From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer

Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios. A challenging scenarios for artificial intelligence (AI) is using biomedical data (e.g., texts, imaging, omics, and clinical) to provide diagnosis and treatment recommendations for cancerous conditions.~Data and knowledge about biomedical entities like cancer, drugs, genes, proteins, and their mechanism is spread across structured (knowledge bases (KBs)) and unstructured (e.g., scientific articles) sources. A large-scale knowledge graph (KG) can be constructed by integrating and extracting facts about semantically interrelated entities and relations. Such a KG not only allows exploration and question answering (QA) but also enables domain experts to deduce new knowledge. However, exploring and querying large-scale KGs is tedious for non-domain users due to their lack of understanding of the data assets and semantic technologies. In this paper, we develop a domain KG to leverage cancer-specific biomarker discovery and interactive QA. For this, we constructed a domain ontology called OncoNet Ontology (ONO), which enables semantic reasoning for validating gene-disease (different types of cancer) relations. The KG is further enriched by harmonizing the ONO, metadata, controlled vocabularies, and biomedical concepts from scientific articles by employing BioBERT- and SciBERT-based information extractors. Further, since the biomedical domain is evolving, where new findings often replace old ones, without having access to up-to-date scientific findings, there is a high chance an AI system exhibits concept drift while providing diagnosis and treatment. Therefore, we fine-tune the KG using large language models (LLMs) based on more recent articles and KBs.Comment: arXiv admin note: substantial text overlap with arXiv:2302.0473

Explainable AI for Bioinformatics: Methods, Tools, and Applications

Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness

Evaluation of thrombophilia testing in the inpatient setting: A single institution retrospective review

BACKGROUND: Thrombophilia workup is typically inappropriate in the inpatient setting as testing may be skewed by anticoagulation, acute thrombosis, or acute illness. OBJECTIVE: To determine adherence of inpatient thrombophilia testing with institutional guidelines. PATIENTS AND METHODS: A retrospective study to evaluate thrombophilia testing practices of adult patients who were admitted to Lehigh Valley Hospital at Cedar Crest with either venous thromboembolism or ischemic stroke in 2019. Testing included inherited and acquired thrombophilia. Patient charts were individually reviewed for three measured outcomes: 1) the number of appropriate thrombophilia testing in the inpatient setting; 2) the indications used for thrombophilia testing; 3) the proportion of positive thrombophilia tests with change in clinical management. RESULTS: 201 patients were included in our study. 26 patients (13%) were tested appropriately in accordance with institution guidelines and 175 (87%) patients were tested inappropriately. The most common reason for the inappropriate testing was testing during acute thrombosis. 28 of the 201 patients had positive thrombophilia tests, but the reviewers only noted 7 patients with change in clinical management-involving anticoagulation change. CONCLUSION: Our study revealed that a majority of inpatient thrombophilia testing did not follow institutional guidelines for appropriate testing and did not change patient management. These thrombophilia tests are often overutilized and have minimal clinical utility in the inpatient setting

Community-Based Population Health Research: A Report from the Field

This Forum, “Community-Based Population Health Research: A Report from the Field” highlights the work of 1889 Jefferson Center for Population Health and Mainline Center for Population Health Research. Leaders from both research centers provide an overview of the history and purpose of the centers and describe accomplishments and current initiatives. Objectives: Describe two innovative models for population health research centers List three benefits of partnering with a University when establishing a population health center Characterize challenges associated with the development of community-engaged and health system embedded, population health research centers Presentation: 49:1

Neurological Symptoms in Patients with Biopsy Proven Celiac Disease

Abstract: In celiac disease (CD), the gut is the typical manifestation site but atypical neurological presentations are thought to occur in 6 to 10% with cerebellar ataxia being the most frequent symptom. Most studies in this field are focused on patients under primary neurological care. To exclude such an observation bias, patients with biopsy proven celiac disease were screened for neurological disease. A total of 72 patients with biopsy proven celiac disease (CD) (mean age 51 6 15 years, mean disease duration 8 6 11 years) were recruited through advertisements. All participants adhered to a gluten-free diet. Patients were interviewed following a standard questionnaire and examined clinically for neurological symptoms. Medical history revealed neurological disorders such as migraine (28%), carpal tunnel syndrome (20%), vestibular dysfunction (8%), seizures (6%), and myelitis (3%). Interestingly, 35% of patients with CD reported of a history of psychiatric disease including depression, personality changes, or even psychosis. Physical examination yielded stance and gait problems in about one third of patients that could be attributed to afferent ataxia in 26%, vestibular dysfunction in 6%, and cerebellar ataxia in 6%. Other motor features such as basal ganglia symptoms, pyramidal tract signs, tics, and myoclonus were infrequent. 35% of patients with CD showed deep sensory loss and reduced ankle reflexes in 14%. Gait disturbances in CD do not only result from cerebellar ataxia but also from proprioceptive or vestibular impairment. Neurological problems may even develop despite strict adherence to a gluten-free diet. 2009 Movement Disorder Societ