[P33]: Six1 is a key regulator of multiple cell type differentiation in olfactory epithelium

2006 ◽  
Vol 24 (8) ◽  
pp. 510-510
Author(s):  
K. Ikeda ◽  
Z.‐Ic. Ando ◽  
R. Kageyama ◽  
K. Kawakami
Keyword(s):  
Olfactory Epithelium ◽  
Cell Type ◽  
Multiple Cell ◽  
Multiple Cell Type

scholarly journals Incorporation of resident macrophages in engineered tissues: Multiple cell type response to microenvironment controlled macrophage‐laden gelatine hydrogels

2017 ◽  
Vol 12 (2) ◽  
pp. 330-340 ◽  
Author(s):  
Camille Dollinger ◽  
Sait Ciftci ◽  
Helena Knopf‐Marques ◽  
Rabia Guner ◽  
Amir M. Ghaemmaghami ◽  
...  
Keyword(s):  
Cell Type ◽  
Engineered Tissues ◽  
Multiple Cell ◽  
Type Response ◽  
Multiple Cell Type

scholarly journals accuEnhancer: Accurate enhancer prediction by integration of multiple cell type data with deep learning

2020 ◽  
Author(s):  
Yi-An Tung ◽  
Wen-Tse Yang ◽  
Tsung-Ting Hsieh ◽  
Yu-Chuan Chang ◽  
June-Tai Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cell Types ◽  
Regulatory Elements ◽  
Sequence Motifs ◽  
Cell Type ◽  
Enhancer Activity ◽  
Multiple Cell ◽  
Type Data ◽  
Different Cell Types ◽  
Multiple Cell Type

AbstractEnhancers are one class of the regulatory elements that have been shown to act as key components to assist promoters in modulating the gene expression in living cells. At present, the number of enhancers as well as their activities in different cell types are still largely unclear. Previous studies have shown that enhancer activities are associated with various functional data, such as histone modifications, sequence motifs, and chromatin accessibilities. In this study, we utilized DNase data to build a deep learning model for predicting the H3K27ac peaks as the active enhancers in a target cell type. We propose joint training of multiple cell types to boost the model performance in predicting the enhancer activities of an unstudied cell type. The results demonstrated that by incorporating more datasets across different cell types, the complex regulatory patterns could be captured by deep learning models and the prediction accuracy can be largely improved. The analyses conducted in this study demonstrated that the cell type-specific enhancer activity can be predicted by joint learning of multiple cell type data using only DNase data and the primitive sequences as the input features. This reveals the importance of cross-cell type learning, and the constructed model can be applied to investigate potential active enhancers of a novel cell type which does not have the H3K27ac modification data yet.AvailabilityThe accuEnhancer package can be freely accessed at: https://github.com/callsobing/accuEnhancer

Fabrication of perfusable 3D hepatic lobule-like constructs through assembly of multiple cell type laden hydrogel microstructures

2018 ◽  
Vol 11 (1) ◽  
pp. 015016 ◽  
Author(s):  
Juan Cui ◽  
Huaping Wang ◽  
Zhiqiang Zheng ◽  
Qing Shi ◽  
Tao Sun ◽  
...  
Keyword(s):  
Cell Type ◽  
Hepatic Lobule ◽  
Multiple Cell ◽  
Multiple Cell Type

Multiple cell-type-specific elements regulate Myc protein stability

Oncogene ◽  
2004 ◽  
Vol 23 (21) ◽  
pp. 3863-3871 ◽  
Author(s):  
Andreas Herbst ◽  
Simone E Salghetti ◽  
So Young Kim ◽  
William P Tansey
Keyword(s):  
Protein Stability ◽  
Cell Type ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Multiple Cell Type

scholarly journals Erratum: Multiple cell-type-specific elements regulate Myc protein stability

Oncogene ◽  
2004 ◽  
Vol 23 (58) ◽  
pp. 9448-9448 ◽  
Author(s):  
Andreas Herbst ◽  
Simone E Salghetti ◽  
So Young Kim ◽  
William P Tansey
Keyword(s):  
Protein Stability ◽  
Cell Type ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Multiple Cell Type
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