 helps T cells differentiate into Treg cells in breast cancer. ATF4 can represent a clinically useful biomarker to predict the overall survival rate, especially in patients with different subtypes of breast cancer. Provide certain guidance value for the development of targeted drugs or inhibitors targeting ATF4.

Identification of cancer driver genes based on dynamic incentive model.

Cancer is a complex genomic mutation disease, and identifying cancer driver genes promotes the development of targeted drugs and personalized therapies. The current computational method takes less consideration of the relationship among features and the effect of noise in protein-protein interaction(PPI) data, resulting in a low recognition rate. In this paper, we propose a cancer driver genes identification method based on dynamic incentive model, DIM. This method firstly constructs a hypergraph to reduce the impact of false positive data in PPI. Then, the importance of genes in each hyperedge in hypergraph is considered from three perspectives, network and functional score(NFS) is proposed. By analyzing the relation among features, the dynamic incentive model is proposed to fuse NFS, the differential expression score of mRNA and the differential expression score of miRNA. DIM is compared with some classical methods on breast cancer, lung cancer, prostate cancer, and pan-cancer datasets. The results show that DIM has the best performance on statistical evaluation indicators, functional consistency and the partial area under the ROC curve, and has good cross-cancer capability.

A framework for block-wise missing data in multi-omics.

High-throughput technologies have generated vast amounts of omic data. It is a consensus that the integration of diverse omics sources improves predictive models and biomarker discovery. However, managing multiple omics data poses challenges such as data heterogeneity, noise, high-dimensionality and missing data, especially in block-wise patterns. This study addresses the challenges of high dimensionality and block-wise missing data through a regularization and constrained-based approach. The methodology is implemented in the R package bwm for binary and continuous response variables, and applied to breast cancer and exposome multi-omics datasets, achieving strong performance even in scenarios with missing data present in all omics. In binary classification task, our proposed model achieves accuracy in the range of 86% to 92%, and F1 in the range of 68% to 79%. And, in regression task the correlation between true and predicted responses is in the range of 72% to 76%. However, there is a slight decline in performance metrics as the percentage of missing data increases. In scenarios where block-wise missing data affects multiple omics, the model performance actually surpasses that of scenarios where missing data is present in only one omics. One possible explanation for this might be that the other scenarios introduce a greater diversity of observation profiles, leading to a more robust model. Depending on the specific omics being studied, there is greater consistency in feature selection when comparing block-wise missing data scenarios.

Performance assessment of hybrid machine learning approaches for breast cancer and recurrence prediction.

Breast cancer is a major health concern for women everywhere and a major killer of women. Malignant tumors may be distinguished from benign ones, allowing for early diagnosis of this disease. Therefore, doctors need an accurate method of diagnosing tumors as either malignant or benign. Even if therapy begins immediately after diagnosis, some cancer cells may persist in the body, increasing the risk of a recurrence. Metastasis and recurrence are the leading causes of death from breast cancer. Therefore, detecting a return of breast cancer early has become a pressing medical issue. Evaluating and contrasting various Machine Learning (ML) techniques for breast cancer and recurrence prediction is crucial to choosing the best successful method. Inaccurate forecasts are common when using datasets with a large number of attributes. This study addresses the need for effective feature selection and optimization methods by introducing Recursive Feature Elimination (RFE) and Grey Wolf Optimizer (GWO), in response to the limitations observed in existing approaches. In this research, the performance evaluation of methods is enhanced by employing the RFE and GWO, considering the Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC) datasets taken from the UCI-ML repository. Various preprocessing techniques are applied to raw data, including imputation, scaling, and others. In the second step, relevant feature correlations are used with RFE to narrow down candidate discriminative features. The GWO chooses the best possible combination of attributes for the most accurate result in the next step. We use seven ML classifiers in both datasets to make a binary decision. On the WDBC and WPBC datasets, several experiments have shown accuracies of 98.25% and 93.27%, precisions of 98.13% and 95.56%, sensitivities of 99.06% and 96.63%, specificities of 96.92% and 73.33%, F1-scores of 98.59% and 96.09% and AUCs of 0.982 and 0.936, respectively. The hybrid approach's superior feature selection improved the accuracy of breast cancer performance indicators and recurrence classification.

Research on Data Security and Privacy Protection Strategies in Hospital Information Management

Abstract Hospital information security, especially the management of hospital information, is of great significance to improve hospital quality, promote resource sharing, and enhance hospital competitiveness. Despite their unique advantages in preventing transmission data leakage when dealing with medical data, federated learning algorithms still have some shortcomings. Based on this, this study proposes to combine the improved TVFedmul algorithm with the federated learning technique to enhance the efficiency of information aggregation and also proposes to utilize the Gaussian difference privacy algorithm to enhance the protection of private data. Four datasets from cancer rehabilitation data are utilized as research samples in experiments. Compared with the FedAvg algorithm, the TVFedmul algorithm is relatively leading in accuracy, e.g., the accuracy enhancement on the same-distribution dataset of renal cancer reaches 3.03%, and the performance enhancement in the C-domain of the non-simultaneous-distribution dataset of breast cancer reaches 14.2%. The TVFedmul algorithm’s model aggregation speed is also faster, which can effectively improve the efficiency of information aggregation. Although the privacy mechanism of the Gaussian differential privacy algorithm affects the accuracy of the model, its accuracy convergence is not much different from that of federated learning without differential privacy, implying that the Gaussian differential privacy algorithm utilizes a small performance loss to provide more valuable privacy protection.

Evaluation of Adaptive Synthetic Resampling Technique for Imbalanced Breast Cancer Identification

As one of the most common types of cancer among women, breast cancer is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. However, detecting breast cancer is challenging due to imbalanced classification, where the minority class (cancerous) is ominously smaller than the majority class (non-cancerous). In this paper, we explore the use of logistic regression (LR) and the adaptive synthetic resampling (ADASYN) technique to address imbalanced classification in breast cancer detection. To that end, we collected the Wisconsin Breast Cancer dataset, which contains 569 instances. The dataset is imbalanced, with 212 malignant (cancerous) cases and 357 benign (non-cancerous) cases. Then, we trained support vector machine, LR, K-nearest neighbor, gradient, and adaptive boosting on the imbalanced dataset. Finally, we trained these algorithms on resampled data with the ADASYN oversampling and we evaluated their performance using cross-validation score with 5-folds. The results of the experiment showed that using ADASYN with LR significantly improved the performance the LR model. The LR model achieves 99.46% accuracy on breast cancer diagnosis. Moreover, the confusion matrix shows that among the 188 samples, the model misclassified one cancerous instance. Thus, we concluded that the proposed model is effective for breast cancer diagnosis.

Intelligent Model for the Early Detection of Breast Cancer Using Fine Needle Aspiration of Breast Mass.

Breast cancer is a type of cancer that forms in the cells of the breasts. It can occur in both men and women, but it is far more common in women with an increase likelihood of development caused by some risk factors like age, family history, gene mutations, hormonal factors, and exposure to estrogen. There are several essential methods for early diagnostic and detection, like regular breast self-examinations, clinical breast examination mammograms, etc. Breast cancer detection faces several challenges that span various stages from screening to diagnosis. It is in the light of these, that the drive to overcome these challenges faced by patients and medical experts to get timely diagnosis or detection of breast cancer that motivated this research. The work aims at developing an intelligent-based system for the early detection of breast cancer driven by machine learning algorithm using features obtained from a digitized image of the Fine Needle Aspiration (FNA) of breast mass. The Machine learning algorithm used was the Support Vector Machine (SVM) and was apply to the Breast Cancer dataset. Additionally, feature selection and a neural network model was applied to improve the performance of the system. This work demonstrates that SVM algorithm can be used to enhance the early detection of breast cancer. Compared with the 92% accuracy of mammography, this machine learning model achieved a higher accuracy of around 98.18% for breast cancer detection and diagnosis based on the dataset that was used.

Dissecting Spatiotemporal Structures in Spatial Transcriptomics via Diffusion-Based Adversarial Learning.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms.

The incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community. This study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant. Our research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm. The results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques. The constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.

Machine Learning Approaches for Investigating Breast Cancer

ABSTRACT: This study aims to predict whether the case is malignant or benign and concentrate on the anticipated diagnosis; if the case is malignant, it is advised to admit the patient to the hospital for treatment. The primary goal of this work is to put together models in two distinct datasets to predict breast cancer more accurately, faster, and with fewer errors than before. Then contrast the techniques that produced datasets with the highest accuracy. In this study, the datasets were processed using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbours, Artificial Neural Network, Nave Bayes, Stochastic Gradient Descent (SGD),Gradient boosting classifiers(GBC), Stochastic Gradient Boosting (SGB), Extreme Gradient Boosting (XGBoost),and Random Forest. Two datasets—the Wisconsin Diagnostic Breast Cancer dataset and the Breast Cancer dataset—are used to test these methods. to evaluate the findings and choose the algorithm that is more adept in predicting breast cancer. Seven algorithms that operate on both datasets in the AI platform were used to build the article. Breast cancer prediction has gotten much harder because so many people die from the disease in its early stages. Consequently, using two real-time datasets, one for Wisconsin diagnosis and the other for research on breast cancer. The same methods are applied to both datasets, and it is found that SVM provides the best accuracy in the shortest time and with the lowest error rate.

The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions.

Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm's evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately.