Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

Abstract

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

“…mRMR [ 16 ] is a mutual information- (MI-) based feature selection approach to evaluate the importance of features. This method has wide applications in tackling several biological and medical problems [ 13 , 18 – 23 ]. For variables x and y , their MI can be calculated by where p ( x ) denotes the marginal probabilistic density of x , p ( x , y ) represents the joint probabilistic density of x and y , respectively.…”

“…BioMed Research International biological and medical problems [13,[18][19][20][21][22][23]. For variables x and y, their MI can be calculated by…”

“…Although some initial analyses on such transcriptomic datasets could identify a group of candidate gene biomarkers, such as IFI6, TIMP1, and LGR6, for COVID-19 in the previous study [ 13 ], the dataset used did not contain normal controls and only divided patients into three rough groups: patients with COVID-19, those with other viral infections, and those without viral infections. Thus, the previous work could not fully identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases.…”

Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types

“…In detail, the first feature subset included the top feature in the list F , the second feature subset contained the top two features, and so forth. For each constructed feature subset, a random forest (RF) classifier was built based on samples represented by features in the subset and it was further evaluated by 10-fold cross-validation ( Kohavi, 1995 ; Li J. et al, 2020 ; Zhou et al, 2020 ; Liu et al, 2021 ; Pan et al, 2021 ; Zhang et al, 2021a , b , c ; Zhu et al, 2021 ). After testing all feature subsets, we obtained the optimal feature subset with the optimal performance.…”

Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes

“…Max-Relevance and Min-Redundancy (Peng et al, 2005) is a feature selection method that can select relevant features and filter redundant features simultaneously, which has wide applications in analysis of various biological and medical systems (Zhao et al, 2018;Chen et al, 2019;Zhang S. et al, 2019;He et al, 2020;Zhang et al, 2020Zhang et al, , 2021a. mRMR uses mutual information (MI) to estimate the feature relevance and redundancy.…”