Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation.
Journal of medical Internet research
2022; 24 (7): e38584
BACKGROUND: Multiple types of biomedical associations of knowledge graphs, including COVID-19-related ones, are constructed based on co-occurring biomedical entities retrieved from recent literature. However, the applications derived from these raw graphs (eg, association predictions among genes, drugs, and diseases) have a high probability of false-positive predictions as co-occurrences in the literature do not always mean there is a true biomedical association between two entities.OBJECTIVE: Data quality plays an important role in training deep neural network models; however, most of the current work in this area has been focused on improving a model's performance with the assumption that the preprocessed data are clean. Here, we studied how to remove noise from raw knowledge graphs with limited labeled information.METHODS: The proposed framework used generative-based deep neural networks to generate a graph that can distinguish the unknown associations in the raw training graph. Two generative adversarial network models, NetGAN and Cross-Entropy Low-rank Logits (CELL), were adopted for the edge classification (ie, link prediction), leveraging unlabeled link information based on a real knowledge graph built from LitCovid and Pubtator.RESULTS: The performance of link prediction, especially in the extreme case of training data versus test data at a ratio of 1:9, demonstrated that the proposed method still achieved favorable results (area under the receiver operating characteristic curve >0.8 for the synthetic data set and 0.7 for the real data set), despite the limited amount of testing data available.CONCLUSIONS: Our preliminary findings showed the proposed framework achieved promising results for removing noise during data preprocessing of the biomedical knowledge graph, potentially improving the performance of downstream applications by providing cleaner data.
View details for DOI 10.2196/38584
View details for PubMedID 35658098
BETA: a comprehensive benchmark for computational drug-target prediction.
Briefings in bioinformatics
Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.
View details for DOI 10.1093/bib/bbac199
View details for PubMedID 35649342
California's Central Valley: Teaching Social Determinants of Health and Cultural Humility Through an Interprofessional, Overnight Road Trip
JOURNAL OF HEALTH CARE FOR THE POOR AND UNDERSERVED
2022; 33 (2): 819-841
This study explored the value of an overnight interprofessional road trip of students, faculty, staff, and community members through the Central Valley of California. The goal of the mobile classroom was to teach complex topics such as cultural humility, health disparities, population health, implicit bias, interprofessionalism, community engagement, and social determinants of health. Participants identified educational outcomes valuable to them and assessed how closely the experience aligned with their university's strategic goals. Pre/post-surveys consisted of Likert scale and open-ended questions over five trips (N=186). Qualitative and quantitative analyses reflected an informational and transformational experience, especially through the sharing of personal stories and connections among participants and community hosts. Participants rated the experience as strongly aligned with the university's strategic goals. This field-trip pedagogy positioned a professionally diverse group to learn together about the contributions, socio-historical complexities, and health challenges of a region where their students and patients live.
View details for Web of Science ID 000802975700002
View details for PubMedID 35574879
UNMET NEEDS AND PERCEIVED BARRIERS TO ACCESSING HOME AND COMMUNITY-BASED SERVICES AMONG CAREGIVERS OF VETERANS OF ALL ERAS
OXFORD UNIV PRESS INC. 2022: S582
View details for Web of Science ID 000788118601587
- Cultural Humility Meets Antiracism in Nurse Leader Training NURSE LEADER 2021; 19 (6): 608-615
The Relationship Between the Functional Gait Assessment and Quality-of-Life Data in Patients Undergoing Vestibular Schwannoma Resection
OTOLOGY & NEUROTOLOGY
2021; 42 (7): 1074-1080
To examine the relationship between the Functional Gait Assessment (FGA) and quality of life (QOL) measurements relating to balance before and after vestibular schwannoma (VS) resection and to assess the role of preoperative FGA in predicting postoperative QOL.A prospective clinical study of adult patients undergoing VS resection between September 2018 and December 2019. FGA was administered 1 week before and after surgery. Dizziness Handicap Inventory (DHI) and Penn Acoustic Neuroma Quality of Life (PANQOL) were administered preoperatively and at 3 months postoperatively.Single tertiary center.Patients (age ≥ 18 years old) with VS undergoing microsurgical resection. Excluded were patient with previous surgery or radiation.VS resection.Primary outcome: correlation between FGA and QOL surveys. Secondary outcome: correlation between preoperative measurements of balance and postoperative PANQOL.One hundred thirty-eight patients were analyzed (mean age: 48 years old, 65.9% female). The translabyrinthine approach was most commonly performed. Under multivariate analysis, preoperative FGA significantly correlated with preoperative PANQOL balance score (p < 0.0001), preoperative PANQOL total score (p = 0.0002), and preoperative DHI (p < 0.0001). However, postoperative FGA did not significantly correlate with postoperative PANQOL balance or total scores (p = 0.446 and p = 0.4, respectively), or postoperative DHI (p = 0.3). Univariate analysis demonstrated that preoperative DHI and preoperative FGA were predictive of changes in postoperative PANQOL balance and total scores. However under multivariate analysis, preoperative FGA did not predict changes in postoperative PANQOL balance or total score (p = 0.24; p = 0.28, respectively). Preoperative DHI remained predictive of changes in postoperative PANQOL balance (p = 0.03) score but not of postoperative PANQOL total score (p = 0.37).Although FGA and QOL data significantly correlated in the preoperative setting, our results did not suggest that preoperative FGA can be used to determine postoperative QOL. Additionally, the lack of correlation between FGA and QOL measurements in the acute postoperative setting suggests that further research is needed to determine contributors to postoperative QOL.
View details for DOI 10.1097/MAO.0000000000003137
View details for Web of Science ID 000672957100042
View details for PubMedID 33741817
Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study.
JMIR medical informatics
2021; 9 (5): e23586
Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic's electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.Accurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.
View details for DOI 10.2196/23586
View details for PubMedID 34032581
View details for PubMedCentralID PMC8188315