Praneeth Sudalagunta

446 total citations
20 papers, 74 citations indexed

About

Praneeth Sudalagunta is a scholar working on Molecular Biology, Hematology and Oncology. According to data from OpenAlex, Praneeth Sudalagunta has authored 20 papers receiving a total of 74 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 11 papers in Hematology and 4 papers in Oncology. Recurrent topics in Praneeth Sudalagunta's work include Multiple Myeloma Research and Treatments (10 papers), Protein Degradation and Inhibitors (5 papers) and Cancer Genomics and Diagnostics (3 papers). Praneeth Sudalagunta is often cited by papers focused on Multiple Myeloma Research and Treatments (10 papers), Protein Degradation and Inhibitors (5 papers) and Cancer Genomics and Diagnostics (3 papers). Praneeth Sudalagunta collaborates with scholars based in United States and Brazil. Praneeth Sudalagunta's co-authors include Rakesh K. Kapania, Pradeep Raj, Cornel Sultan, Layne T. Watson, Kenneth H. Shain, Rafael Renatino Canevarolo, Mark B. Meads, Ariosto S. Silva, Rachid Baz and Robert A. Gatenby and has published in prestigious journals such as Nature Communications, Blood and Bioinformatics.

In The Last Decade

Praneeth Sudalagunta

17 papers receiving 74 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Praneeth Sudalagunta United States 5 22 21 21 16 14 20 74
Olga Bandman Russia 6 6 0.3× 28 1.3× 4 0.2× 1 0.1× 7 0.5× 20 122
Renying Wang China 7 6 0.3× 65 3.1× 4 0.2× 13 0.9× 19 170
Ziwei Liu China 7 5 0.2× 51 2.4× 5 0.3× 9 0.6× 26 137
Dilan Pathirana Germany 5 3 0.1× 54 2.6× 11 0.5× 8 0.6× 12 123
Adam Kowalewski Poland 9 7 0.3× 21 1.0× 26 1.2× 1 0.1× 24 1.7× 48 282
Zhehui Chen China 7 4 0.2× 37 1.8× 3 0.1× 2 0.1× 23 1.6× 30 138
Ilaria Gandoglia Italy 6 5 0.2× 13 0.6× 10 0.6× 36 2.6× 13 133
Annie Xie United States 4 4 0.2× 13 0.6× 13 0.6× 7 0.5× 8 52
Yongxiang Liu China 6 5 0.2× 155 7.4× 1 0.0× 13 0.8× 12 0.9× 19 258

Countries citing papers authored by Praneeth Sudalagunta

Since Specialization
Citations

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

Fields of papers citing papers by Praneeth Sudalagunta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Praneeth Sudalagunta

This figure shows the co-authorship network connecting the top 25 collaborators of Praneeth Sudalagunta. A scholar is included among the top collaborators of Praneeth Sudalagunta 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 Praneeth Sudalagunta. Praneeth Sudalagunta 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.
Persi, Erez, Praneeth Sudalagunta, Yuri I. Wolf, et al.. (2025). Genome-level selection in tumors as a universal marker of resistance to therapy. Nature Communications. 16(1). 6535–6535. 1 indexed citations
2.
Bishop, Ryan T., Tao Li, Praneeth Sudalagunta, et al.. (2024). Acid ceramidase controls proteasome inhibitor resistance and is a novel therapeutic target for the treatment of relapsed / refractory multiple myeloma. Haematologica. 110(6). 1351–1367.
3.
Meads, Mark B., Xiaohong Zhao, David Noyes, et al.. (2024). De Novo Resistance and Relapse from Daratumumab Monotherapy in NDMM Is Associated with Immune Evasion and Immunosuppression. Blood. 144(Supplement 1). 1906–1906.
4.
Ionescu, Filip, Pragi Patel, Praneeth Sudalagunta, et al.. (2024). Activity and Safety of Venetoclax (VEN)-Based Treatment in t(11;14)-Positive Relapsed/Refractory Multiple Myeloma (R/R MM). Blood. 144(Supplement 1). 4740–4740.
5.
Li, Lin, Xiao Hu, Maciej Kmieciak, et al.. (2024). Combined MEK1/2 and ATR inhibition promotes myeloma cell death through a STAT3‐dependent mechanism in vitro and in vivo. British Journal of Haematology. 205(6). 2338–2348. 2 indexed citations
6.
Bishop, Ryan T., Tao Li, Jeremy S. Frieling, et al.. (2024). The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous drug resistant disease. Nature Communications. 15(1). 2458–2458. 4 indexed citations
7.
8.
Canevarolo, Rafael Renatino, Mark B. Meads, Xiaohong Zhao, et al.. (2023). Ex Vivo Mathematical Myeloma Advisor (EMMA) - a Clinical, Molecular, and Phenotypic Platform to Tailor Personalized Therapeutic Strategies for Multiple Myeloma. Blood. 142(Supplement 1). 2280–2280. 1 indexed citations
9.
Canevarolo, Rafael Renatino, et al.. (2022). Glutathione levels are associated with methotrexate resistance in acute lymphoblastic leukemia cell lines. Frontiers in Oncology. 12. 1032336–1032336. 7 indexed citations
10.
Sudalagunta, Praneeth, Carolina Silva, Rafael Renatino Canevarolo, et al.. (2022). CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation. Bioinformatics. 38(16). 4002–4010. 2 indexed citations
11.
Shain, Kenneth H., Rafael Renatino Canevarolo, Mark B. Meads, et al.. (2020). Characterization of Synergistic Selinexor Combinations of Dexamethasone, Pomalidomide, Elotuzumab and Daratumumab in Primary MM Samples Ex Vivo. Blood. 136(Supplement 1). 29–30. 1 indexed citations
12.
Sudalagunta, Praneeth, Maria Cláudia Silva, Rafael Renatino Canevarolo, et al.. (2020). A pharmacodynamic model of clinical synergy in multiple myeloma. EBioMedicine. 54. 102716–102716. 16 indexed citations
13.
Canevarolo, Rafael Renatino, Mark B. Meads, Maria João Silva, et al.. (2020). Dynamic Epigenetic Landscapes Define Multiple Myeloma Progression and Drug Resistance. Blood. 136(Supplement 1). 32–33. 1 indexed citations
14.
Canevarolo, Rafael Renatino, Mark B. Meads, Praneeth Sudalagunta, et al.. (2020). Ex Vivo Drug Sensitivity and Functional Genomics Platform Identifies Novel Combinations Targeting Intrinsic and Extrinsic Apoptotic Signaling Pathways in Multiple Myeloma. Blood. 136(Supplement 1). 49–50. 1 indexed citations
15.
Shain, Kenneth H., Daniel P. Hart, Praneeth Sudalagunta, et al.. (2019). Reinforcement Learning to Optimize the Treatment of Multiple Myeloma. Blood. 134(Supplement_1). 5511–5511. 3 indexed citations
16.
Sudalagunta, Praneeth, Cornel Sultan, Rakesh K. Kapania, Layne T. Watson, & Pradeep Raj. (2018). Aeroelastic Control-Oriented Modeling of an Airbreathing Hypersonic Vehicle. Journal of Guidance Control and Dynamics. 41(5). 1136–1149. 16 indexed citations
17.
18.
Sudalagunta, Praneeth, Cornel Sultan, Rakesh K. Kapania, Layne T. Watson, & Pradeep Raj. (2016). Accurate Computing of Higher Vibration Modes of Thin Flexible Structures. AIAA Journal. 54(5). 1704–1718. 6 indexed citations
19.
Sudalagunta, Praneeth, Cornel Sultan, Rakesh K. Kapania, Layne T. Watson, & Pradeep Raj. (2016). Aeroelastic Control-oriented Modeling of an Air-breathing Hypersonic Vehicle. 8 indexed citations
20.
Sudalagunta, Praneeth, Cornel Sultan, Rakesh K. Kapania, Layne T. Watson, & Pradeep Raj. (2015). A Novel Scheme to Accurately Compute Higher Vibration Modes using the Ritz Method and a Two-point BVP Solver. 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 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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