The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

We consider the problem of identifying regions within a pan-genome De Bruijn graph that are traversed by many sequence paths. We define such regions and the subpaths that traverse them as frequented regions (FRs). In this work, we formalize the FR problem and describe an efficient algorithm for finding FRs. Subsequently, we propose some applications of FRs based on machine-learning and pan-genome graph simplification. We demonstrate the effectiveness of these applications using data sets for the organisms Staphylococcus aureus (bacterium) and Saccharomyces cerevisiae (yeast). We corroborate the biological relevance of FRs such as identifying introgressions in yeast that aid in alcohol tolerance, and show that FRs are useful for classification of yeast strains by industrial use and visualizing pan-genomic space.

Automated short-answer grading (ASAG) is a cru-cial element of any intelligent tutoring platform. Machine Learning (ML) has shown great promise for ASAG. However, this task remains challenging even for Deep Learning (DL) approaches and Large Language Models (LLMs), requiring semantic inference and textual entailment recognition. The SemEval-2013 Task 7, The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge, is a benchmark widely used for research on ASAG. The SciEntsBank data included in this collection contains nearly 11,000 answers to 197 assessment questions in 15 different science domains. Despite the popularity, only a few researchers have explored the potential of DL or LLMs for this task. In this project, we explore the effectiveness of the RoBERTa Large model, an LLM trained on an extensive text corpus for language comprehension. By fine-tuning the model on the Multi-Genre Natural Language Inference (MNLI) corpus for semantic inference and subsequently on the SciEntsBank dataset, with a focus on the 3-way labels of correct, incorrect, and contradictory, we achieved a weighted Fl-score of 0.77, 0.72, and 0.72 on unseen answers, questions, and domains, respectively. Notably, our model significantly benefits from fine-tuning on the MNLI corpus, particularly in enhancing its performance on the contradictory class (which constitutes only 10% of the dataset) through transfer learning leading to significant improvements on the more challenging test sets: unseen questions and unseen domains.

This study provides a snapshot of the current vaccine business ecosystem, including practices, challenges, beliefs, and expectations of vaccine providers. Our team focused on providers’ firsthand experience with administering vaccines to determine if an oral vaccine (e.g. pill or oral-drop) would be well-received. We interviewed 135 healthcare providers and vaccine specialists across the US, focusing questions on routine vaccinations, not COVID-19 vaccines. Improving workflow efficiency is a top concern among vaccine providers due to shrinking reimbursement rates—determined by pharmacy benefit managers (PBMs)—and the time-intensiveness of injectable vaccines. Administering injectable vaccines takes 23 minutes/patient on average, while dispensing pills takes only 5 minutes/patient. An average of 24% of patients express needle-fear, which further lengthens the processing time. Misaligned incentives between providers and PBMs could reduce the quality and availability of vaccine-related care. The unavailability of single-dose orders prevents some rural providers from offering certain vaccines. Most interviewees (74%) believe an oral vaccine would improve patient–provider experience, patient-compliance, and workflow efficiency, while detractors (26%) worry about the taste, vaccine absorption, and efficacy. Additional research could investigate whether currently non-vaccinating pharmacies would be willing to offer oral vaccines, and the impact of oral vaccines on vaccine acceptance.

The biomedical literature provides an extensive source of information in the form of unstructured text. One of the most important types of information hidden in biomedical literature is the relationships between human proteins and their phenotypes, which, due to the exponential growth of publications, can remain hidden. This provides a range of opportunities for the development of computational methods to extract the biomedical relationships from the unstructured text. In our previous work, we developed a supervised machine learning approach, called PPPred, for classifying the validity of a given sentence-level human protein-phenotype co-mention. In this work, we propose DeepPPPred, an ensemble classifier composed of PPPred and three deep neural network models: RNN, CNN, and BERT. Using an expanded gold-standard co-mention dataset, we demonstrate that the proposed ensemble method significantly outperforms its constituent components and provides a new state-of-the-art performance on classifying the co-mentions of human proteins and phenotype terms.