JMIR Bioinformatics and Biotechnology

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.

As an important medical device to ensure the smooth breathing and life-saving of critically ill patients, the tracheal tube causes damage to the oral mucosa of patients during use, which not only increases the pain of patients but also increases the risk of infection.

Patient-derived cancer models (PDCMs) have become essential tools in cancer research and preclinical studies. Consequently, the number of publications on PDCMs has increased significantly over the past decade. Advances in Artificial Intelligence (AI), particularly in Large Language Models (LLMs), offer promising solutions for extracting knowledge from scientific literature at scale.

Biobank privacy policies strip patient identifiers from donated specimens, undermining transparency, utility, and value for patients, scientists, and society. We are advancing decentralized biobanking apps that reconnect patients with biospecimens and facilitate engagement through a privacy-preserving nonfungible token (NFT) digital twin framework. The decentralized biobanking platform was first piloted for breast cancer biobank members.

The number of survivors of cancer is growing, and they often experience negative long-term behavioral outcomes due to cancer treatments. There is a need for better computational methods to handle and predict these outcomes so that physicians and health care providers can implement preventive treatments.

Multiple Correspondence Analysis (MCA) is an unsupervised data science methodology that aims to identify and represent associations between categorical variables. Gliomas are an aggressive type of cancer characterized by diverse molecular and clinical features that serve as key prognostic factors. Thus, advanced computational approaches are essential to enhance analysis and interpretation of the associations between clinical and molecular features in gliomas.

Health care students often experience high levels of stress, anxiety, and mental health issues, making it crucial to address these challenges. Variations in stress levels may be associated with changes in dehydroepiandrosterone sulfate (DHEA-S) and interleukin-6 (IL-6) levels and gene expression. Meditative practices have demonstrated effectiveness in reducing stress and improving mental well-being.

Environmentally sensitive pathogens exhibit ecological and evolutionary responses to climate change that result in the emergence and global expansion of well-adapted variants. It is imperative to understand the mechanisms that facilitate pathogen emergence and expansion, as well as the drivers behind the mechanisms, to understand and prepare for future pandemic expansions.

An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions.