Transcriptomics and epigenetic data integration learning module on Google Cloud

Precision Targeting in Metastatic Prostate Cancer: Molecular Insights to Therapeutic Frontiers

Metastatic prostate cancer (mPCa) remains a significant cause of cancer-related mortality in men. Advances in molecular profiling have demonstrated that the androgen receptor (AR) axis, DNA damage repair pathways, and the PI3K/AKT/mTOR pathway are critical drivers of disease progression and therapeutic resistance. Despite the established benefits of hormone therapy, chemotherapy, and bone-targeting agents, mPCa commonly becomes treatment-resistant. Recent breakthroughs have highlighted the importance of identifying actionable genetic alterations, such as BRCA2 or ATM defects, that render tumors sensitive to poly-ADP ribose polymerase (PARP) inhibitors. Parallel efforts have refined imaging-particularly prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography-to detect and localize metastatic lesions with high sensitivity, thereby guiding patient selection for PSMA-targeted radioligand therapies. Multi-omics innovations, including liquid biopsy technologies, enable the real-time tracking of emergent AR splice variants or reversion mutations, supporting adaptive therapy paradigms. Nonetheless, the complexity of mPCa necessitates combination strategies, such as pairing AR inhibition with PI3K/AKT blockade or PARP inhibitors, to inhibit tumor plasticity. Immuno-oncological approaches remain challenging for unselected patients; however, subsets with mismatch repair deficiency or neuroendocrine phenotypes may benefit from immune checkpoint blockade or targeted epigenetic interventions. We present these pivotal advances, and discuss how biomarker-guided integrative treatments can improve mPCa management.

Large Language Models in Genomics-A Perspective on Personalized Medicine

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and efficiency of extensive genomic analyses through contextualization. LLMs have made significant advancements in their ability to understand complex genetic terminology and accurately predict medical outcomes. These capabilities allow for a more thorough understanding of genetic influences on health issues and the creation of more effective therapies. This review emphasizes LLMs' significant impact on healthcare, evaluates their triumphs and limitations in genomic data processing, and makes recommendations for addressing these limitations in order to enhance the healthcare system. It explores the latest advancements in LLMs for genomic analysis, focusing on enhancing disease diagnosis and treatment accuracy by taking into account an individual's genetic composition. It also anticipates a future in which AI-driven genomic analysis is commonplace in clinical practice, suggesting potential research areas. To effectively leverage LLMs' potential in personalized medicine, it is vital to actively support innovation across multiple sectors, ensuring that AI developments directly contribute to healthcare solutions tailored to individual patients.

Overview and Prospects of DNA Sequence Visualization

Due to advances in big data technology, deep learning, and knowledge engineering, biological sequence visualization has been extensively explored. In the post-genome era, biological sequence visualization enables the visual representation of both structured and unstructured biological sequence data. However, a universal visualization method for all types of sequences has not been reported. Biological sequence data are rapidly expanding exponentially and the acquisition, extraction, fusion, and inference of knowledge from biological sequences are critical supporting technologies for visualization research. These areas are important and require in-depth exploration. This paper elaborates on a comprehensive overview of visualization methods for DNA sequences from four different perspectives-two-dimensional, three-dimensional, four-dimensional, and dynamic visualization approaches-and discusses the strengths and limitations of each method in detail. Furthermore, this paper proposes two potential future research directions for biological sequence visualization in response to the challenges of inefficient graphical feature extraction and knowledge association network generation in existing methods. The first direction is the construction of knowledge graphs for biological sequence big data, and the second direction is the cross-modal visualization of biological sequences using machine learning methods. This review is anticipated to provide valuable insights and contributions to computational biology, bioinformatics, genomic computing, genetic breeding, evolutionary analysis, and other related disciplines in the fields of biology, medicine, chemistry, statistics, and computing. It has an important reference value in biological sequence recommendation systems and knowledge question answering systems.

NIGMS Sandbox: a learning platform toward democratizing cloud computing for biomedical research

Biomedical data are growing exponentially in both volume and levels of complexity, due to the rapid advancement of technologies and research methodologies. Analyzing these large datasets, referred to collectively as "big data," has become an integral component of research that guides experimentation-driven discovery and a new engine of discovery itself as it uncovers previously unknown connections through mining of existing data. To fully realize the potential of big data, biomedical researchers need access to high-performance-computing (HPC) resources. However, supporting on-premises infrastructure that keeps up with these consistently expanding research needs presents persistent financial and staffing challenges, even for well-resourced institutions. For other institutions, including primarily undergraduate institutions and minority serving institutions, that educate a large portion of the future workforce in the USA, this challenge presents an insurmountable barrier. Therefore, new approaches are needed to provide broad and equitable access to HPC resources to biomedical researchers and students who will advance biomedical research in the future.