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Using machine learning to detect coronaviruses potentially infectious to humans.

ABSTRACT: Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from spike protein sequences of alpha and beta coronaviruses and their binding annotation to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among coronaviruses. Three viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 and Pipistrellus abramus bat coronavirus HKU5-related (both MERS related viruses), and Rhinolophus affinis coronavirus isolate LYRa3 (a SARS related virus). We further analyze the binding properties of BtCoV/133/2005 and LYRa3 using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-CoV-2 and all viral sequences released after the SARS-CoV-2 was published. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.

SUBMITTER: Gonzalez-Isunza G

PROVIDER: S-EPMC10248971 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Using machine learning to detect coronaviruses potentially infectious to humans.

Gonzalez-Isunza Georgina G Jawaid M Zaki MZ Liu Pengyu P Cox Daniel L DL Vazquez Mariel M Arsuaga Javier J

Scientific reports 20230608 1

Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from spike protein sequences of alpha and beta coronaviruses and their binding annotation to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among coronaviruses. Three vi ...[more]

PMID: 37291260

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Project description:BACKGROUND:With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets. RESULTS:We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases. CONCLUSIONS:To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.

Project description:ImportanceAn adequate system for triaging patients with head trauma in prehospital settings and choosing optimal medical institutions is essential for improving the prognosis of these patients. To our knowledge, there has been no established way to stratify these patients based on their head trauma severity that can be used by ambulance crews at an injury site.ObjectivesTo develop a prehospital triage system to stratify patients with head trauma according to trauma severity by using several machine learning techniques and to evaluate the predictive accuracy of these techniques.Design, setting, and participantsThis single-center retrospective cohort study was conducted by reviewing the electronic medical records of consecutive patients who were transported to Tokyo Medical and Dental University Hospital in Japan from April 1, 2018, to March 31, 2021. Patients younger than 16 years with cardiopulmonary arrest on arrival or with a significant amount of missing data were excluded.Main outcomes and measuresMachine learning-based prediction models to detect the presence of traumatic intracranial hemorrhage were constructed. The predictive accuracy of the models was evaluated with the area under the receiver operating curve (ROC-AUC), area under the precision recall curve (PR-AUC), sensitivity, specificity, and other representative statistics.ResultsA total of 2123 patients (1527 male patients [71.9%]; mean [SD] age, 57.6 [19.8] years) with head trauma were enrolled in this study. Traumatic intracranial hemorrhage was detected in 258 patients (12.2%). Among several machine learning algorithms, extreme gradient boosting (XGBoost) achieved the mean (SD) highest ROC-AUC (0.78 [0.02]) and PR-AUC (0.46 [0.01]) in cross-validation studies. In the testing set, the ROC-AUC was 0.80, the sensitivity was 74.0% (95% CI, 59.7%-85.4%), and the specificity was 74.9% (95% CI, 70.2%-79.3%). The prediction model using the National Institute for Health and Care Excellence (NICE) guidelines, which was calculated after consultation with physicians, had a sensitivity of 72.0% (95% CI, 57.5%-83.8%) and a specificity of 73.3% (95% CI, 68.7%-77.7%). The McNemar test revealed no statistically significant differences between the XGBoost algorithm and the NICE guidelines for sensitivity or specificity (P = .80 and P = .55, respectively).Conclusions and relevanceIn this cohort study, the prediction model achieved a comparatively accurate performance in detecting traumatic intracranial hemorrhage using only the simple pretransportation information from the patient. Further validation with a prospective multicenter data set is needed.

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Using machine learning to detect coronaviruses potentially infectious to humans.

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Using machine learning to detect coronaviruses potentially infectious to humans.

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