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In the wave of science and technology in the new era, the research and application of deep learning biological information algorithms are bringing revolutionary breakthroughs to the medical field. Recently, researchers have used deep learning techniques by combining the mechanisms of long non-coding Rnas (lncrnas) in neurogenesis to ensure that they work together to ensure normal development and functional maintenance of the nervous system. Non-coding RNA (ncRNA) constitutes a higher proportion of nucleic acids compared to coding RNA. Unlike protein-coding RNA, ncRNA is transcribed from DNA but does not undergo translation into proteins; instead, it functions as a functional RNA molecule.In recent years, research on ncRNA has deepened, and the development of ncRNA recognition algorithms has aided in understanding the functions of ncRNA, gradually revealing its growing importance in regulatory processes. An increasing number of ncRNAs have established their significant roles in the fields of disease, reprogramming, and differentiation. In disease research, researchers have developed databases and computational methods based on these databases to predict the relationships between diseases and related ncRNAs. In the field of reprogramming and differentiation, miRNAs, a well-studied subgroup of ncRNAs, have shown considerable progress. MicroRNAs can act on transcription factors (TFs) and other regulatory factors involved in reprogramming, thereby enhancing reprogramming efficiency. From a developmental biology perspective, certain miRNAs, such as miRNA-124 or let-7a, can interact with their target genes to regulate neural cell development. Recent studies have increasingly focused on the role of long non-coding RNAs (LncRNAs) in the fields of cellular reprogramming and developmental biology. Researchers have discovered that specific LncRNAs promote neuron differentiation, influence the expression of neighboring genes, and serve as key modulators of signaling pathways. Additionally, LncRNAs and miRNAs can jointly regulate neurogenesis. Understanding these mechanisms and research advancements may contribute to the future development of ncRNAs in the fields of disease, reprogramming, and developmental biology.The application of deep learning bioinformation algorithm in lncRNA research has brought new hope for neuroscience and clinical medicine. In the future, with the continuous progress of technology and in-depth research, lncRNA is expected to play an important role in more disease fields and make greater contributions to the cause of human health.
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