Performance comparison between multi-level gene expression data in cancer subgroup classification.

Cancer is a serious disease that can affect various parts of the body such as breast, colon, lung or stomach. Each of these cancers has their own treatment dependent historical subgroups. Hence, the correct identification of cancer subgroup has almost same importance as the timely diagnosis of cancer. This is still a challenging task and a system with highest accuracy is essential. Current researches are moving towards analyzing the gene expression data of cancer patients for various purposes including biomarker identification and studying differently expressed genes, using gene expression data measured in a single level (selected from different gene levels including genome, transcriptome or translation). However, previous studies showed that information carried by one level of gene expression is not similar to another level. This shows the importance of integrating multi-level omics data in these studies. Hence, this study uses tumor gene expression data measured from various levels of gene along with the integration of those data in the subgroup classification of nine different cancers. This is a comprehensive analysis where four different gene expression data such as transcriptome, miRNA, methylation and proteome are used in this subgrouping and the performances between models are compared to reveal the best model.

SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach.

SARS-CoV-2 pandemic is the big issue of the whole world right now. The health community is struggling to rescue the public and countries from this spread, which revives time to time with different waves. Even the vaccination seems to be not prevents this spread. Accurate identification of infected people on time is essential these days to control the spread. So far, Polymerase chain reaction (PCR) and rapid antigen tests are widely used in this identification, accepting their own drawbacks. False negative cases are the menaces in this scenario. To avoid these problems, this study uses machine learning techniques to build a classification model with higher accuracy to filter the COVID-19 cases from the non-COVID individuals. Transcriptome data of the SARS-CoV-2 patients along with the control are used in this stratification using three different feature selection algorithms and seven classification models. Differently expressed genes also studied between these two groups of people and used in this classification. Results shows that mutual information (or DEGs) along with naïve Bayes (or SVM) gives the best accuracy (0.98 ± 0.04) among these methods. Supplementary Information: The online version contains supplementary material available at 10.1007/s42979-023-01703-6.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients.

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Prolonged viral shedding prediction on non-hospitalized, uncomplicated SARS-CoV-2 patients using their transcriptome data.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is identified as a highly transmissible coronavirus which threatens the world with this deadly pandemic. WHO reported that it spreads through contact, droplet, airborne, formite, fecal-oral, bloodborne, mother-to-child and animal-to-human. Hence, viral shedding has a huge impact on this pandemic. This study uses transcriptome data of coronavirus disease 2019 (COVID-19) patients to predict the prolonged viral shedding of the corresponding patient. This prediction starts with the transcriptome features which gives the lowest root mean squared value of 16.3±3.3 using top 25 feature selected using forward feature selection algorithm and linear regression algorithm. Then to see the impact of few non-molecular features in this prediction, they were added to the model one by one along with the selected transcriptome features. However, this study shows that those features do not have any impact on prolonged viral shedding prediction. Further this study predicts the day since onset in the same way. Here also top 25 transcriptome features selected using forward feature selection algorithm gives a comparably good accuracy (accuracy value of 0.74±0.1). However, the best accuracy was obtained using the best 20 features from feature importance using SVM (0.78±0.1). Moreover, adding non-molecular features shows a great impact on mutual information selected features in this prediction.