Kai-Yao Huang
The Chinese University of Hong Kong
50 Papers
22 Citations
Kai-Yao Huang is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Biology & Medicine. The author has an hindex of 19, co-authored 46 publications. Previous affiliations of Kai-Yao Huang include National Cheng Kung University & Yuan Ze University.
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Papers
dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications.
Kai-Yao Huang,Tzong-Yi Lee,Hui Ju Kao,Chen Tse Ma,Chao Chun Lee,Tsai Hsuan Lin,Wen Chi Chang,Hsien Da Huang +7 more
TL;DR: The dbPTM update highlights the current challenges in PTM crosstalk investigation and breaks the bottleneck of how proteomics may contribute to understanding PTM codes, revealing the next level of data complexity and proteomic limitation in prospective PTM research.
Global characterization of macrophage polarization mechanisms and identification of M2-type polarization inhibitors.
Lizhi He,Jhih-Hua Jhong,Jhih-Hua Jhong,Qi Chen,Kai-Yao Huang,Karin Strittmatter,Johannes Kreuzer,Michael DeRan,Xu Wu,Tzong-Yi Lee,Nikolai Slavov,Wilhelm Haas,Alexander G. Marneros +12 more
TL;DR: In this article, global quantitative time-course proteomics and phosphoproteomics paired with transcriptomics provide a comprehensive characterization of temporal changes in cell metabolism, cellular functions, and signaling pathways that occur during the induction phase of M1- versus M2-type polarization.
163
dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data
Jhih-Hua Jhong,Yu-Hsiang Chi,Wen-Chi Li,Tsai-Hsuan Lin,Kai-Yao Huang,Tzong-Yi Lee,Tzong-Yi Lee +6 more
TL;DR: The dbAMP database as mentioned in this paper is a database of antimicrobial peptides (AMPs) from the public domain and manually curated literature, including 12, 389 unique entries, including 4271 experimentally verified AMPs and 8118 putative AMPs along with their functional activities, supported by 1924 articles.
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Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication.
TL;DR: This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.
81
dbSNO 2.0: a resource for exploring structural environment, functional and disease association and regulatory network of protein S-nitrosylation
Yi-Ju Chen,Cheng-Tsung Lu,Min-Gang Su,Kai-Yao Huang,Wei-Chieh Ching,Hsiao-Hsiang Yang,Yen-Chen Liao,Yu-Ju Chen,Tzong-Yi Lee +8 more
TL;DR: An endogenous yet pathophysiological S-nitrosoproteomic dataset from colorectal cancer patients was adopted to demonstrate that dbSNO could discover potential SNO proteins involving in the regulation of NO signaling for cancer pathways.
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