Identification of novel lncRNA by reanalysis of RNA-seq data in Zika Virus Infected hiNPCs.

The Zika Virus (ZIKV) infection is a serious, public health concern with no vaccines or antiviral treatments. This study aims to identify the differentially expressed long non-coding RNAs (lncRNAs) in ZIKV infected human-induced neuroprogenitor cells (hiNPCs). Though lncRNA is well-known for its role in gene regulation, its role in ZIKV infection remains unclear. Thus, taking advantage of publicly available transcriptome data, BioProject PRJNA551246 was analysed. Performed the gene ontology and pathway analysis of differentially expressed lncRNAs were functionally interpreted based on the neighbouring protein-coding genes (100 kb upstream and downstream of each lncRNAs). The study revealed 19 novels and 237 differentially expressed lncRNAs in ZIKV infected hiNPCs. They are found to be significantly enriched in type I interferon signalling pathway, negative regulation of viral genome replication, defense response to the virus, pathways involved in Influenza A and Herpes simplex infection, tumor necrosis factor signalling pathway, and apoptosis. In ZIKV, associated microcephaly type I interferon act as potential modulating factors. Type-I interferon inhibits ZIKV replication in many human cell types. The results support future studies on understanding the structure and function of the novel lncRNAs and experimental approaches to determine the role of the lncRNAs in ZIKV induced infection. Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-022-00771-1.

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples.

BACKGROUND: Machine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments. Hence, machine learning techniques are widely used in cancer detection and prognosis. METHODS: In this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets to generate sub-datasets, each of which contains a subset of the most relevant/informative attributes of the original dataset. The MFSAC method is a feature selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets. RESULTS: To assess the performance of the proposed MFSAC-EC model, it is applied on different high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing models to establish its effectiveness with respect to other models. From the experimental results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes.