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Wing-Kin Sung
Researcher at National University of Singapore
Publications - 335
Citations - 28128
Wing-Kin Sung is an academic researcher from National University of Singapore. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 64, co-authored 327 publications receiving 26116 citations. Previous affiliations of Wing-Kin Sung include University of Hong Kong & Yale University.
Papers
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Book ChapterDOI
Algorithms for the Majority Rule (+) Consensus Tree and the Frequency Difference Consensus Tree
TL;DR: Two new deterministic algorithms for constructing consensus trees with identical leaf label sets and n leaves each are presented, which are optimal since the input size is Ω(k n), and the second one constructs the frequency difference consensus tree in min {O (k n 2), O (k + log2 n) time.
Book ChapterDOI
An Efficient Algorithm for the Rooted Triplet Distance Between Galled Trees
TL;DR: The previously fastest algorithm for computing the rooted triplet distance between two input galled trees runs in \(O(n^{2.687})\) time, where n is the cardinality of the leaf label set.
Journal ArticleDOI
Pan-omics analysis of biological data.
TL;DR: This issue will discuss methods for analyzing and integrating omics datasets, and discusses recent methods for integrated analysis of protein-protein interaction data, metabolomics data, genomics and transcriptomics data.
Book ChapterDOI
The 2-Interval Pattern Matching Problems and Its Application to ncRNA Scanning
TL;DR: This paper proposes an efficient algorithm to solve the 2-Interval pattern matching problem for {< , ⊂} -structured pattern and applies it on scanning for the ncRNAs without pseudoknots and shows that the method can result in much fewer false positives.
Posted ContentDOI
Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study
Mustafa Umit Oner,Mustafa Umit Oner,Jianbin Chen,Egor Revkov,Egor Revkov,Anne James,Seow Ye Heng,Arife Neslihan Kaya,Jacob Josiah Santiago Alvarez,Jacob Josiah Santiago Alvarez,Angela Takano,Xin Min Cheng,Tony Kiat Hon Lim,Daniel Shao Weng Tan,Daniel Shao Weng Tan,Weiwei Zhai,Weiwei Zhai,Anders Jacobsen Skanderup,Anders Jacobsen Skanderup,Wing-Kin Sung,Wing-Kin Sung,Hwee Kuan Lee +21 more
TL;DR: Wang et al. as mentioned in this paper developed a deep multiple instance learning model predicting tumor purity from H&E stained digital histopathology slides, which can be used for high throughput sample selection for genomic analysis, which will help reduce pathologists9 workload and decrease inter-observer variability.