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

© 2020, Springer-Verlag London Ltd., part of Springer Nature. 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

New Isomers in the Full Seniority Scheme of Neutron-Rich Lead Isotopes: The Role of Effective Three-Body Forces

The neutron-rich lead isotopes, up to Pb-216, have been studied for the first time, exploiting the fragmentation of a primary uranium beam at the FRS-RISING setup at GSI. The observed isomeric states exhibit electromagnetic transition strengths which deviate from state-of-the-art shell-model calculations. It is shown that their complete description demands the introduction of effective three-body interactions and two-body transition operators in the conventional neutron valence space beyond Pb-208.INFN, ItalyINFN, ItalyMICINN, Spain [AIC10-D-000568]MICINN, SpainGeneralitat Valenciana, SpainGeneralitat Valenciana, Spain [FPA2008-06419, PROMETEO/2010/101]UK STFCUK STFCAWE plcAWE plcDFGDFG [EXC 153