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These limitations however can each be addressed as functional pathways become better defined and understanding of gene function improves, through linking genomic and gene expression and/or proteomic data.
Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes.
While this detailed annotation is critical for communicating our understanding of gene function, it also provides a gold standard for quantifying the functional information derived from large-scale studies, which may vary in quality and breadth.
The advent of transgenic and gene replacement technologies has revolutionized the understanding of gene function and regulation.
The past decade has seen the rapid evolution of small-molecule gene-silencing strategies, driven largely by enhanced understanding of gene function in the pathogenesis of disease.
By sharing cases worldwide, clusters of rare cases having phenotype and structural rearrangement in common can be identified, leading to the delineation of new syndromes and furthering understanding of gene function.
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Genetically engineered mice have greatly improved our understanding of gene functions and disease mechanisms.
Co-expression analysis using Pearson correlations has been previously shown to be a useful technique to facilitate understanding of gene functions [64], [65], [77].
PaGeFinder is particularly useful for dynamic and global understanding of gene functions under serial spatiotemporal conditions.
Deciphering these differences is essential to precise understanding of gene functions and their relevance to phenotypes and diseases (5 8).
The skellam model provides a useful tool for clustering gene expression data by RNA-seq, thenhancingancing our understanding of gene functions and networks.
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