Runtao Yang

715 total citations
38 papers, 486 citations indexed

About

Runtao Yang is a scholar working on Molecular Biology, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Runtao Yang has authored 38 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 6 papers in Computer Networks and Communications and 5 papers in Artificial Intelligence. Recurrent topics in Runtao Yang's work include Machine Learning in Bioinformatics (21 papers), RNA and protein synthesis mechanisms (9 papers) and Genomics and Phylogenetic Studies (7 papers). Runtao Yang is often cited by papers focused on Machine Learning in Bioinformatics (21 papers), RNA and protein synthesis mechanisms (9 papers) and Genomics and Phylogenetic Studies (7 papers). Runtao Yang collaborates with scholars based in China. Runtao Yang's co-authors include Chengjin Zhang, Rui Gao, Lína Zhang, Yong Song, Qing Song, Hongling Wang, Feng Wu, Lina Zhang, Guangtao Wei and Baiying Li and has published in prestigious journals such as PLoS ONE, Scientific Reports and Chemical Engineering Journal.

In The Last Decade

Runtao Yang

36 papers receiving 478 citations

Peers

Runtao Yang
Qiang Lyu China
Jian Peng China
Pritam Chanda United States
Vishwesh Kulkarni United States
Hengxi Zhang China
Nataša Jonoska United States
Xianfang Wang China
Mingyi Zhang China
Ilan Shomorony United States
Qiang Lyu China
Runtao Yang
Citations per year, relative to Runtao Yang Runtao Yang (= 1×) peers Qiang Lyu

Countries citing papers authored by Runtao Yang

Since Specialization
Citations

This map shows the geographic impact of Runtao Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Runtao Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Runtao Yang more than expected).

Fields of papers citing papers by Runtao Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Runtao Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Runtao Yang. The network helps show where Runtao Yang may publish in the future.

Co-authorship network of co-authors of Runtao Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Runtao Yang. A scholar is included among the top collaborators of Runtao Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Runtao Yang. Runtao Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yuan, Quan, et al.. (2025). An ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations. Computational Biology and Chemistry. 115. 108336–108336.
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Yang, Runtao, et al.. (2024). GCNGAT: Drug–disease association prediction based on graph convolution neural network and graph attention network. Artificial Intelligence in Medicine. 150. 102805–102805. 9 indexed citations
4.
Yang, Runtao, et al.. (2023). Multi-view feature fusion and density-based minority over-sampling technique for amyloid protein prediction under imbalanced data. Applied Soft Computing. 150. 111100–111100. 9 indexed citations
5.
Yang, Runtao, et al.. (2023). Prediction of plant LncRNA-protein interactions based on feature fusion and an improved residual network. Expert Systems with Applications. 238. 121991–121991. 2 indexed citations
6.
Qiu, Qingtao, et al.. (2023). Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model. Cancers. 15(18). 4443–4443. 6 indexed citations
7.
Yang, Runtao, et al.. (2023). ECAmyloid: An amyloid predictor based on ensemble learning and comprehensive sequence-derived features. Computational Biology and Chemistry. 104. 107853–107853. 3 indexed citations
8.
Yang, Runtao, et al.. (2022). A deep learning framework for enhancer prediction using word embedding and sequence generation. Biophysical Chemistry. 286. 106822–106822. 12 indexed citations
9.
Yang, Runtao, et al.. (2022). Association prediction of CircRNAs and diseases using multi-homogeneous graphs and variational graph auto-encoder. Computers in Biology and Medicine. 151(Pt A). 106289–106289. 8 indexed citations
10.
Wu, Feng, et al.. (2021). A deep learning framework combined with word embedding to identify DNA replication origins. Scientific Reports. 11(1). 844–844. 10 indexed citations
11.
Song, Yong, et al.. (2019). A Swarm Robotic Exploration Strategy Based on an Improved Random Walk Method. Journal of Robotics. 2019. 1–9. 32 indexed citations
12.
Yang, Runtao, et al.. (2018). A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique. BioMed Research International. 2018. 1–10. 23 indexed citations
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Yang, Runtao, et al.. (2018). Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides. Scientific Reports. 8(1). 14062–14062. 8 indexed citations
15.
Song, Yong, et al.. (2018). Bacterial foraging optimization based on improved chemotaxis process and novel swarming strategy. Applied Intelligence. 49(4). 1283–1305. 14 indexed citations
16.
Zhang, Lína, Chengjin Zhang, Rui Gao, Runtao Yang, & Qing Song. (2016). Using the SMOTE technique and hybrid features to predict the types of ion channel-targeted conotoxins. Journal of Theoretical Biology. 403. 75–84. 19 indexed citations
17.
Zhang, Chengjin, et al.. (2016). Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy. PLoS ONE. 11(9). e0163274–e0163274. 26 indexed citations
18.
Zhang, Lína, Chengjin Zhang, Rui Gao, Runtao Yang, & Qing Song. (2016). Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes. BMC Bioinformatics. 17(1). 225–225. 37 indexed citations
19.
Yang, Runtao, et al.. (2015). An Ensemble Method with Hybrid Features to Identify Extracellular Matrix Proteins. PLoS ONE. 10(2). e0117804–e0117804. 20 indexed citations
20.
Zhang, Lína, Chengjin Zhang, Rui Gao, & Runtao Yang. (2015). JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method. BioMed Research International. 2015. 1–12. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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