Junshui Ma

6.3k citations
48 papers · 2.7k indexed · 2 hit papers · h-index 19

Junshui Ma

48 papers receiving 2.6k citations

Hit Papers

Extreme Gradient Boosting as a Method for Quantitative St...3852015202620182022250500750

Peers

Junshui Ma
Comparison fields: 5 of 182
  • Computational Theory and Mathematics 837
  • Endocrine and Autonomic Systems 213
  • Cognitive Neuroscience 501
  • Experimental and Cognitive Psychology 321
  • Signal Processing 231
Replace Vladimir Svetnik with:
Vladimir Svetnik United States
Guo‐Qiang Zhang United States
Jinbo Bi United States
Alan Wee‐Chung Liew Australia
Alejandro Pazos Spain
Holger Fröhlich Germany
Ziyu Wang China
Polina Mamoshina United States
Tapio Pahikkala Finland
Lihua Li China
Junshui Ma relative to Vladimir Svetnik United States Vladimir Svetnik's profile →
Citations per field
00.5×1.5×2.4×
Vladimir Svetnik · 1×
Citations per year

Countries citing papers authored by Junshui Ma

Since Specialization
Citations

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

Fields of papers citing papers by Junshui Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Junshui Ma, 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 Junshui Ma Line = papers co-authored together Junshui Ma links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20213
2 20202
3 20207
4 20206
5 201832
6 201619
7
Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationshipsbreakdown →
2016385
8
Deep Neural Nets as a Method for Quantitative Structure–Activity Relationshipsbreakdown →
2015787
9 2013132
10 201236
11 201117
12 201113
13 20104
14 201018
15 20091
16 200745
17 200336
18 20033
19
Feature study for high-range-resolution based automatic target recognition : analysis and extraction /
20012
20 20017

About Junshui Ma

Junshui Ma is a scholar working on Cognitive Neuroscience, Endocrine and Autonomic Systems and Signal Processing, having authored 48 papers that have together received 2.7k indexed citations. Recurring topics across this work include Sleep and Wakefulness Research (11 papers), Sleep and related disorders (9 papers), EEG and Brain-Computer Interfaces (9 papers), Circadian rhythm and melatonin (6 papers), Blind Source Separation Techniques (5 papers), Cancer Immunotherapy and Biomarkers (5 papers), Advanced SAR Imaging Techniques (5 papers) and User Authentication and Security Systems (4 papers). The work is most often cited by research in Computational Theory and Mathematics (837 citations), Endocrine and Autonomic Systems (213 citations) and Cognitive Neuroscience (501 citations). Junshui Ma has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Andy Liaw, Robert P. Sheridan, Vladimir Svetnik, Simon Perkins, George E. Dahl, James Theiler, Eric Gifford, Wei Min Wang, Yuting Xu and Christopher Lines. Their work appears in journals such as SLEEP, Journal of Clinical Oncology, Journal of Chemical Information and Modeling, Neurocomputing and Journal of Sleep Research.

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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