Lei Xie

10.8k citations
185 papers · 6.2k indexed · 1 hit paper · h-index 44

Lei Xie

177 papers receiving 6.1k citations

Hit Papers

AI-driven multi-omics integration for multi-scale predict...412025202610203040

Peers

Lei Xie
Comparison fields: 5 of 189
  • Computational Theory and Mathematics 1.9k
  • Energy Engineering and Power Technology 326
  • Catalysis 494
  • Molecular Biology 2.9k
  • Materials Chemistry 1.5k
Replace Jong Min Lee with:
Jong Min Lee South Korea
James C. Liao United States
Hiroshi Sasaki Japan
Yonghong Chen China
Guohui Li China
Fang Wang China
Lian Li China
Guozheng Li China
Hiroshi Noguchi Japan
Hui Jiang China
Lei Xie relative to Jong Min Lee South Korea Jong Min Lee's profile →
Citations per field
00.5×7.2×
Jong Min Lee · 1×
Citations per year

Countries citing papers authored by Lei Xie

Since Specialization
Citations

This map shows the geographic impact of Lei Xie'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 Lei Xie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lei Xie more than expected).

Fields of papers citing papers by Lei Xie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lei Xie. 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 Lei Xie. The network helps show where Lei Xie may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Lei Xie, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lei Xie Line = papers co-authored together Lei Xie links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20242
2 202414
3 20248
4 20240
5 202254
6
WeNet: Production First and Production Ready End-to-End Speech Recognition Toolkit.
202111
7 202119
8 202013
9 20204
10 202033
11 20202
12 201815
13 201719
14
Attention-Based End-to-End Speech Recognition in Mandarin.
20174
15 201655
16 201448
17
Discovering Protein Clusters
20120
18 2011125
19 201083
20 2009206

About Lei Xie

Lei Xie is a scholar working on Energy Engineering and Power Technology, Computational Theory and Mathematics and Catalysis, having authored 185 papers that have together received 6.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (53 papers), Bioinformatics and Genomic Networks (31 papers), Hydrogen Storage and Materials (24 papers), Speech Recognition and Synthesis (22 papers), Speech and Audio Processing (17 papers), Protein Structure and Dynamics (16 papers), Music and Audio Processing (16 papers) and Ammonia Synthesis and Nitrogen Reduction (16 papers). The work is most often cited by research in Computational Theory and Mathematics (1.9k citations), Energy Engineering and Power Technology (326 citations) and Catalysis (494 citations). Lei Xie has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Philip E. Bourne, Li Xie, Xingguo Li, Sarah Kinnings, Jie Zheng, Jianglan Qu, Rong Yang, Peter J. Tonge, Nina Liu and Qiao Liu. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Advanced Materials.

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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