Ashraf Abou Tabl | St. Clair College (original) (raw)

Papers by Ashraf Abou Tabl

Machine learning model for identifying gene biomarkers for breast cancer treatment survival

F1000Research, Sep 12, 2017

SupplementaryMaterial_CLN – Supplemental material for A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies

Supplemental material, SupplementaryMaterial_CLN for A Novel Approach for Identifying Relevant Ge... more Supplemental material, SupplementaryMaterial_CLN for A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies by Ashraf Abou Tabl, Abedalrhman Alkhateeb, Huy Quang Pham, Luis Rueda, Waguih ElMaraghy and Alioune Ngom in Evolutionary Bioinformatics

Although research into the use of optics in computers has increased in the last and current decad... more Although research into the use of optics in computers has increased in the last and current decades, the fact remains that electronics is still superior to optics in almost every way. Research into the use of optics at this stage mirrors the research into electronics after the 2 nd World War. The advantages of using fiber optics over wiring are the same as the argument for using optics over electronics in computers. Even through totally optical computers are now a reality, computers that combine both electronics and optics, electro-optic hybrids, have been in use for some time. In the present paper, architecture of optical interconnect is built up on the bases of four Vertical-Cavity Surface-Emitting Laser Diodes (VCSELD) and two optical links where thermal effects of both the diodes and the links are included. Nonlinear relations are correlated to investigate the power-current and the voltage-current dependences of the four devices. The good performance (high speed) of the intercon...

Deep Learning in Multi-Omics Data Integration in Cancer Diagnostic

Deep Learning for Biomedical Data Analysis, 2021

Human cell is a complex of interacting small molecules which work together to perform daily tasks... more Human cell is a complex of interacting small molecules which work together to perform daily tasks of the cell. The reading and the measurements of this different molecules are called omics, where any dysfunction among these omics may cause different diseases, and cancer is not any exception. The advances in biomedical technology in general and in software development help to speed up, and reduce the cost of reading these omics. However, the challenge is how to integrate the big amount of data from these omics to help the predictions systems in cancer outcomes, where these outcomes include the diagnosis, development, and treatment of cancer.

A novel approach to identify subtype-specific network biomarkers of breast cancer survivability

Network Modeling Analysis in Health Informatics and Bioinformatics

Background Increasing the survival rates for breast cancer has gained significant researcher inte... more Background Increasing the survival rates for breast cancer has gained significant researcher interest. However, current studies reveal that a small subset of gene makers can predict survivability for people with different breast cancer subtypes. In these studies, the selected genes are not necessarily functionally related, and hence, they may not correctly indicate the molecular mechanism behind breast cancer survivability. Also, several studies have shown there is a very low overlap between the biomarkers subsets for the same cancer disease. To improve the robustness of the classification performance and stability of detected biomarkers, recent methods involve taking existing knowledge on relations between genes into account in the classifier by aggregating functionality-related genes to produce discriminative gene subnetworks called network biomarkers. Results In this paper, using a dataset of patients with different subtypes of breast cancer, we devised a novel network-based approach by integrating a protein–protein interaction (PPI) network with gene expression data to (1) identify the network biomarkers (metagene) of breast cancer survivability and (2) predict the survivability of breast cancer patients based on their subtypes of breast cancer. Our method involves using the concept of seed genes for the identification of network biomarkers, ADASYN to solve class-imbalance, and random forest to predict the survivability of patients. We obtained the best classification performance with distance three from seed gene protein where the Gmean, F1-measure, and accuracy were respectively 0.900, 0.800, and 90.34%. The maximum size of a network biomarker with distance 3 is 9. A maximum of 34 genes is needed to accurately predict the survivability of breast cancer patients. Conclusion This method can be used to identify the survivability of breast cancer patients using gene relationship networks. It has high prediction performance, including specificity and sensitivity for both cohorts of survivals and deceased.

Identification of the Treatment Survivability Gene Biomarkers of Breast Cancer Patients via a Tree-Based Approach

Bioinformatics and Biomedical Engineering

Studying breast cancer survivability among different patients who received various treatments may... more Studying breast cancer survivability among different patients who received various treatments may help to understand the relationship between the survivability and treatment therapy based on the gene expression. In this work, we built a classifier system that predicts whether a given breast cancer patient who underwent some form of treatment (which is either hormone therapy (H), radiotherapy (R), or surgery (S)) will survive beyond five years after the treatment therapy. Our classifier is a tree-based hierarchical approach which partitions breast cancer patients according to survivability classes; each node in the tree is associated to a treatment therapy and finds a predictive subset of genes that can best predict whether a given patient will survive after that particular treatment. We applied our tree-based method to a gene expression dataset consisting of 347 treated breast cancer patients and identified potential biomarker subsets with accuracies ranging from 80.9% to 100%. We have investigated the roles of many biomarkers through the literature.

Evolutionary bioinformatics online, 2018

Analyzing the genetic activity of breast cancer survival for a specific type of therapy provides ... more 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 investigat...

Frontiers in Genetics

Genomic profiles among different breast cancer survivors who received similar treatment may provi... more 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.

Machine Learning Model for Identifying Gene Biomarkers for Breast Cancer Treatment Survival

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics, 2017

Studying the breast cancer survival genes information will help to enhance the treatment and save... more Studying the breast cancer survival genes information will help to enhance the treatment and save more patents life by identifying the genes biomarker to recommend the proper treatment type. That is why it is now a great challenge for researchers to have more research on breast cancer specially with the great 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 is used for building the prediction model, the gene expression are the features of the learning model [1], where the combination of the survival and treatments information are the classes. A hierarchal 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 ...

Due to the technological advancement in Today’s manufacturing systems, a large amount of data is ... more 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 it...

Identification of gene biomarkers for breast cancer lymph nodes metastasis using a deep neural network

Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Deep Learning Approach for Breast Cancer InClust 5 Prediction based on Multiomics Data Integration

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics