Here, we speak to Fan Zhang, Assistant Professor at the University of Colorado, about the applications of computational biology in studying disease behaviour.

Unlocking the potential for FFPE samples in advancing single-cell sequencing for tumour microenvironment research.

Deep learning and AI are enhancing synthetic biology's role in drug design, emphasising the necessity of quality data in constructing models for approaches such as genome mining and data deconvolution.

Our ‘Navigating the Regulatory Landscape’ eBook has been designed to provide you with invaluable insights into the intricacies of regulations & policies surrounding the challenges and opportunities of Real-World Data, genome editing, biomedical informatics, manufacturing and many more.

Machine learning methods can be hugely important in aiding the interpretation and integration of data generated through single-cell omics analysis, assisting in disease detection and modelling.

Spatial transcriptomics leverages omics techniques to extract transcriptome data while retaining the context of the original tissue, aiding disease research, biomarker development, and understandings of tissue structures and cellular heterogeneity.

Recent studies have used multiplex blood biomarker information from patients to distinguish individual patient subgroups for neurodegenerative diseases, helping to stratify patients by their pathology.