Genomic profiles among different breast cancer survivors who received similar treatment may provide clues about the key biological processes involved in the cells and finding the right treatment. More specifically, such profiling may help personalize the treatment based on the patients’ gene expression. In this paper, we present a hierarchical machine learning system that predicts the 5-year survivability of the patients who underwent though specific therapy; The classes are built on the combination of two parts that are the survivability information and the given therapy. For the survivability information part, it defines whether the patient survives the 5-years interval or deceased. While the therapy part denotes the therapy has been taken during that interval, which includes hormone therapy, radiotherapy, or surgery, which totally forms six classes. The Model classifies one class vs. the rest at each node, which makes the tree-based model creates five nodes. The model is trained using a set of standard classifiers based on a comprehensive study dataset that includes genomic profiles and clinical information of 347 patients. A combination of feature selection methods and a prediction method are applied on each node to identify the genes that can predict the class at that node, the identified genes for each class may serve as potential biomarkers to the class’s treatment for better survivability. The results show that the model identifies the classes with high-performance measurements. An exhaustive analysis based on relevant literature shows that some of the potential biomarkers are strongly related to breast cancer survivability and cancer in general.

Analyzing the genetic activity of breast cancer survival for a specific type of therapy provides a better understanding of the body response to the treatment and helps select the best course of action and while leading to the design of drugs based on gene activity. In this work, we use supervised and nonsupervised machine learning methods to deal with a multiclass classification problem in which we label the samples based on the combination of the 5-year survivability and treatment; we focus on hormone therapy, radiotherapy, and surgery. The proposed nonsupervised hierarchical models are created to find the highest separability between combinations of the classes. The supervised model consists of a combination of feature selection techniques and efficient classifiers used to find a potential set of biomarker genes specific to response to therapy. The results show that different models achieve different performance scores with accuracies ranging from 80.9% to 100%. We have investigated the roles of many biomarkers through the literature and found that some of the discriminative genes in the computational model such as ZC3H11A, VAX2, MAF1, and ZFP91 are related to breast cancer and other types of cancer.

Due to the technological advancement in Today’s manufacturing systems, a large amount of data is generated in different volume, velocity, and variety of kinds. Extracting information from these data and make a real-time decision is a big challenge to the current manufacturing systems. This study presents a novel model that converts the iFactory learning facility into a fully Industry 4.0 (I4.0) manufacturing system. To achieve this purpose, we utilized the cyber physical system (CPS) components and sensors, the Internet of Things (IoT), deep learning methods, and cloud computing to fully meet the I4.0 enablers. Cloud computing is utilised in two phases: (1) during the model training phase to hold a large amount of product image data collected from the inspection station, and (2) during the execution of the model. The core learning model is based on a convolutional neural network (CNN) that is trained from the captured product images in the production line to predict the defective items in the line. The model was initialized by Resnet method and optimized to improve the learning rate and reduced loss function. The supervised learning model achieved high accuracy prediction performance up to 96.75% in the real-time decision making process. The model was able to extract the feature map of the normal non-detective product and use it to improving the accuracy and reducing the traffic between iFactory station and the cloud server. The model exploits the parallel computing big-data framework to achieve a real time decision making. The model can be applied to the current system and adopted as with all it is functionalities for the newer systems.

Studying the breast cancer survival genes information will help to enhance the treatment and save more patients life by identifying the gene biomarkers to recommend the proper treatment type. That is why it is now a great challenge for researchers to have more research on breast cancer especially with the significant enhancement in the fields of bioinformatics, data mining, and machine learning techniques which were a new revolution in the cancer treatment. A dataset contains the survival information and treatments methods for 1980 female breast cancer patient are used for building the prediction model, the gene expression is the features of the learning model, where the combination of the survival and treatments information are the classes. A hierarchical model that consists of hybrid feature selection and classification method are utilized to differentiate a class from the rest of the classes. The results show that a few number of gene biomarkers (gene signature) at each node which can determine the class with accuracy around 99% for survival living / deceased based on treatments which are vital to ensure that the patients will have the best potential response to a specific therapy. These signatures will be used as a predictor of survival in breast cancer.