Elephant Search with Deep Learning for Microarray Data Analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl

MapReduce based Classification for Microarray data using Parallel Genetic Algorithm

Inorder to uncover thousands of genes Microarray   produces high throughput is used. Only few gene expression data out of thousands of data is used for disease predication and also for disease classification in medical environment.  To find such initial coexpressed gene groups of clusters whose joint expression is strongly related with the class label A Supervised attribute clustering is used. By sharing the information between each attributes the Mutual Information uses the information of sample varieties to measure the similarity among the attributes. From this the redundant and irrelevant attributes are removed. After forming the clusters the PGA is used to find the optimal feature and is given as mapper function so as to improve the class separability. Using this method the diagnosis can be made easier and effective since its done parallelly. The predictive accuracy is estimated using all the three classifiers such as K-nearest neighbours including naive bayes and Support Vector machine. Thus the overall approach used reducer function which provides excellent predictive capability for accurate medical diagnosis

Deep Learning Applications in Medical Bioinformatics

After a patient’s breast cancer diagnosis, identifying breast cancer lymph node metastases is one of the most important and critical factor that is directly related to the patient’s survival. The traditional way to examine the existence of cancer cells in the breast lymph nodes is through a lymph node procedure, biopsy. The procedure process is time-consuming for the patient and the provider, costly, and lacks accuracy as not every lymph node is examined. The intent of this study is to develop an artificial neural network (ANNs) that would map genetic biomarkers to breast lymph node classes using ANNs. The neural network classes were adopted from the American joint committee on cancer materials pathologic lymph node statuses. Twenty gene biomarkers were selected based on a hybrid feature selection model, and the neural network parameters were configured using hyperparameter tuning techniques targeting the neural network capacity, activation function, weight initialization, and the neural network learning rate and momentum. The METABRIC breast cancer data set was used to train, validate, and test the neural network. The results show an accuracy of 96% for the training dataset and 85% for both the validation and test dataset. As far as the area under the curve (AUC), the neural network scored 100% for the training dataset, 95% for the validation, and finally, 90% for the test dataset. This study directly benefits and supports the cancer organizations transition from identifying cancer based on the organ in the patient’s body, where cancer first starts to develop as well as the shape of cancer under a microscope, to grouping cancer cells based on gene mutations. This change in cancer identification will assist the providers in improving the diagnosis, prognosis, and treatment of cancer patients

Deep Learning Applications in Medical Bioinformatics

After a patient’s breast cancer diagnosis, identifying breast cancer lymph node metastases is one of the most important and critical factor that is directly related to the patient’s survival. The traditional way to examine the existence of cancer cells in the breast lymph nodes is through a lymph node procedure, biopsy. The procedure process is time-consuming for the patient and the provider, costly, and lacks accuracy as not every lymph node is examined. The intent of this study is to develop an artificial neural network (ANNs) that would map genetic biomarkers to breast lymph node classes using ANNs. The neural network classes were adopted from the American joint committee on cancer materials pathologic lymph node statuses. Twenty gene biomarkers were selected based on a hybrid feature selection model, and the neural network parameters were configured using hyperparameter tuning techniques targeting the neural network capacity, activation function, weight initialization, and the neural network learning rate and momentum. The METABRIC breast cancer data set was used to train, validate, and test the neural network. The results show an accuracy of 96% for the training dataset and 85% for both the validation and test dataset. As far as the area under the curve (AUC), the neural network scored 100% for the training dataset, 95% for the validation, and finally, 90% for the test dataset. This study directly benefits and supports the cancer organizations transition from identifying cancer based on the organ in the patient’s body, where cancer first starts to develop as well as the shape of cancer under a microscope, to grouping cancer cells based on gene mutations. This change in cancer identification will assist the providers in improving the diagnosis, prognosis, and treatment of cancer patients