{"type": "root", "children": [{"type": "p", "children": [{"type": "t", "text": "\n The four abstracts provided do not offer any direct experimental or functional characterization of the gene KRTAP4‐6. In several large‐scale omics studies—one mapping co‐regulated transcriptional signatures (abstract"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "1"}]}, {"type": "t", "text": ", another delineating the proteomic landscape of cilia"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "2"}]}, {"type": "t", "text": ", a third performing exome analysis of carotid body tumors"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "3"}]}, {"type": "t", "text": ", and a fourth applying neural network methods to identify drug‐sensitivity genes"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "4"}]}, {"type": "t", "text": "—KRTAP4‐6 was not explicitly discussed or investigated for its molecular function. "}]}, {"type": "t", "text": "\n "}, {"type": "p", "children": [{"type": "t", "text": "\n Generally, keratin-associated proteins such as those from the KRTAP4 family are known in other contexts to play roles in the structural organization of hair and related keratinized tissues. However, without targeted analysis or functional enrichment evidence from the studies cited here, its specific role or involvement in the biological processes under investigation (for example, transcriptional regulation in cancer cells, ciliary assembly, tumorigenesis, or drug response) remains undetermined.\n "}]}, {"type": "t", "text": "\n "}, {"type": "p", "children": [{"type": "t", "text": "\n In summary, while these abstracts illustrate powerful high-throughput approaches for mapping gene co-regulation, protein interaction networks, tumor genomics, and biomarker discovery, they do not provide sufficient data to define the function of KRTAP4‐6.\n "}]}, {"type": "rg", "children": [{"type": "r", "ref": 1, "children": [{"type": "t", "text": "Fabrice Lopez, Julien Textoris, Aurélie Bergon, et al. "}, {"type": "b", "children": [{"type": "t", "text": "TranscriptomeBrowser: a powerful and flexible toolbox to explore productively the transcriptional landscape of the Gene Expression Omnibus database."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "PLoS One (2008)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1371/journal.pone.0004001"}], "href": "https://doi.org/10.1371/journal.pone.0004001"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "19104654"}], "href": "https://pubmed.ncbi.nlm.nih.gov/19104654"}]}, {"type": "r", "ref": 2, "children": [{"type": "t", "text": "Karsten Boldt, Jeroen van Reeuwijk, Qianhao Lu, et al. "}, {"type": "b", "children": [{"type": "t", "text": "An organelle-specific protein landscape identifies novel diseases and molecular mechanisms."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Nat Commun (2016)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1038/ncomms11491"}], "href": "https://doi.org/10.1038/ncomms11491"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "27173435"}], "href": "https://pubmed.ncbi.nlm.nih.gov/27173435"}]}, {"type": "r", "ref": 3, "children": [{"type": "t", "text": "Anastasiya V Snezhkina, Elena N Lukyanova, Dmitry V Kalinin, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Exome analysis of carotid body tumor."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "BMC Med Genomics (2018)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s12920-018-0327-0"}], "href": "https://doi.org/10.1186/s12920-018-0327-0"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "29504908"}], "href": "https://pubmed.ncbi.nlm.nih.gov/29504908"}]}, {"type": "r", "ref": 4, "children": [{"type": "t", "text": "Faming Liang, Qizhai Li, Lei Zhou "}, {"type": "b", "children": [{"type": "t", "text": "Bayesian Neural Networks for Selection of Drug Sensitive Genes."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "J Am Stat Assoc (2018)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1080/01621459.2017.1409122"}], "href": "https://doi.org/10.1080/01621459.2017.1409122"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "31354179"}], "href": "https://pubmed.ncbi.nlm.nih.gov/31354179"}]}]}]}