JMIR Bioinformatics and Biotechnology

Sensitivity—expressed as percent positive agreement (PPA) with a reference assay—is a primary metric for evaluating lateral-flow antigen tests (ATs), typically benchmarked against a quantitative reverse transcription polymerase chain reaction (qRT-PCR). In SARS-CoV-2 diagnostics, ATs detect nucleocapsid protein, whereas qRT-PCR detects viral RNA copy numbers. Because observed PPA depends on the underlying viral-load distribution (proxied by the number cycle thresholds or Cts, which is inversely related to load), study-specific sampling can bias sensitivity estimates. Cohort differences—such as enrichment for high- or low-Ct specimens—therefore complicate cross-test comparisons, and real-world datasets often deviate from regulatory guidance to sample across the full concentration range. Although logistic models relating test positivity to Ct are well described, they are seldom used to re-weight results to a standardized reference viral-load distribution. As a result, reported sensitivities remain difficult to compare across studies, limiting both accuracy and generalizability

Integrating clinical, genomic, and social determinants of health (SDoH) data is essential for advancing precision medicine and addressing cancer health disparities. However, existing bioinformatics tools often lack the flexibility to perform equity-driven analyses or require significant programming expertise.

Ninety percent of the 65,000 human diseases are infrequent, collectively affecting ~ 400 million peo-ple, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of over 100 subjects per group to achieve clinical accuracy while managing high-dimensional input (~25,000 transcripts). These requirements are infeasible for micro-cohorts of ~20 individuals, where overfitting becomes pervasive.

The systemic treatment of cancer typically requires the use of multiple anticancer agents in combination and/or sequentially. Clinical narrative texts often contain extensive descriptions of the temporal sequencing of systemic anticancer therapy (SACT), setting up an important task that may be amenable to automated extraction of SACT timelines.

Deep learning (DL) shows promise for automated lung cancer diagnosis, but limited clinical data restricts performance. While data augmentation (DA) helps, existing methods struggle with chest computed tomography (CT) scans across diverse DL architectures.

The protein A Disintegrin and Metalloprotease Domain-Containing 17 (ADAM-17), also called TACE, generally plays an important role in the cleavage of the Pro-Leu-Ala-Gln-Ala-/-Val-Arg-Ser-Ser-Ser sequence in the membrane-bound precursor of tumor necrosis factor alpha (TNF-α). This cleavage process has significant implications for inflammatory and immune responses, and recent studies suggest a role for ADAM-17 variants in SARS-CoV-2 infection.

Cancer is one of the leading causes of disease burden globally, and early and accurate diagnosis is crucial for effective treatment. This study presents a deep learning-based model designed to classify five common types of cancer in Saudi Arabia: Breast, Colorectal, Thyroid, Non-Hodgkin Lymphoma (NHL), and Corpus Uteri.

National and Ethnic Mutation Frequency Databases (NEMDBs) play a crucial role in documenting gene variations across populations, offering invaluable insights for gene mutation research and the advancement of precision medicine. These databases provide an essential resource for understanding genetic diversity and its implications for health and disease across different ethnic groups.

Previous machine learning approaches for prostate cancer detection using gene expression data have shown remarkable classification accuracies. However, prior studies overlook the influence of racial diversity within the population and the importance of selecting outlier genes based on expression profiles.

Prediabetes is an intermediate stage between normal glucose metabolism and diabetes and is associated with increased risk of complications like cardiovascular disease and kidney failure.

Artificial intelligence (AI) is poised to become an integral component in healthcare research and delivery, promising to address complex challenges with unprecedented efficiency and precision. However, many clinicians lack training and experience with AI, and for those who wish to incorporate AI into research and practice, the path forward remains unclear. Technical barriers, institutional constraints, and lack of familiarity with computer and data science frequently stall progress. In this tutorial, we present a transparent account of our experiences as a newly established interdisciplinary team of clinical oncology researchers and data scientists working to develop a natural language processing (NLP) model to identify symptomatic adverse events during pediatric cancer therapy. We outline the key steps for clinicians to consider as they explore the utility of AI in their inquiry and practice, including building a digital laboratory, curating a large clinical dataset, and developing early-stage AI models. We emphasize the invaluable role of institutional support, including financial and logistical resources, and dedicated and innovative computer and data scientists as equal partners in the research team. Our account highlights both facilitators and barriers encountered spanning financial support, learning curves inherent with interdisciplinary collaboration, and constraints of time and personnel. Through this narrative tutorial, we intend to demystify the process of AI research and equip clinicians with actionable steps to initiate new ventures in oncology research. As AI continues to reshape the research and practice landscapes, sharing insights from past successes and challenges will be essential to informing a clear path forward.

Computational data generated from artificial intelligence (AI) and quantum computing will change the course of new drug discovery and approval by accelerating and optimizing the process of identifying potential drug candidates by creating computational data, predicting the efficacy of pharmaceuticals, and assessing their safety.