Ilya Muchnik

2.7k total citations
68 papers, 1.8k citations indexed

About

Ilya Muchnik is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ilya Muchnik has authored 68 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 20 papers in Artificial Intelligence and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ilya Muchnik's work include Machine Learning in Bioinformatics (13 papers), RNA and protein synthesis mechanisms (11 papers) and Genomics and Phylogenetic Studies (9 papers). Ilya Muchnik is often cited by papers focused on Machine Learning in Bioinformatics (13 papers), RNA and protein synthesis mechanisms (11 papers) and Genomics and Phylogenetic Studies (9 papers). Ilya Muchnik collaborates with scholars based in United States, Russia and United Kingdom. Ilya Muchnik's co-authors include Inna Dubchak, Stephen R. Holbrook, Sung‐Hou Kim, Temple F. Smith, P. L. Hammer, Roderic Guigó, Eddy Mayoraz, Toshihide Ibaraki, Endre Boros and Alexander Kogan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, IEEE Transactions on Image Processing and Proteins Structure Function and Bioinformatics.

In The Last Decade

Ilya Muchnik

66 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ilya Muchnik United States 18 1.1k 341 318 141 92 68 1.8k
Laxmi Parida United States 23 979 0.9× 152 0.4× 422 1.3× 275 2.0× 201 2.2× 142 2.3k
Yasubumi Sakakibara Japan 31 2.2k 1.9× 499 1.5× 571 1.8× 208 1.5× 58 0.6× 114 3.3k
Bo Liao China 29 1.6k 1.4× 216 0.6× 253 0.8× 72 0.5× 34 0.4× 111 2.1k
Zheng Rong Yang United Kingdom 19 940 0.8× 213 0.6× 253 0.8× 93 0.7× 38 0.4× 94 1.9k
Jorng‐Tzong Horng Taiwan 28 1.4k 1.2× 86 0.3× 224 0.7× 123 0.9× 142 1.5× 135 2.6k
Robert E. Smith United States 23 259 0.2× 270 0.8× 551 1.7× 121 0.9× 17 0.2× 56 1.6k
Zhihua Du United States 27 1.1k 0.9× 396 1.2× 509 1.6× 71 0.5× 95 1.0× 86 1.9k
Chuan Yi Tang Taiwan 23 596 0.5× 384 1.1× 249 0.8× 146 1.0× 40 0.4× 145 1.7k
Jianzhu Ma United States 27 2.2k 2.0× 489 1.4× 309 1.0× 162 1.1× 33 0.4× 69 3.1k
Guohui Lin Canada 24 1.1k 1.0× 248 0.7× 342 1.1× 228 1.6× 58 0.6× 171 2.6k

Countries citing papers authored by Ilya Muchnik

Since Specialization
Citations

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

Fields of papers citing papers by Ilya Muchnik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ilya Muchnik

This figure shows the co-authorship network connecting the top 25 collaborators of Ilya Muchnik. A scholar is included among the top collaborators of Ilya Muchnik 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 Ilya Muchnik. Ilya Muchnik 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
1.
Tkachev, Victor, Maxim Sorokin, Andrew Garazha, et al.. (2019). FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier. Frontiers in Genetics. 9. 717–717. 17 indexed citations
2.
Muchnik, Ilya, et al.. (2012). Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data. Journal of The Royal Society Interface. 9(73). 1934–1942. 29 indexed citations
3.
Kulikowski, Casimir A., et al.. (2008). Coring method for clustering a graph. Proceedings - International Conference on Pattern Recognition. 1–4. 9 indexed citations
4.
Muchnik, Ilya, et al.. (2007). Quasi-concave functions on meet-semilattices. Discrete Applied Mathematics. 156(4). 492–499. 2 indexed citations
5.
Vashist, Akshay, Casimir A. Kulikowski, & Ilya Muchnik. (2007). Ortholog Clustering on a Multipartite Graph. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 4(1). 17–27. 11 indexed citations
6.
Muchnik, Ilya, et al.. (2006). Optimal requantization of deep grayscale images and Lloyd-Max quantization. IEEE Transactions on Image Processing. 15(2). 445–448. 9 indexed citations
7.
Fradkin, Dmitriy, et al.. (2006). Prevalence of wet litter and the associated risk factors in broiler flocks in the United Kingdom. Veterinary Record. 158(18). 615–622. 31 indexed citations
8.
Kulikowski, Casimir A., et al.. (2005). Sequence-based Protein-Protein Interaction Prediction Optimized for Target Selection in Biological Experiments. PubMed. 402. 236–239. 1 indexed citations
9.
Muchnik, Ilya, et al.. (2005). A sparse dynamic programming algorithm for alignment with non-overlapping inversions. RAIRO - Theoretical Informatics and Applications. 39(1). 175–189. 3 indexed citations
10.
Mirkin, Boris & Ilya Muchnik. (2002). Layered clusters of tightness set functions. Applied Mathematics Letters. 15(2). 147–151. 10 indexed citations
11.
Wolf, Denise M., et al.. (2000). Automatic discovery of sub-molecular sequence domains in multi-aligned \nsequences: A dynamic programming algorithm for multiple alignment \nsegmentation. eScholarship (California Digital Library). 2 indexed citations
12.
Mirkin, Boris, et al.. (1997). Monotone linkage clustering and quasi-concave set functions. Applied Mathematics Letters. 10(4). 19–24. 8 indexed citations
13.
Guigó, Roderic, Ilya Muchnik, & Temple F. Smith. (1996). Reconstruction of Ancient Molecular Phylogeny. Molecular Phylogenetics and Evolution. 6(2). 189–213. 139 indexed citations
14.
Mirkin, Boris, Ilya Muchnik, & Temple F. Smith. (1995). A Biologically Meaningful Model for Comparing Molecular Phylogenies. Nanotechnology. 21(34). 345702–345702. 4 indexed citations
15.
Mirkin, Boris, Ilya Muchnik, & Temple F. Smith. (1995). A Biologically Consistent Model for Comparing Molecular Phylogenies. Journal of Computational Biology. 2(4). 493–507. 60 indexed citations
16.
Demetrovics, János, et al.. (1992). Normal form relation schemes: a new characterization. SZTAKI Publication Repository (Hungarian Academy of Sciences). 4 indexed citations
17.
Demetrovics, János, et al.. (1992). On the interaction between closure operations and choice functions with applications to relational databases. SZTAKI Publication Repository (Hungarian Academy of Sciences). 3 indexed citations
18.
Demetrovics, János, Leonid Libkin, & Ilya Muchnik. (1992). Functional dependencies in relational databases: A lattice point of view. Discrete Applied Mathematics. 40(2). 155–185. 34 indexed citations
19.
Biskup, Joachim, János Demetrovics, Leonid Libkin, & Ilya Muchnik. (1991). On Relational Database Schemes Having Unique Minimal Key.. Journal of automata, languages and combinatorics. 27. 217–225. 6 indexed citations
20.
Muchnik, Ilya, et al.. (1990). Nuclei of monotonic systems on a semilattice of sets.. Automation and Remote Control. 50. 1095–1102. 2 indexed citations

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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