Deep learning tools and applications in single-cell RNA sequencing

• Published in: AIP conference proceedings Vol. 2971; no. 1
• Main Authors: Rajesh, Mothe, Martha, Sheshikala
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 05.06.2024
• Subjects: Data processing; Deep learning; Gene sequencing; Machine learning; Regeneration (physiology); Ribonucleic acid; RNA; Soft tissues; Stem cells; Subgroups; Tissue engineering
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0195717

When used in treatments, stem cells have the potential to heal many previously incurable illnesses. Existing stem cell application techniques, however, are insufficient since these cells are used directly independent of growing medium or subgroup. Researchers, for example, do not consider the source, culture method, application angle, or function of mesenchymal stem cells (MSCs) when using them in cell therapy (soft tissue regeneration, hard tissue regeneration, suppression of immune function, or promotion of immune function). By combining machine learning methods (such as deep learning) with data sets obtained through single-cell RNA sequencing (scRNA-seq), we can discover the hidden structure of these cells, predict their effects more accurately, and effectively use subpopulations with differentiation potential for stem cell research. ScRNA-seq technique has revolutionised transcription research because it can express single-cell genes with single-cell anatomical accuracy. This powerful technology, however, is subject to biological and technical noise, making data processing computationally difficult.