Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data

Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification. The first stage aggregates three filter methods, namely principal component analysis, correlation, and spectral-based feature selection techniques. Next, the genetic algorithm is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely support vector machine, k-nearest neighbors, and random forest, are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation, and a comparison is made with four state-of-the-art related algorithms. Three sets of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false positive rate, precision, F-measure, and entropy. The obtained results suggest better performance of the current proposal.

Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

Recently unique spans of genetic data are produced by researchers, there is a trend in genetic exploration using machine learning integrated analysis and virtual combination of adaptive data into the solution of classification problems. Detection of ailments and infections at early stage is of key concern and a huge challenge for researchers in the field of machine learning classification and bioinformatics. Considerate genes contributing to diseases are of huge dispute to a lot of researchers. This study reviews various works on Dimensionality reduction techniques for reducing sets of features that groups data effectively with less computational processing time and classification methods that contributes to the advances of RNA-Sequencing approach.

/pdf/a-survey-of-dimension-reduction-and-classification-methods-2b0npw5o64.pdf

A survey of dimension reduction and classification methods for RNA-Seq data on malaria vector

Gene expression data of cancer has a huge feature set size, making its categorization a challenge for the existing classification methods. It contains redundancy, noise, and irrelevant genes. Therefore, feature selection/reduction plays a crucial role in the classification of such gene expression datasets. This work presents an ensemble of three filter methods, namely, Symmetrical Uncertainty (SU), chi square ( X 2 ), and Relief to reduce the feature dimensions by eliminating redundant and noisy genes. The present work designs a novel heuristic called Local Search-based Feature Selection (LSFS) that further reduces noise generated by the ensemble method. The resulting selected features are then optimized using a genetic algorithm. Afterwards, the optimal set of features is classified using three models; Support Vector Machine (SVM), k-NN (k-nearest neighbor), and Random Forest (RF) to find cancer relevant genes. Experiments are conducted using six benchmark datasets. The obtained results are compared with five state-of-the-art algorithms based on accuracy, sensitivity, specificity, F-measure, entropy, and precision. Additional experiments are carried out by manipulating the SVM kernel as a fitness value as well as using multiple distance measures and various values of k for k-NN. Prediction accuracy of the proposed system on the six benchmark datasets is 99%, 90%, 98%, 94%, 98%, and 99%. Significant outcomes obtained from experimental analysis indicate that the proposed approach improves classification of cancerous gene expression data and can be used as a practical tool for the analysis of gene expression data.

An ensemble filter-based heuristic approach for cancerous gene expression classification

Accurately classifying medical data is critical for improving diagnostic prediction system and identifying threptic targets for treatments. Analysing gene expression data has a major challenge in extracting disease-related genes from the large number of genes output from next generation sequencing technology. Therefore, eliminating irrelevant and redundant genes is a major step to process data for prediction. Our objective is to predict more accurately the presence of cancer disease in a sample cell from the gene expression.In this paper, we create a function called Classification Technique as Feature Selection (CTFS) as a new feature selection (FS) method to extract a subset (small number) of genes from classified big number of genes expression to improve cancer prediction result. The enrolled classification techniques in CTFS function for selection are K-Nearest Neighbors (K-NN) and Extreme Gradient Boosting (XGBoosting) optimized by Bayesian Parameter Tuning (BPT). The feature selection methods used to investigate the performance of CTFS function are Univariate Feature Selection (UFS) and Feature Importance (FI). The classification stage is carried out after the feature selection stage using three machine learning (ML) algorithms, Naive Bayes (NB), Linear Support Vector Machine (LSVM), and Random Forest (RF). Results shows that, using XGBoosting optimized by BPT for FS outperforms FI method in terms of increasing the prediction accuracies along with minimum number of features but with higher running time. The performance of K-NN in FS outperforms all other FS methods in terms of accuracies providing an accuracy that is up to 100% when applied with LSVM on simulation dataset.

A New Feature Selection Method for Enhancing Cancer Diagnosis Based on DNA Microarray

Gene Expression data comprising more number genes and samples are used for cancer classification Gene Expression information provides the gene expression level contributing to specific action Cancer classification is done using the gene expression data In our work, feature selection methods such as mutual information and genetic algorithm are used for examining cancer microarray Gene expression Data By using feature selection methods most probable cancer associated genes are selected from large microarray Gene Expression data The objective of the present work to attain enhanced classification accuracy We have used the colon cancer dataset contains 2000 gene has 62 types, in which 40 are colon cancer tissue's types and the other 20 are normal types

Feature selection and classification in gene expression cancer data

Patients now want a contemporary, advanced healthcare system that is faster and more individualized and that can keep up with their changing needs. An edge computing environment, in conjunction with 5G speeds and contemporary computing techniques, is the solution for the latency and energy efficiency criteria to be satisfied for a real-time collection and analysis of health data. The feature of optimum computing approaches, including encryption, authentication, and classification that are employed on the devices deployed in an edge-computing architecture, has been ignored by previous healthcare systems, which have concentrated on novel fog architecture and sensor kinds. To avoid this problem in this paper, an Optimized Deep Recurrent Neural Network (O-DRNN) model is used with a multitier secured architecture. Initially, the data obtained from the patient are sent to the healthcare server in edge computing and the processed data are stored in the cloud using the Elliptic Curve Key Agreement Scheme (ECKAS) security model. The data is pre-processed and optimal features are selected using the Particle Swarm Optimization (PSO) algorithm. O-DRNN algorithm hyper-parameters are optimized using Bayesian optimization for better diagnosis. The proposed work offers superior outcomes in terms of accuracy and encryption latency while using computational cloud services.

https://doi.org/10.1080/00051144.2023.2195218

A secured and optimized deep recurrent neural network (DRNN) scheme for remote health monitoring system with edge computing

The prevalence of type 2 diabetes is fairly high worldwide. Early identification is essential for Diabetic management and prophylaxis. Due to their capacity to classify data, Machine Learning (ML) techniques are currently becoming more and more important in the field of diagnostic purposes. To aid in the diagnosis of Type 2 diabetes, a mixed proposed methodology is put out in this work. K-means has been employed in the proposed model-based minimization and a J48 decision tree is used as a classifier to classify. We were using the Pima Indians Diabetic Database from the UCI ML Repository to obtain the results of the experiment. The outcome demonstrates that, in comparison to other earlier research that is described in the literature, the proposed model has achieved more reliability. These findings demonstrate that the proposed methodology might be useful for diagnosing Type 2 diabetes.

K-Means and Decision Tree Approach to Predict the Severity of Diabetes Cases

Since knowing the likelihood of contracting an illness is crucial to maintaining a healthy lifestyle, disease prediction systems have made significant contributions to people’s daily lives. New data mining methods have allowed for the development of illness forecasting models. In particular, Single-Layer Perceptron may be used to extract useful information for building a disease prediction system from the mountain of medical data created daily. Many obstacles, such as information security and prediction efficiency, may restrict the practical application of the illness prediction system, despite its considerable promise. Therefore, determining the illness and safeguarding the data are the primary goals of the study. Many forms of preprocessing, including resizing, augmenting, and min-max based normalisation, are used to the raw data acquired by IoT devices. This process culminates in the use of a classifier based on the sparrow search optimization algorithm (SSOA) and the vibrational auto encoder (VAE) to distinguish between unhealthy and healthy occurrences. Finally, the Smartphone notifies the staff and administration in the event of a sickness incident.

Designing the Prediction Protocol based on Sparrow Search Optimization Algorithm with Vibrational Auto Encoder in E-Healthcare

https://link.springer.com/content/pdf/10.1007/s12652-022-04053-y.pdf

Retraction Note to: An improved adaptive neuro fuzzy interference system for the detection of autism spectrum disorder

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