Chemotherapy is a routine treatment approach for early-stage cancers, but the effectiveness of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity. Combination chemotherapy, in which two or more drugs are applied simultaneously, offers one promising approach to address these concerns, since two single-target drugs may synergize with one another through interconnected biological processes. However, the identification of effective dual therapies has been particularly challenging; because the search space is large, combination success rates are low. Here, we present our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergistic drug combinations. Our approach involves using biologically relevant drug and cell line features with machine learning. Our machine learning model obtained the primary metric = 0.36 and the tie-breaker metric = 0.37 in the extension round of the challenge which was ranked in top 15 out of 76 submissions. Our approach also achieves a mean primary metric of 0.39 with ten repetitions of 10-fold cross-validation. Further, we analyzed our model’s predictions to better understand the molecular processes underlying synergy and discovered that key regulators of tumorigenesis such as TNFA and BRAF are often targets in synergistic interactions, while MYC is often duplicated. Through further analysis of our predictions, we were also ble to gain insight into mechanisms and potential biomarkers of synergistic drug pairs.

Background: Current approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs. Results: We designed different evaluation settings to accurately assess the performance for predicting DDIs. The settings for disjoint cross-validation produced lower performance scores, as expected, but still were good at predicting the drug interactions. We have applied Logistic Regression, Naive Bayes and Random Forest on DrugBank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. RDF2Vec with Skip-Gram generally surpasses other embedding methods. We also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. The performance was not enhanced significantly when an integrated knowledge graph including these three datasets was used. Conclusion: We showed that the knowledge embeddings are powerful predictors and comparable to current state-of-the-art methods for inferring new DDIs. We addressed the evaluation biases by introducing drug-wise and pairwise disjoint test classes. Although the performance scores for drug-wise and pairwise disjoint seem to be low, the results can be considered to be realistic in predicting the interactions for drugs with limited interaction information.

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

Background: Integrated health data are foundational for secondary use, research, and policymaking. However, data quality issues—such as missing values and inconsistencies—are common due to the heterogeneity of health data sources. Existing frameworks often use static, 1-time assessments, which limit their ability to address quality issues across evolving data pipelines. Objective: This study evaluates the AIDAVA (artificial intelligence–powered data curation and validation) data quality framework, which introduces dynamic, life cycle–based validation of health data using knowledge graph technologies and SHACL (Shapes Constraint Language)–based rules. The framework is assessed for its ability to detect and manage data quality issues—specifically, completeness and consistency—during integration. Methods: Using the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we simulated real-world data quality challenges by introducing structured noise, including missing values and logical inconsistencies. The data was transformed into source knowledge graphs and integrated into a unified personal health knowledge graph. SHACL validation rules were applied iteratively during the integration process, and data quality was assessed under varying noise levels and integration orders. Results: The AIDAVA framework effectively detected completeness and consistency issues across all scenarios. Completeness was shown to influence the interpretability of consistency scores, and domain-specific attributes (eg, diagnoses and procedures) were more sensitive to integration order and data gaps. Conclusions: AIDAVA supports dynamic, rule-based validation throughout the data life cycle. By addressing both dimension-specific vulnerabilities and cross-dimensional effects, it lays the groundwork for scalable, high-quality health data integration. Future work should explore deployment in live clinical settings and expand to additional quality dimensions.

Detection of potential Drug-Drug Interactions (DDIs) can reduce the costs associated drug administration and drug developments. It can also prevent serious adverse drug reactions possibly causing death. In this work, we have employed Rooted Page Rank algorithm in DDI network with weights calculated using therapeutic, genomic, phenotypic and chemical similarity of drugs to discover unknown DDIs. Weighting approach is inspired from the method used in collaborative filtering to score for recommendation of an item to a user based on similarities of users or items. Different than our previous work, this method enables the integration of global structure of DDI network with similarity scores of interactions to predict new DDIs. We obtained significant performance enhancement both in terms of AUC and Precision on DDI networks extracted from Drugbank. Interestingly some weighting scheme increases AUC and decreases precision such as in case of applying chemical similarity weighting. However, weighting with drug genomic similarities decreases AUC and raises precision. Therapeutic and phenotypic similarity weighting has increased performance of both in AUC and precision.

The effectiveness of machine learning models to provide accurate and consistent results in drug discovery and clinical decision support is strongly dependent on the quality of the data used. However, substantive amounts of open data that drive drug discovery suffer from a number of issues including inconsistent representation, inaccurate reporting, and incomplete context. For example, databases of FDA-approved drug indications used in computational drug repositioning studies do not distinguish between treatments that simply offer symptomatic relief from those that target the underlying pathology. Moreover, drug indication sources often lack proper provenance and have little overlap. Consequently, new predictions can be of poor quality as they offer little in the way of new insights. Hence, work remains to be done to establish higher quality databases of drug indications that are suitable for use in drug discovery and repositioning studies. Here, we report on the combination of weak supervision (i.e., programmatic labeling and crowdsourcing) and deep learning methods for relation extraction from DailyMed text to create a higher quality drug-disease relation dataset. The generated drug-disease relation data shows a high overlap with DrugCentral, a manually curated dataset. Using this dataset, we constructed a machine learning model to classify relations between drugs and diseases from text into four categories; treatment, symptomatic relief, contradiction, and effect, exhibiting an improvement of 15.5% with Bi-LSTM (F1 score of 71.8%) over the best performing discrete method. Access to high quality data is crucial to building accurate and reliable drug repurposing prediction models. Our work suggests how the combination of crowds, experts, and machine learning methods can go hand-in-hand to improve datasets and predictive models.