RESUMEN
In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data. However, EHR data is challenging for training ML models because it contains irregularly timestamped diagnosis, prescription, and procedure codes. For such data, transformer-based models are promising. We extended the previously published Med-BERT model by including age, sex, medications, quantitative clinical measures, and state information. After pre-training on approximately 988 million EHRs from 3.5 million patients, we developed models to predict Acute Respiratory Manifestations (ARM) risk using the medical history of 80,211 COVID-19 patients. Compared to XGBoost and Random Forests, our transformer-based models more accurately forecast the risk of developing ARM after COVID-19 infection. We used Integrated Gradients and Bayesian networks to understand the link between the essential features of our model. Finally, we evaluated adapting our model to Austrian in-patient data. Our study highlights the promise of predictive transformer-based models for precision medicine.
RESUMEN
Prediction and understanding of tissue-specific virus-host interactions have relevance for the development of novel therapeutic interventions strategies. In addition, virus-like particles (VLPs) open novel opportunities to deliver therapeutic compounds to targeted cell types and tissues. Given our incomplete knowledge of virus-host interactions on one hand and the cost and time associated with experimental procedures on the other, we here propose a novel deep learning approach to predict virus-host protein-protein interactions (PPIs). Our method (Siamese Tailored deep sequence Embedding of Proteins - STEP) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. After evaluating the high prediction performance of STEP in comparison to an existing method, we apply it to two use cases, SARS-CoV-2 and John Cunningham polyomavirus (JCV), to predict virus protein to human host interactions. For the SARS-CoV-2 spike protein our method predicts an interaction with the sigma 2 receptor, which has been suggested as a drug target. As a second use case, we apply STEP to predict interactions of the JCV VP1 protein showing an enrichment of PPIs with neurotransmitters, which are known to function as an entry point of the virus into glial brain cells. In both cases we demonstrate how recent techniques from the field of Explainable AI (XAI) can be employed to identify those parts of a pair of sequences, which most likely contribute to the protein-protein interaction. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of natural language processing as well as XAI methods for the analysis of biological sequences. We have made our method publicly available via GitHub. The bigger pictureDevelopment of novel cell and tissue specific therapies requires a profound knowledge about protein-protein interactions (PPIs). Identifying these PPIs with experimental approaches such as biochemical assays or yeast two-hybrid screens is cumbersome, costly, and at the same time difficult to scale. Computational approaches can help to prioritize huge amounts of possible PPIs by learning from biological sequences plus already-known PPIs. In this work, we developed a novel approach (Siamese Tailored deep sequence Embedding of Proteins - STEP) that is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. We use this approach to train models by utilizing protein sequence information and known PPIs. After evaluating the high prediction performance of STEP in comparison to an existing method, we apply it to two use cases, SARS-CoV-2 and John Cunningham polyomavirus (JCV), to predict virus protein to human host interactions. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of natural language processing as well as Explainable AI methods for the analysis of biological sequence data. HighlightsO_LIA novel deep learning approach (STEP) predicts virus protein to human host protein interactions based on recent deep protein sequence embedding and a Siamese neural network architecture C_LIO_LIPrediction of protein-protein interactions of the JCV VP1 protein and of the SARS-CoV-2 spike protein C_LIO_LIIdentification of parts of sequences that most likely contribute to the protein-protein interaction using Explainable AI (XAI) techniques C_LI Data Science MaturityDSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
RESUMEN
Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimers Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.
RESUMEN
The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific communitys massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.