Rare diseases affect fewer than 200,000 individuals in the United States, with some being so rare that only a handful of people are impacted. According to the U.S. Food and Drug Administration (FDA), there are 1,268 approved orphan drugs available for treating these conditions. However, potentially beneficial drugs can also have side effects. Some adverse events, while serious, may be rare, making them difficult to identify or quantify in randomized controlled trials. Understanding these events is critical for improving patient safety and treatment outcomes. To better assess these risks, we aimed at summarizing adverse drug events for rare diseases by utilizing FDA Adverse Event Reporting System (FAERS). This study offers a foundation for future research of improving drug safety in rare diseases.

Over 300 million people are suffering from rare diseases, most of which have limited treatment options. Therefore, discovering new treatments for rare diseases is imperative. Drug repurposing, which identifies new uses for approved drugs, is considered one of the viable and risk-managed strategies for disease treatments. To promote the drug repurposing process, we introduced a prediction model to uncover novel relationships between gene targets and chemical compounds. In our previous study, we identified enriched genes for compounds from the Toxicology in the 21st Century program (Tox21) 10K library, to extend that study for enriched gene target prediction, we developed machine learning (ML) models including Support Vector Machine; K-Nearest Neighbors; Random Forest; and extreme gradient boosting (XGBoost), by using Tox21 bioassay screening data. All four models perform well with f1_score over 0.7, and XGBoost has the best performance with four different multi-label prediction embedding algorithms, including Binary Relevance; Label Powerset; Classifier Chain; Multi-Output Classifier. Our study explored a reliable method to predict potential gene targets from in vitro activity profile data toward drug repurposing.

With the advances in science and technology, the number of research in rare diseases has dramatically increased over the past twenty years. Systematically accessing those research projects funded by NIH would allow us to assess the current status of research, and research gaps remain in this area. Consequently, new research might be inspired to bridge the gaps. We previously developed a knowledge graph to semantically represent NIH funded rare disease research projects by analyzing project titles. To expand the use of NIH funding data, in this study we extended the previous work in two folds, 1) we applied our self-developed NLP package named NormMap to identify rare disease related projects, 2) we semantically annotated project titles and abstracts with biomedical concepts in UMLS to illustrate the project aims. With such rich information extracted from NIH funding data via semantic annotation, an updated version of the knowledge graph will be developed to advance rare disease research as the next step.

Given the nature of rare diseases, lack of data and standards impedes research in rare diseases. A method to improve data interoperability is necessary to allow data reuse, integration, and exchange in rare disease. A computational package named NormMap was developed to identify rare disease related data from various types of resources in free text via semantic annotation with rare disease terms from NCATS Genetic and Rare Diseases (GARD). In this preliminary study, four different sources which include NIH funded projects, clinical trials, PubMed articles, and Reddit subreddits, were applied to generate rare disease profiles by extending and exploring NormMap. Those profiles would offer a complete view of rare diseases from different aspects, funding agencies, patient groups, scientific research, to ultimately advance rare disease research, which is demonstrated in our case study.

Despite the individually low prevalence of rare diseases, they collectively constitute a big challenge to human health. Accurate annotation of rare diseases in biomedical data through natural language processing (NLP) could be instrumental in biomedical informatics research. Herein, we propose a data normalization-based annotation approach, complementary to the popular biomedical data annotation tool named MetaMap, for disease annotation from biomedical data in free text.

Rare diseases are naturally associated with low prevalence rate, which raises a big challenge due to less data available for supporting preclinical and clinical studies. Therefore, it is critical to fully utilize the accumulated scientific publications in rare diseases over years, in order to access full spectrum of scientific research and enable relevant scientific evidence extraction and generation. In this study, we obtained rare disease related PubMed articles, extracted multiple types of biomedical information, and semantically presented the data in a knowledge graph, which is hosted in Neo4j based on a predefined data model to support further rare disease research.

There are approximately 7,000 rare diseases, and 25-30 million people affected with a rare disease in the United States. Prevalence rate for rare diseases is relatively low compared to common diseases. Thus, disease rarity leads to a lack of clinical familiarity that often impedes accurate and timely diagnosis for many rare disease patients. Social media has become an important resource/tool for discussing, sharing, and seeking information relevant to rare diseases by patients and families. In this study, we aimed to analyze rare disease-related posts from Reddit, one popular social media platform to reveal rare disease patients' needs based on hidden topics to be identified. We implemented NLP/topic modelling to identify main topics from the posts and consequently computed TF-IDF to detect the most prevalent phrases as sub-topics from the posts. As a proof of concept, we primarily focused on Cystic Fibrosis as a case study to demonstrate the use of data from Reddit for rare disease research.