Krishna R. Kalari

9.5k total citations · 3 hit papers
131 papers, 5.5k citations indexed

About

Krishna R. Kalari is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Krishna R. Kalari has authored 131 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Molecular Biology, 54 papers in Cancer Research and 41 papers in Oncology. Recurrent topics in Krishna R. Kalari's work include Cancer Genomics and Diagnostics (27 papers), Estrogen and related hormone effects (14 papers) and Cancer Immunotherapy and Biomarkers (13 papers). Krishna R. Kalari is often cited by papers focused on Cancer Genomics and Diagnostics (27 papers), Estrogen and related hormone effects (14 papers) and Cancer Immunotherapy and Biomarkers (13 papers). Krishna R. Kalari collaborates with scholars based in United States, Japan and Sweden. Krishna R. Kalari's co-authors include Liewei Wang, Gregory D. Jenkins, Brooke L. Fridley, Liang Li, Nicholas Chia, Xiaojia Tang, Janet Yao, Jun Chen, Zhenkun Lou and Richard M. Weinshilboum and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Journal of Clinical Oncology.

In The Last Decade

Krishna R. Kalari

129 papers receiving 5.4k 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.

Multiple sclerosis patients have a distinct gut microbiota compared to healthy controls

2016 700 citations Krishna R. Kalari et al. profile →
FKBP51 Affects Cancer Cell Response to Chemotherapy by Negatively Regulating Akt

2009 568 citations Liang Li, Brooke L. Fridley et al. profile →
COVID-19 Transmission, Current Treatment, and Future Therapeutic Strategies

2021 184 citations Peter T. Vedell, Krishna R. Kalari et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Krishna R. Kalari United States	35	3.3k	1.3k	1.1k	618	532	131	5.5k
Hao Li China	34	3.4k 1.0×	1.1k 0.8×	1.4k 1.3×	856 1.4×	589 1.1×	257	6.5k
Rita Mancini Italy	36	2.1k 0.6×	956 0.7×	950 0.9×	411 0.7×	429 0.8×	199	4.3k
Anton Buzdin Russia	40	3.3k 1.0×	776 0.6×	953 0.9×	376 0.6×	789 1.5×	195	5.3k
Robert C. Stein United Kingdom	39	2.5k 0.8×	1.7k 1.3×	819 0.8×	829 1.3×	487 0.9×	110	5.7k
Yi Liu China	45	5.2k 1.6×	1.3k 1.0×	1.1k 1.0×	704 1.1×	1.5k 2.7×	247	8.4k
Surendra Dasari United States	48	5.2k 1.6×	723 0.6×	1.3k 1.2×	574 0.9×	467 0.9×	252	8.2k
Jinghui Zhang United States	42	3.5k 1.1×	1.0k 0.8×	988 0.9×	728 1.2×	695 1.3×	218	6.4k
Vito Michele Fazio Italy	39	3.3k 1.0×	779 0.6×	992 0.9×	924 1.5×	774 1.5×	167	5.3k
Hiroyuki Nishiyama Japan	38	2.3k 0.7×	1.2k 0.9×	621 0.6×	305 0.5×	557 1.0×	420	6.2k
Albert M. Levin United States	30	2.8k 0.8×	644 0.5×	924 0.9×	1.1k 1.8×	292 0.5×	116	5.8k

Countries citing papers authored by Krishna R. Kalari

Since Specialization

Citations

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

Fields of papers citing papers by Krishna R. Kalari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Krishna R. Kalari. 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 Krishna R. Kalari. The network helps show where Krishna R. Kalari may publish in the future.

Co-authorship network of co-authors of Krishna R. Kalari

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

All Works

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20 of 20 papers shown

Leon‐Ferre, Roberto A., Jodi M. Carter, David M. Zahrieh, et al.. (2024). Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer. npj Breast Cancer. 10(1). 25–25. 1 indexed citations

Thompson, Kevin J., et al.. (2024). QSP Modeling Shows Pathological Synergism Between Insulin Resistance and Amyloid‐Beta Exposure in Upregulating VCAM1 Expression at the BBB Endothelium. CPT Pharmacometrics & Systems Pharmacology. 14(3). 561–571.

Geist, Felix, Xiaojia Tang, Krishna R. Kalari, et al.. (2024). Identification of a Notch transcriptomic signature for breast cancer. Breast Cancer Research. 26(1). 4–4. 4 indexed citations

Wicik, Zofia, Kevin S. Pitel, Megan Weivoda, et al.. (2023). Estrogen-regulated miRs in bone enhance osteoblast differentiation and matrix mineralization. Molecular Therapy — Nucleic Acids. 33. 28–41. 3 indexed citations

Wu, Xinyan, Krishna R. Kalari, Xiaojia Tang, et al.. (2023). Endoxifen downregulates AKT phosphorylation through protein kinase C beta 1 inhibition in ERα+ breast cancer. npj Breast Cancer. 9(1). 101–101. 4 indexed citations

Sicotte, Hugues, Krishna R. Kalari, Sisi Qin, et al.. (2022). Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment. Molecular Cancer Research. 20(12). 1739–1750. 9 indexed citations

Tang, Xiaojia, Jianning Song, Upasana Ray, et al.. (2022). DNA barcoded competitive clone-initiating cell analysis reveals novel features of metastatic growth in a cancer xenograft model. NAR Cancer. 4(3). zcac022–zcac022. 1 indexed citations

Carter, Jodi M., Mei‐Yin C. Polley, Roberto A. Leon‐Ferre, et al.. (2021). Characteristics and Spatially Defined Immune (micro)landscapes of Early-stage PD-L1–positive Triple-negative Breast Cancer. Clinical Cancer Research. 27(20). 5628–5637. 35 indexed citations

Nair, Asha, Xiaojia Tang, Kevin J. Thompson, et al.. (2020). Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Triple-Negative Breast Cancer. iScience. 23(6). 101249–101249. 4 indexed citations

10.

Shee, Kevin, Matthew Ung, Nicole A. Traphagen, et al.. (2019). A Transcriptionally Definable Subgroup of Triple-Negative Breast and Ovarian Cancer Samples Shows Sensitivity to HSP90 Inhibition. Clinical Cancer Research. 26(1). 159–170. 1 indexed citations

11.

Liu, Duan, Sisi Qin, Balmiki Ray, et al.. (2018). Single Nucleotide Polymorphisms (SNPs) Distant from Xenobiotic Response Elements Can Modulate Aryl Hydrocarbon Receptor Function: SNP-Dependent CYP1A1 Induction. Drug Metabolism and Disposition. 46(9). 1372–1381. 13 indexed citations

12.

Vu, Trung Nghia, Quin F. Wills, Krishna R. Kalari, et al.. (2018). Isoform-level gene expression patterns in single-cell RNA-sequencing data. Bioinformatics. 34(14). 2392–2400. 12 indexed citations

13.

Kalari, Krishna R., Jason P. Sinnwell, Kevin J. Thompson, et al.. (2018). PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient. JCO Clinical Cancer Informatics. 2(2). 1–11. 11 indexed citations

14.

Tu, Xinyi, Mohamed M. Kahila, Qin Zhou, et al.. (2018). ATR Inhibition Is a Promising Radiosensitizing Strategy for Triple-Negative Breast Cancer. Molecular Cancer Therapeutics. 17(11). 2462–2472. 71 indexed citations

15.

Leon‐Ferre, Roberto A., Mei‐Yin C. Polley, Heshan Liu, et al.. (2017). Impact of histopathology, tumor-infiltrating lymphocytes, and adjuvant chemotherapy on prognosis of triple-negative breast cancer. Breast Cancer Research and Treatment. 167(1). 89–99. 73 indexed citations

16.

Ingle, James N., Fang Xie, Matthew J. Ellis, et al.. (2016). Genetic Polymorphisms in the Long Noncoding RNA MIR2052HG Offer a Pharmacogenomic Basis for the Response of Breast Cancer Patients to Aromatase Inhibitor Therapy. Cancer Research. 76(23). 7012–7023. 43 indexed citations

17.

Niu, Nifang, Daniel J. Schaid, Ryan Abo, et al.. (2012). Genetic association with overall survival of taxane-treated lung cancer patients - a genome-wide association study in human lymphoblastoid cell lines followed by a clinical association study. SHILAP Revista de lepidopterología. 28 indexed citations

18.

Asmann, Yan W., Brian M. Necela, Krishna R. Kalari, et al.. (2012). Detection of Redundant Fusion Transcripts as Biomarkers or Disease-Specific Therapeutic Targets in Breast Cancer. Cancer Research. 72(8). 1921–1928. 75 indexed citations

19.

Asmann, Yan W., Asif Hossain, Brian M. Necela, et al.. (2011). A novel bioinformatics pipeline for identification and characterization of fusion transcripts in breast cancer and normal cell lines. Nucleic Acids Research. 39(15). e100–e100. 80 indexed citations

20.

Li, Liang, Brooke L. Fridley, Krishna R. Kalari, et al.. (2008). Gemcitabine and Cytosine Arabinoside Cytotoxicity: Association with Lymphoblastoid Cell Expression. Cancer Research. 68(17). 7050–7058. 137 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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