John Kececioglu
- Artificial Intelligence top 2%
- Algorithms and Data Compression 23
- Molecular Biology top 10%
- Genomics and Phylogenetic Studies 26
- Machine Learning in Bioinformatics 6
- Glycosylation and Glycoproteins Research 5
- RNA and protein synthesis mechanisms 5
- DNA and Biological Computing 4
- Microbial Metabolic Engineering and Bioproduction 4
- Genetics top 5%
- Genome Rearrangement Algorithms 10
- Signal Processing top 5%
- Software top 10%
- Co-authors
- David J. LipmanStephen F. AltschulTravis J. WheelerDavid SankoffEugene W. MyersAlejandro A. SchäfferSandeep K. S. GuptaDan DeBlasio
- Journals
- Journal of Computational Biology (6 papers)Bioinformatics (3 papers)Discrete Applied Mathematics (3 papers)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
John Kececioglu
45 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 495
- Molecular Biology 1.0k
- Genetics 351
- Signal Processing 109
- Software 29
Countries citing papers authored by John Kececioglu
This map shows the geographic impact of John Kececioglu'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 John Kececioglu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Kececioglu more than expected).
Fields of papers citing papers by John Kececioglu
This network shows the impact of papers produced by John Kececioglu. 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 John Kececioglu. The network helps show where John Kececioglu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside John Kececioglu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 2 | |
| 2 | 2023 | 5 | |
| 3 | 2022 | 5 | |
| 4 | 2021 | 1 | |
| 5 | 2021 | 41 | |
| 6 | 2020 | 1 | |
| 7 | 2017 | 3 | |
| 8 | 2015 | 9 | |
| 9 | 2013 | 15 | |
| 10 | 2012 | 21 | |
| 11 | 2008 | 10 | |
| 12 | 2000 | 29 | |
| 13 | 1998 | 9 | |
| 14 | 1998 | 27 | |
| 15 | Computing Maximum-Cardinality Matchings in Sparse General Graphs. | 1998 | 1 |
| 16 | 1997 | 3 | |
| 17 | 1997 | 16 | |
| 18 | 1995 | 111 | |
| 19 | 1994 | 6 | |
| 20 | 1989 | 334 |
About John Kececioglu
John Kececioglu is a scholar working on Artificial Intelligence, Molecular Biology, Information Systems and Management, Genetics and Instrumentation, having authored 45 papers that have together received 1.4k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (26 papers), Algorithms and Data Compression (23 papers), Genome Rearrangement Algorithms (10 papers), Machine Learning in Bioinformatics (6 papers), Glycosylation and Glycoproteins Research (5 papers), RNA and protein synthesis mechanisms (5 papers), DNA and Biological Computing (4 papers) and Microbial Metabolic Engineering and Bioproduction (4 papers). The work is most often cited by research in Artificial Intelligence (495 citations), Molecular Biology (1.0k citations), Genetics (351 citations), Signal Processing (109 citations) and Software (29 citations). John Kececioglu has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include David J. Lipman, Stephen F. Altschul, Travis J. Wheeler, David Sankoff, Eugene W. Myers, Alejandro A. Schäffer, Sandeep K. S. Gupta, Dan DeBlasio, Dan Gusfield and Christian Collberg. Their work appears in journals such as Journal of Computational Biology, Bioinformatics, Discrete Applied Mathematics, Algorithmica and The Astronomical Journal.
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.