Mingzhou Song

2.4k citations
66 papers · 1.4k indexed · h-index 17
Topics
Gene Regulatory Network Analysis (15 papers)Gene expression and cancer classification (11 papers)Bioinformatics and Genomic Networks (11 papers)
Partner nations
United StatesChinaCanada

In The Last Decade

Mingzhou Song

63 papers receiving 1.4k citations

Peers

Mingzhou Song
Comparison fields: 5 of 155
  • Plant Science 656
  • Molecular Biology 432
  • Artificial Intelligence 150
  • Endocrinology 150
  • Biomedical Engineering 145
Replace Yuanning Liu with:
Yuanning Liu China
Xiaohui Cheng China
Ruifang Liu China
Zhiyi Zhang China
Marc Strickert Germany
Marie‐Laure Martin‐Magniette France
Yufeng Wu China
Lan Yi United States
Robert Müller Germany
Mingzhou Song relative to Yuanning Liu China Yuanning Liu's profile →
Citations per field
00.5×5.6×
Yuanning Liu · 1×
Citations per year

Countries citing papers authored by Mingzhou Song

Since Specialization
Citations

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

Fields of papers citing papers by Mingzhou Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingzhou Song

This figure shows the co-authorship network connecting the top 25 collaborators of Mingzhou Song. A scholar is included among the top collaborators of Mingzhou Song 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 Mingzhou Song. Mingzhou Song 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
#WorkIndexed citations
1 2
2 4
3 0
4 1
5 7
6 1
7 6
8 46
9 18
10 4
11 19
12 20
13 149
14 3
15 20
16
Maximum Likelihood Quantization of Genomic Features Using Dynamic Programming
2
17
Detecting Low Complexity Clusters by Skewness and Kurtosis in Data Stream Clustering.
2
18 60
19
Optimally Quantized and Smoothed Histograms.
3
20 13

About Mingzhou Song

Mingzhou Song is a scholar working on Endocrinology, Molecular Biology and Computer Vision and Pattern Recognition, having authored 66 papers that have together received 1.4k indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (15 papers), Gene expression and cancer classification (11 papers) and Bioinformatics and Genomic Networks (11 papers). The work is most often cited by research in Endocrinology (150 citations), Plant Science (656 citations) and Molecular Biology (432 citations). Mingzhou Song has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Haizhou Wang, Jinfa Zhang, Joseph Said, Zhongxu Lin, Xianlong Zhang, Hongbin Wang, Z. Lewis Liu, Menggen Ma, Stéphane Boissinot and Hua Zhong. Their work appears in journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

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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