Citations

Abstract The advent of high-throughput sequencing has led to an exponential increase in genomic data, highlighting the need for efficient and accurate models to analyze and interpret this information. Here, we introduce a novel, exomic foundational model that leverages a combination of human reference genome and multispecies data to improve variant detection and interpretation. Our model utilizes a short- range transformer architecture and is trained on a large dataset of human exomic sequences derived from the Tapestry study. Through a series of ablation studies and scaling experiments, we demonstrate the effectiveness of our model in pre- dicting next token accuracy and identifying clinically pathogenic variants. We also show that our model outperforms existing models in a range of downstream tasks, including variant effect prediction and disease state identification. In fact, our largest STRAND variant (1B parameters) surpassed previous benchmarks, demonstrating a mean accuarcy of 0.880 (8.2% improvement over the original NT and 7% improvement over NT-v2). Furthermore, we construct a unique exomic ClinVar dataset to evaluate the model’s performance on pathogenicity and disease states. Our results highlight the potential of this model to improve our understanding of the human exome and its role in disease. The model and its applications have significant implications for genomic based diagnosis and personalized medicine including tailored therapeutic development.

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850–0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16–13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.

INTRODUCTION: Patients with inflammatory bowel disease (IBD) are at an increased risk of infections. Before the COVID-19 pandemic, respiratory syncytial virus (RSV) followed predictable seasonal patterns, which have been recently disrupted. This study aimed to investigate whether severe acute respiratory syndrome (SARS) coronavirus 2 (CoV-2) infection is associated with an increased risk of RSV infection in patients with IBD compared with those without a history of SARS-CoV-2 infection. METHODS: This retrospective cohort study used the TriNetX database to identify patients aged 18 years and older with IBD and SARS-CoV-2 infection (IBD-SARS-CoV-2 cohort) during the 2022 and 2023 RSV seasons. A 1:1 propensity score matching was used to compare patients with IBD but no history of SARS-CoV-2 infection (IBD non-SARS-CoV-2 cohort). RESULTS: In the 2022 IBD-SARS-CoV-2 cohort (mean age: 53.7 ± 17.6 years; 59% female; 77% White), the RSV infection risk was 0.47%, higher than 0.19% in the matched IBD non-SARS-CoV-2 cohort (adjusted odds ratio [aOR]: 2.4; 95% CI: 1.5–3.6). The risk was highest 30–60 days after SARS-CoV-2 infection (aOR: 2.9; 95% CI: 1.7–4.9), particularly in those aged 60 years and older (aOR: 2.5; 95% CI: 1.3–4.5). The use of systemic corticosteroids (aOR: 2.3; 95% CI: 1.1–4.6) or immune-modifying therapies (aOR: 3.9; 95% CI: 2–7.1) was associated with higher RSV infection risk. Similar trends were observed during the 2023 RSV season, with no significant differences in RSV-related hospitalizations. DISCUSSION: Adults with IBD have a higher risk of RSV infection after SARS-CoV-2 infection, particularly those receiving steroids or immune therapies. SARS-CoV-2 infection may have contributed to the recent RSV infection surge in this population, warranting further research.