Journal of Bioanalysis & Biomedicine

The advent of precision medicine has revolutionized oncology by promising tailored therapeutic strategies based on individual patient characteristics. Central to this advancement is the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics— providing a comprehensive understanding of cancer's molecular underpinnings. This study explores the integration of machine learning algorithms for predictive modeling of drug response in cancer patients using a multi-omics approach. By leveraging advanced computational techniques and vast multi-omics datasets, the research aims to enhance the accuracy and efficacy of predicting patient-specific responses to cancer treatments, thereby facilitating personalized medicine. Key challenges such as cancer heterogeneity, high dimensionality of data, and integration of disparate data types are addressed using multi-view learning, data integration frameworks, and feature fusion strategies. Explainable AI methods are employed to interpret the models and uncover potential biomarkers and therapeutic targets. The ultimate goal is to develop a predictive modeling framework for clinical use, guiding treatment decisions and improving patient outcomes.

Inflammatory Bowel Disease (IBD) encompasses chronic inflammatory conditions of the gastrointestinal tract, including Crohn's disease and ulcerative colitis. Recent studies suggest that the gut microbiome plays a crucial role in the pathogenesis of IBD. This study aims to explore the gut microbiome signature in IBD patients through metagenomic analysis and functional profiling. Using high-throughput sequencing, we characterized the microbial communities and identified significant alterations in the microbiome of IBD patients compared to healthy controls. Functional profiling revealed key metabolic pathways and microbial functions associated with disease states. These findings provide insights into the microbial contributions to IBD and potential targets for therapeutic intervention.

The outbreak of Zika virus has posed significant global health challenges, necessitating the development of rapid and accurate diagnostic tools. This study presents the development of a point-of-care diagnostic test for Zika virus infection utilizing CRISPR-Cas12a technology. Leveraging the specific and robust targeting capability of CRISPR-Cas12a, we designed a diagnostic platform capable of detecting ZIKV with high sensitivity and specificity. The assay demonstrated a limit of detection comparable to existing laboratory-based methods but with the advantage of faster results and minimal equipment requirements. This CRISPR-Cas12a-based diagnostic test offers a promising solution for timely and effective ZIKV detection, especially in resource-limited settings.

Prostate cancer remains one of the leading causes of cancer-related deaths among men, necessitating the development of more precise diagnostic tools. This study aimed to identify and characterize novel biomarkers for prostate cancer diagnosis using mass spectrometry-based proteomics. Serum samples from prostate cancer patients and healthy controls were analyzed, leading to the identification of several proteins differentially expressed between the two groups. These findings were validated using targeted proteomics, highlighting their potential utility in clinical diagnostics. Our results demonstrate the effectiveness of mass spectrometry-based proteomics in uncovering novel biomarkers, offering promise for improving prostate cancer diagnosis.