David A. Taft
University of Pittsburgh
5 Papers
2 Citations
David A. Taft is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 4, co-authored 5 publications.
Chat about Author
Papers
Learn to segment single cells with deep distance estimator and deep cell detector.
Weikang Wang,David A. Taft,Yi-Jiun Chen,Jingyu Zhang,Callen T. Wallace,Min Xu,Simon C. Watkins,Jianhua Xing +7 more
TL;DR: A strategy that combines strengths of CNN and traditional watershed algorithm is developed that achieved significantly higher cell count accuracy than the pixel-wise classification algorithm did, with the latter performing poorly when separating connected cells, especially those connected by blurry boundaries.
81
•Posted Content
Learn to segment single cells with deep distance estimator and deep cell detector
Weikang Wang,David A. Taft,Yi-Jiun Chen,Jingyu Zhang,Callen T. Wallace,Min Xu,Simon C. Watkins,Jianhua Xing +7 more
TL;DR: Wang et al. as mentioned in this paper proposed a different learning strategy that combines strengths of CNN and watershed algorithm, which achieves similar pixel accuracy but significant higher cell count accuracy than pixel-wise classification methods do, and the advantage is most obvious when applying on noisy images of densely packed cells.
4
Rapid, modular, and cost-effective generation of donor DNA constructs for CRISPR-based gene knock-in
TL;DR: This study developed a general Gibson assembly procedure that combines strengths of a Modular Overlap-Directed Assembly with Linkers (MODAL) strategy and a restriction enzyme based hierarchical framework and allows fusing sgRNAs to the constructs for enhanced homology-directed repairing efficiency.
2
Spatial clustering and common regulatory elements correlate with coordinated gene expression
Jingyu Zhang,Jingyu Zhang,Hengyu Chen,Ruoyan Li,David A. Taft,Guang Yao,Fan Bai,Jianhua Xing +7 more
TL;DR: In this paper, a combined analysis of time-course RNA-seq data of TGF-β treated MCF10A cells and related epigenomic and Hi-C data was performed to examine how local gene environment and transcription factor regulation are coupled.
Rapid, modular, and cost-effective generation of donor DNA constructs for CRISPR-based gene knock-in.
Yi Jiun Chen,Ya Yun Cheng,Weikang Wang,Xiao-Jun Tian,Daniel E. Lefever,David A. Taft,Jingyu Zhang,Jianhua Xing +7 more
TL;DR: A new strategy is developed that combines a Gibson assembly reaction, a linker pair composed of eight in silico screened restriction enzyme sites, and a hierarchical framework to remarkably improve the efficiency of producing donor constructs for common genes as well as for the genes containing unbalanced guanine-cytosine content and requiring a selectable marker.