HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction.
HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction.
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HybridSucc:用于一般和物种特异性琥珀酰化位点预测的混合学习架构
DOI:
10.1016/j.gpb.2019.11.010
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发表时间:
2020-04
期刊:
影响因子:
--
通讯作者:
Xue Y
中科院分区:
文献类型:
--
作者:
Ning W;Xu H;Jiang P;Cheng H;Deng W;Guo Y;Xue Y
As an important protein acylation modification, lysine succinylation (Ksucc) is involved in diverse biological processes, and participates in human tumorigenesis. Here, we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set, combined 10 types of informative features, and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework. We constructed a new tool named HybridSucc, which achieved area under curve (AUC) values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites, respectively. In comparison, the accuracy of HybridSucc was 17.84%–50.62% better than that of other existing tools. Using HybridSucc, we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins, including PKM2, SHMT2, and IDH2. We not only developed a high-profile tool for predicting Ksucc sites, but also generated useful candidates for further experimental consideration. The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.
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DOI:
10.1126/science.aaa1193
发表时间:
2015-04-03
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
Amunts A;Brown A;Toots J;Scheres SHW;Ramakrishnan V
通讯作者:
Ramakrishnan V
DOI:
10.1093/bioinformatics/bts565
发表时间:
2012-12-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
作者:
Fu L;Niu B;Zhu Z;Wu S;Li W
通讯作者:
Li W
影响因子:
11.4
作者:
Landry, Christian R.;Levy, Emmanuel D.;Michnick, Stephen W.
通讯作者:
Michnick, Stephen W.
影响因子:
--
作者:
Hasan, Md. Mehedi;Yang, Shiping;Mollah, Md. Nurul Haque
通讯作者:
Mollah, Md. Nurul Haque
影响因子:
16.6
作者:
Li L;Shi L;Yang S;Yan R;Zhang D;Yang J;He L;Li W;Yi X;Sun L;Liang J;Cheng Z;Shi L;Shang Y;Yu W
通讯作者:
Yu W