Nitish K. Mishra

1.7k citations
42 papers · 1.2k indexed · h-index 21
Topics
Machine Learning in Bioinformatics (6 papers)RNA and protein synthesis mechanisms (6 papers)Epigenetics and DNA Methylation (6 papers)

In The Last Decade

Nitish K. Mishra

40 papers receiving 1.2k citations

Peers

Nitish K. Mishra
Comparison fields: 5 of 109
  • Molecular Biology 856
  • Microbiology 180
  • Computational Theory and Mathematics 173
  • Oncology 139
  • Cancer Research 132
Replace Roberto Raggiaschi with:
Roberto Raggiaschi Italy
Xiaoling Xie China
Annemette Thougaard Denmark
Xiang Wu China
Katia Pane Italy
Dezső Módos United Kingdom
Minjie Mou China
Dong‐Yan Shen China
Ben S. Sidders United Kingdom
Robert R. Lavieri United States
Nitish K. Mishra relative to Roberto Raggiaschi Italy Roberto Raggiaschi's profile →
Citations per field
00.5×4.5×
Roberto Raggiaschi · 1×
Citations per year

Countries citing papers authored by Nitish K. Mishra

Since Specialization
Citations

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

Fields of papers citing papers by Nitish K. Mishra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nitish K. Mishra. 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 Nitish K. Mishra. The network helps show where Nitish K. Mishra may publish in the future.

Co-authorship network of co-authors of Nitish K. Mishra

This figure shows the co-authorship network connecting the top 25 collaborators of Nitish K. Mishra. A scholar is included among the top collaborators of Nitish K. Mishra 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 Nitish K. Mishra. Nitish K. Mishra 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 1
3 10
4 12
5 23
6 30
7 2
8 16
9 29
10 54
11 27
12 39
13 36
14 24
15 87
16 17
17 22
18 31
19 117
20 15

About Nitish K. Mishra

Nitish K. Mishra is a scholar working on Obstetrics and Gynecology, Molecular Biology and Cancer Research, having authored 42 papers that have together received 1.2k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (6 papers), RNA and protein synthesis mechanisms (6 papers) and Epigenetics and DNA Methylation (6 papers). The work is most often cited by research in Microbiology (180 citations), Molecular Biology (856 citations) and Computational Theory and Mathematics (173 citations). Nitish K. Mishra has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Gajendra P. S. Raghava, Chittibabu Guda, Sneh Lata, Jagat Chauhan, Patrick X. Zhao, Uppala Radhakrishna, Sangeetha Vishweswaraiah, Ray Bahado‐Singh, Buket Aydas and Ali Yılmaz. Their work appears in journals such as PLoS ONE, Molecular and Cellular Biology and Cancer 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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