Mukesh Bansal

7.0k total citations · 2 hit papers
59 papers, 3.7k citations indexed

About

Mukesh Bansal is a scholar working on Molecular Biology, Cancer Research and Pathology and Forensic Medicine. According to data from OpenAlex, Mukesh Bansal has authored 59 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 8 papers in Cancer Research and 7 papers in Pathology and Forensic Medicine. Recurrent topics in Mukesh Bansal's work include Gene Regulatory Network Analysis (13 papers), Bioinformatics and Genomic Networks (13 papers) and Microbial Metabolic Engineering and Bioproduction (6 papers). Mukesh Bansal is often cited by papers focused on Gene Regulatory Network Analysis (13 papers), Bioinformatics and Genomic Networks (13 papers) and Microbial Metabolic Engineering and Bioproduction (6 papers). Mukesh Bansal collaborates with scholars based in United States, Italy and India. Mukesh Bansal's co-authors include Diego di Bernardo, Vincenzo Belcastro, Alberto Ambesi‐Impiombato, Andrea Califano, Giusy Della Gatta, Archana Iyer, Pavel Sumazin, Paolo Guarnieri, Hua‐Sheng Chiu and Xuerui Yang and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Mukesh Bansal

56 papers receiving 3.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma

2011 580 citations Pavel Sumazin, Xuerui Yang et al. Cell profile →
How to infer gene networks from expression profiles

2007 555 citations Mukesh Bansal, Vincenzo Belcastro et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mukesh Bansal United States	25	2.9k	969	452	299	265	59	3.7k
Zheng Guo China	33	2.9k 1.0×	1.4k 1.5×	622 1.4×	619 2.1×	240 0.9×	210	4.2k
Pavel Sumazin United States	29	2.8k 1.0×	1.4k 1.4×	658 1.5×	411 1.4×	189 0.7×	68	4.1k
Edwin Wang Canada	31	2.1k 0.7×	936 1.0×	267 0.6×	271 0.9×	143 0.5×	87	3.2k
Alistair G. Rust United Kingdom	34	3.0k 1.0×	1.2k 1.3×	646 1.4×	313 1.0×	202 0.8×	76	4.4k
Kimberly J. Bussey United States	25	2.4k 0.8×	632 0.7×	768 1.7×	211 0.7×	131 0.5×	45	3.5k
Matan Hofree United States	18	2.0k 0.7×	684 0.7×	538 1.2×	441 1.5×	112 0.4×	25	3.1k
Özgür Şahin United States	33	2.5k 0.9×	1.6k 1.7×	647 1.4×	248 0.8×	131 0.5×	92	3.8k
Gabriela Alexe United States	31	2.2k 0.8×	924 1.0×	1.2k 2.6×	432 1.4×	165 0.6×	82	3.9k
Catharina Olsen Belgium	13	2.5k 0.8×	1.4k 1.4×	845 1.9×	965 3.2×	204 0.8×	43	3.6k
Bhavana Harsha United Kingdom	6	1.4k 0.5×	574 0.6×	323 0.7×	229 0.8×	201 0.8×	10	1.9k

Countries citing papers authored by Mukesh Bansal

Since Specialization

Citations

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

Fields of papers citing papers by Mukesh Bansal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mukesh Bansal

This figure shows the co-authorship network connecting the top 25 collaborators of Mukesh Bansal. A scholar is included among the top collaborators of Mukesh Bansal 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 Mukesh Bansal. Mukesh Bansal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Bansal, Mukesh, et al.. (2022). Performance Analysis of IPv4 and IPv6 in VANET Routing. 12. 1–5. 1 indexed citations

Kalac, Matko, Shi‐Xian Deng, Luigi Scotto, et al.. (2020). N-quinoline-benzenesulfonamide derivatives exert potent anti-lymphoma effect by targeting NF-κB. iScience. 23(12). 101884–101884.

Lorsch, Zachary S., Alberto Ambesi‐Impiombato, Irene Morganstern, et al.. (2020). Computational Analysis of Multidimensional Behavioral Alterations After Chronic Social Defeat Stress. Biological Psychiatry. 89(9). 920–928. 13 indexed citations

Bansal, Mukesh, Jing He, Michael Peyton, et al.. (2019). Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis. PLoS ONE. 14(1). e0208646–e0208646. 11 indexed citations

Garg, Ankur, et al.. (2018). FGF-induced Pea3 transcription factors program the genetic landscape for cell fate determination. PLoS Genetics. 14(9). e1007660–e1007660. 23 indexed citations

Bansal, Mukesh, et al.. (2018). Jaya Algorithm and Artificial Neural Network Based Approach for Object- Oriented Software Quality Analysis. International journal of intelligent engineering and systems. 11(4). 275–282. 3 indexed citations

Ambesi‐Impiombato, Alberto, Yue Qin, Daniel Herranz, et al.. (2017). Synergistic antileukemic therapies in NOTCH1-induced T-ALL. Clinical Cancer Research. 23(24). 65–66. 1 indexed citations

Zhang, Xu, Tapan K. Maity, Manoj Kumar Kashyap, et al.. (2017). Quantitative Tyrosine Phosphoproteomics of Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitor-treated Lung Adenocarcinoma Cells Reveals Potential Novel Biomarkers of Therapeutic Response. Molecular & Cellular Proteomics. 16(5). 891–910. 37 indexed citations

Udyavar, Akshata R., David J. Wooten, Mukesh Bansal, et al.. (2016). Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity. Cancer Research. 77(5). 1063–1074. 59 indexed citations

10.

Bisikirska, Brygida, Mukesh Bansal, Yao Shen, et al.. (2015). Elucidation and Pharmacological Targeting of Novel Molecular Drivers of Follicular Lymphoma Progression. Cancer Research. 76(3). 664–674. 37 indexed citations

11.

Gambardella, Gennaro, Ivana Peluso, Sandro Montefusco, et al.. (2015). A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data. BMC Bioinformatics. 16(1). 279–279. 6 indexed citations

12.

Sonabend, Adam M., Mukesh Bansal, Paolo Guarnieri, et al.. (2014). The Transcriptional Regulatory Network of Proneural Glioma Determines the Genetic Alterations Selected during Tumor Progression. Cancer Research. 74(5). 1440–1451. 41 indexed citations

13.

Sonabend, Adam M., Mukesh Bansal, Lei Liang, et al.. (2014). Convection-enhanced delivery of etoposide is effective against murine proneural glioblastoma. Neuro-Oncology. 16(9). 1210–1219. 29 indexed citations

14.

Ying, Carol Y., David Dominguez-Sola, Ivo C. Lorenz, et al.. (2013). MEF2B mutations lead to deregulated expression of the oncogene BCL6 in diffuse large B cell lymphoma. Nature Immunology. 14(10). 1084–1092. 122 indexed citations

15.

Sumazin, Pavel, Xuerui Yang, Hua‐Sheng Chiu, et al.. (2011). An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma. Cell. 147(2). 370–381. 580 indexed citations breakdown →

16.

Cantone, Irene, Lucia Marucci, Francesco Iorio, et al.. (2009). A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches. Cell. 137(1). 172–181. 277 indexed citations

17.

Gatta, Giusy Della, Mukesh Bansal, Alberto Ambesi‐Impiombato, et al.. (2008). Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Genome Research. 18(6). 939–948. 63 indexed citations

18.

Ambesi‐Impiombato, Alberto, Mukesh Bansal, Píetro Lió, & Diego di Bernardo. (2006). Computational framework for the prediction of transcription factor binding sites by multiple data integration. BMC Neuroscience. 7(S1). S8–S8. 13 indexed citations

19.

Amato, Francesco, et al.. (2005). Identification of Quadratic Nonlinear Models Oriented to Genetic Network Analysis. PubMed. 2005. 5615–5618. 3 indexed citations

20.

Bansal, Mukesh, Giusy Della Gatta, Jamey Wierzbowski, Timothy S. Gardner, & Diego di Bernardo. (2005). Discovering drug mode of action using reverse-engineered gene networks. PubMed. 158. 4739–4742. 1 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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