Iosif I. Vaisman

2.1k total citations
67 papers, 1.6k citations indexed

About

Iosif I. Vaisman is a scholar working on Molecular Biology, Materials Chemistry and Virology. According to data from OpenAlex, Iosif I. Vaisman has authored 67 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Molecular Biology, 18 papers in Materials Chemistry and 9 papers in Virology. Recurrent topics in Iosif I. Vaisman's work include Protein Structure and Dynamics (28 papers), Machine Learning in Bioinformatics (18 papers) and Enzyme Structure and Function (14 papers). Iosif I. Vaisman is often cited by papers focused on Protein Structure and Dynamics (28 papers), Machine Learning in Bioinformatics (18 papers) and Enzyme Structure and Function (14 papers). Iosif I. Vaisman collaborates with scholars based in United States, Russia and Japan. Iosif I. Vaisman's co-authors include Majid Masso, Max L. Berkowitz, Alexander Tropsha, Raj Kumar Singh, Estela Blaisten‐Barojas, Mohammed Lach-hab, Shujiang Yang, Guangshun Wang, Monique L. van Hoek and David Bostick and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Biological Chemistry and The Journal of Chemical Physics.

In The Last Decade

Iosif I. Vaisman

62 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Iosif I. Vaisman United States 22 1.0k 405 229 160 139 67 1.6k
Hatsuho Uedaira Japan 22 958 0.9× 431 1.1× 145 0.6× 210 1.3× 198 1.4× 68 1.6k
René C. van Schaik Netherlands 12 953 0.9× 371 0.9× 381 1.7× 53 0.3× 191 1.4× 17 1.5k
Daniel Trzesniak Switzerland 12 1.2k 1.1× 445 1.1× 483 2.1× 68 0.4× 237 1.7× 16 1.7k
Roland H. Stote France 24 1.1k 1.1× 196 0.5× 319 1.4× 50 0.3× 122 0.9× 59 1.9k
Aritomo Shinozaki United States 9 1.4k 1.4× 721 1.8× 345 1.5× 68 0.4× 158 1.1× 10 2.5k
Saeed Izadi United States 19 1.2k 1.2× 380 0.9× 365 1.6× 44 0.3× 176 1.3× 37 2.0k
Parbati Biswas India 21 663 0.7× 368 0.9× 200 0.9× 251 1.6× 76 0.5× 87 1.4k
Zhe Wu United States 17 723 0.7× 253 0.6× 271 1.2× 49 0.3× 83 0.6× 31 1.3k
Raffaello Potestio Italy 22 745 0.7× 580 1.4× 384 1.7× 59 0.4× 112 0.8× 62 1.4k
Beatriz Carrasco Spain 13 1.5k 1.5× 588 1.5× 201 0.9× 96 0.6× 240 1.7× 24 2.2k

Countries citing papers authored by Iosif I. Vaisman

Since Specialization
Citations

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

Fields of papers citing papers by Iosif I. Vaisman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Iosif I. Vaisman. 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 Iosif I. Vaisman. The network helps show where Iosif I. Vaisman may publish in the future.

Co-authorship network of co-authors of Iosif I. Vaisman

This figure shows the co-authorship network connecting the top 25 collaborators of Iosif I. Vaisman. A scholar is included among the top collaborators of Iosif I. Vaisman 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 Iosif I. Vaisman. Iosif I. Vaisman 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.
Vaisman, Iosif I., et al.. (2024). Topology-based protein classification: A deep learning approach. Biochemical and Biophysical Research Communications. 746. 151240–151240.
2.
Shao, Jonathan, Yan Zhao, Wei Wei, & Iosif I. Vaisman. (2024). AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria. Frontiers in Microbiology. 15. 1304044–1304044. 11 indexed citations
3.
Woodward-Greene, M. Jennifer, Jason M. Kinser, Tad S. Sonstegard, et al.. (2022). PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements. PLoS ONE. 17(10). e0275821–e0275821.
4.
Gordon, Scott M., Denis Sviridov, Angel Aponte, et al.. (2022). A New Structural Model of Apolipoprotein B100 Based on Computational Modeling and Cross Linking. International Journal of Molecular Sciences. 23(19). 11480–11480. 14 indexed citations
5.
Masso, Majid, et al.. (2019). Cancer-Ml: Modeling Fitness of Unregulated RAS Mutants using Computational Mutagenesis and Machine Learning. Biophysical Journal. 116(3). 561a–561a.
6.
Oberti, Mauricio & Iosif I. Vaisman. (2017). Identification and Prediction of Intrinsically Disordered Regions in Proteins Using n-grams. 67–72. 3 indexed citations
7.
Masso, Majid, et al.. (2014). Structure-based predictors of resistance to the HIV-1 integrase inhibitor Elvitegravir. Antiviral Research. 106. 5–12. 5 indexed citations
8.
Masso, Majid, et al.. (2014). Structure-Based Predictors of Resistance to the HIV-1 Integrase Inhibitor Elvitegravir. Biophysical Journal. 106(2). 409a–409a. 1 indexed citations
9.
Chiechi, Antonella, Chiara Novello, Giovanna Magagnoli, et al.. (2013). Elevated TNFR1 and Serotonin in Bone Metastasis Are Correlated with Poor Survival following Bone Metastasis Diagnosis for Both Carcinoma and Sarcoma Primary Tumors. Clinical Cancer Research. 19(9). 2473–2485. 28 indexed citations
10.
Masso, Majid & Iosif I. Vaisman. (2011). A structure-based computational mutagenesis elucidates the spectrum of stability-activity relationships in proteins. PubMed. 321. 3225–3228. 5 indexed citations
11.
Masso, Majid, et al.. (2010). A combined sequence–structure approach for predicting resistance to the non-nucleoside HIV-1 reverse transcriptase inhibitor Nevirapine. Biophysical Chemistry. 153(2-3). 168–172. 10 indexed citations
12.
Yang, Shujiang, Mohammed Lach-hab, Iosif I. Vaisman, & Estela Blaisten‐Barojas. (2008). Machine Learning Approach for Classification of Zeolite Crystals.. 702–706.
13.
Luchini, Alessandra, David P. Long, Iosif I. Vaisman, et al.. (2008). Charge Transport Phenomena in Peptide Molecular Junctions. Journal of Nanotechnology. 2008(1). 1 indexed citations
14.
Masso, Majid & Iosif I. Vaisman. (2007). Accurate prediction of enzyme mutant activity based on a multibody statistical potential. Bioinformatics. 23(23). 3155–3161. 41 indexed citations
15.
Masso, Majid & Iosif I. Vaisman. (2007). A Novel Sequence-Structure Approach for Accurate Prediction of Resistance to HIV-1 Protease Inhibitors. 7. 952–958. 4 indexed citations
16.
Mathé, Ewy A., Magali Olivier, Shunsuke Kato, et al.. (2006). Predicting the transactivation activity of p53 missense mutants using a four-body potential score derived from Delaunay tessellations. Human Mutation. 27(2). 163–172. 19 indexed citations
17.
Pushkarsky, Tatiana, Vyacheslav Yurchenko, Christophe Vanpouille, et al.. (2005). Cell Surface Expression of CD147/EMMPRIN Is Regulated by Cyclophilin 60. Journal of Biological Chemistry. 280(30). 27866–27871. 66 indexed citations
18.
Barenboim, Maxim, D. Curtis Jamison, & Iosif I. Vaisman. (2005). Statistical geometry approach to the study of functional effects of human nonsynonymous SNPs. Human Mutation. 26(5). 471–476. 9 indexed citations
19.
Bostick, David & Iosif I. Vaisman. (2003). A new topological method to measure protein structure similarity. Biochemical and Biophysical Research Communications. 304(2). 320–325. 19 indexed citations
20.
Laiter, Sergei, et al.. (1995). Pseudotorsional OCCO backbone angle as a single descriptor of protein secondary structure. Protein Science. 4(8). 1633–1643. 10 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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