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https://www.selleckchem.com/ Understanding the different effects of chemical substances on human proteins is fundamental for designing new drugs. It is also important for elucidating the different mechanisms of action of drugs that can cause side-effects. In this context, computational methods for predicting chemical-protein interactions can provide valuable insights on the relation between therapeutic chemical substances and proteins. Their predictions therefore can help in multiple tasks such as drug repurposing, identifying new drug side-effects, etc. Despite their useful predictions, these methods are unable to predict the different implications - such as change in protein expression, abundance, etc, - of chemical - protein interactions. Therefore, In this work, we study the modelling of chemical-protein interactions' effects on proteins activity using computational approaches. We hereby propose using 3D tensors to model chemicals, their target proteins and the effects associated to their interactions. We then use multi-part embedding tensor factorisation to predict the different effects of chemicals on human proteins. We assess the predictive accuracy of our proposed method using a benchmark dataset that we built. We then show by computational experimental evaluation that our approach outperforms other tensor factorisation methods in the task of predicting effects of chemicals on human proteins.Research to support precision medicine for leukemia patients requires integration of biospecimen and clinical data. The Observational Medical Outcomes Partnership common data model (OMOP CDM) and its Specimen table presents a potential solution. Although researchers have described progress and challenges in mapping electronic health record (EHR) data to populate the OMOP CDM, to our knowledge no studies have described populating the OMOP CDM with biospecimen data. Using biobank data from our institution, we mapped 26% of biospecimen records to the OMOP Specimen table. Re

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