Sukriti Goyal

2.3k total citations
92 papers, 1.7k citations indexed

About

Sukriti Goyal is a scholar working on Molecular Biology, Computational Theory and Mathematics and Applied Mathematics. According to data from OpenAlex, Sukriti Goyal has authored 92 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 32 papers in Computational Theory and Mathematics and 21 papers in Applied Mathematics. Recurrent topics in Sukriti Goyal's work include Computational Drug Discovery Methods (30 papers), Analytic and geometric function theory (17 papers) and Cancer therapeutics and mechanisms (10 papers). Sukriti Goyal is often cited by papers focused on Computational Drug Discovery Methods (30 papers), Analytic and geometric function theory (17 papers) and Cancer therapeutics and mechanisms (10 papers). Sukriti Goyal collaborates with scholars based in India, Japan and Canada. Sukriti Goyal's co-authors include Abhinav Grover, H. M. Srivastava, Salma Jamal, K. C. Gupta, Chetna Tyagi, Jaspreet Kaur Dhanjal, Aditi Singh, Pranay Goswami, Sonam Grover and Asheesh Shanker and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Sukriti Goyal

86 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
Sukriti Goyal India 23 736 354 340 194 181 92 1.7k
Roelof Koekoek Netherlands 18 753 1.0× 203 0.6× 841 2.5× 98 0.5× 113 0.6× 27 2.2k
Pierre Leroux France 38 847 1.2× 218 0.6× 51 0.1× 197 1.0× 8 0.0× 181 5.2k
Xue-Mei Li China 16 368 0.5× 152 0.4× 355 1.0× 96 0.5× 47 0.3× 100 1.3k
Yanyan Li China 29 1.2k 1.6× 233 0.7× 325 1.0× 20 0.1× 6 0.0× 83 2.3k
Fukun Zhao China 25 524 0.7× 761 2.1× 1.1k 3.4× 27 0.1× 15 0.1× 102 2.1k
Yi Cheng China 34 1.1k 1.5× 46 0.1× 15 0.0× 620 3.2× 186 1.0× 210 3.9k
Alberto Abbondandolo Italy 24 642 0.9× 120 0.3× 249 0.7× 360 1.9× 8 0.0× 103 1.9k
Alain Schenkel Switzerland 6 778 1.1× 67 0.2× 38 0.1× 10 0.1× 76 0.4× 7 1.2k
Satoru Murakami Japan 26 816 1.1× 352 1.0× 1.1k 3.3× 110 0.6× 336 1.9× 131 2.8k
Guo‐Wei Wei United States 30 1.6k 2.2× 1.7k 4.8× 4 0.0× 61 0.3× 39 0.2× 85 3.2k

Countries citing papers authored by Sukriti Goyal

Since Specialization
Citations

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

Fields of papers citing papers by Sukriti Goyal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sukriti Goyal

This figure shows the co-authorship network connecting the top 25 collaborators of Sukriti Goyal. A scholar is included among the top collaborators of Sukriti Goyal 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 Sukriti Goyal. Sukriti Goyal 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.
Jamal, Salma, Sukriti Goyal, Asheesh Shanker, & Abhinav Grover. (2016). Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes. BMC Genomics. 17(1). 807–807. 31 indexed citations
2.
Nagpal, Neha, Sukriti Goyal, Jaspreet Kaur Dhanjal, et al.. (2016). Molecular dynamics-based identification of novel natural mortalin–p53 abrogators as anticancer agents. Journal of Receptors and Signal Transduction. 37(1). 8–16. 11 indexed citations
3.
Verma, Sharad, Sonam Grover, Chetna Tyagi, et al.. (2016). Hydrophobic Interactions Are a Key to MDM2 Inhibition by Polyphenols as Revealed by Molecular Dynamics Simulations and MM/PBSA Free Energy Calculations. PLoS ONE. 11(2). e0149014–e0149014. 77 indexed citations
4.
Goyal, Sukriti, et al.. (2016). Molecular modeling and lead design of substituted zanamivir derivatives as potent anti-influenza drugs. BMC Bioinformatics. 17(S19). 512–512. 6 indexed citations
5.
Gupta, Ankita, et al.. (2015). Abrogation of AuroraA-TPX2 by novel natural inhibitors: molecular dynamics-based mechanistic analysis. Journal of Receptors and Signal Transduction. 35(6). 626–633. 9 indexed citations
6.
Dhanjal, Jaspreet Kaur, Khushboo Bafna, Shashank P. Katiyar, et al.. (2015). Computational Structure-Based De Novo Design of Hypothetical Inhibitors against the Anti- Inflammatory Target COX-2. PLoS ONE. 10(8). e0134691–e0134691. 27 indexed citations
7.
Grover, Abhinav, Sukriti Goyal, Shashank P. Katiyar, et al.. (2015). Targeting Mortalin by Embelin Causes Activation of Tumor Suppressor p53 and Deactivation of Metastatic Signaling in Human Breast Cancer Cells. PLoS ONE. 10(9). e0138192–e0138192. 40 indexed citations
8.
Goyal, Sukriti, et al.. (2014). Group-based QSAR and molecular dynamics mechanistic analysis revealing the mode of action of novel piperidinone derived protein–protein inhibitors of p53-MDM2. Journal of Molecular Graphics and Modelling. 51. 64–72. 15 indexed citations
9.
Dhanjal, Jaspreet Kaur, et al.. (2013). Mechanistic insights into mode of action of potent natural antagonists of BACE-1 for checking Alzheimer’s plaque pathology. Biochemical and Biophysical Research Communications. 443(3). 1054–1059. 19 indexed citations
10.
Goyal, Sukriti, et al.. (2012). Extensions of sufficient conditions for starlikeness and convexity of order α for multivalent function. Applied Mathematics Letters. 25(11). 1993–1998. 3 indexed citations
11.
Goyal, Sukriti & Pranay Goswami. (2009). Certain coefficient inequalities for Sakaguchi type functions and applications to fractional derivative operator.. 19. 159–166. 6 indexed citations
12.
Johnsingh, A. J. T. & Sukriti Goyal. (2005). Tiger Conservation in India : the Past, Present and the Future. Indian Forester. 131(10). 1279–1296. 10 indexed citations
13.
Goyal, Sukriti & Ritu Goyal. (2004). A GENERAL THEOREM FOR THE GENERALIZED WEYL FRACTIONAL INTEGRAL OPERATOR INVOLVING THE MULTIVARIABLE H-FUNCTION. Taiwanese Journal of Mathematics. 8(4). 559–568.
14.
Goyal, Sukriti, et al.. (2004). On the Laplace Transform and the Generalized Weyl Fractional Integral Operator Involving the H-function. Kyungpook mathematical journal. 44(4). 481–481. 1 indexed citations
15.
Goyal, Sukriti, et al.. (2002). Some Results for Unified Riemann-Zeta Function. Kyungpook mathematical journal. 42(1). 87–87. 2 indexed citations
16.
Srivastava, H. M., et al.. (1990). Fractional integral operators involving a general class of polynomials. Journal of Mathematical Analysis and Applications. 148(1). 87–100. 4 indexed citations
17.
Srivastava, H. M. & Sukriti Goyal. (1985). Fractional derivatives of the H-function of several variables. Journal of Mathematical Analysis and Applications. 112(2). 641–651. 14 indexed citations
18.
Srivastava, H. M., K. C. Gupta, & Sukriti Goyal. (1982). The H-functions of one and two variables, with applications. 306 indexed citations
19.
Kalla, S. L., et al.. (1981). On multiple integral transformations. 15–27. 1 indexed citations
20.
Goyal, Sukriti, et al.. (1975). Comparative study of various methods used for staining the mast cells.. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 29(6-7). 173–6.

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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