Ariella Sasson

4.4k total citations · 1 hit paper
16 papers, 967 citations indexed

About

Ariella Sasson is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Ariella Sasson has authored 16 papers receiving a total of 967 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 5 papers in Immunology and 5 papers in Cancer Research. Recurrent topics in Ariella Sasson's work include Cancer Genomics and Diagnostics (5 papers), T-cell and B-cell Immunology (4 papers) and Genomics and Rare Diseases (3 papers). Ariella Sasson is often cited by papers focused on Cancer Genomics and Diagnostics (5 papers), T-cell and B-cell Immunology (4 papers) and Genomics and Rare Diseases (3 papers). Ariella Sasson collaborates with scholars based in United States, Germany and Australia. Ariella Sasson's co-authors include Joseph D. Szustakowski, William J. Geese, Ryan Golhar, Chang Han, Naiyer A. Rizvi, Giovanni Selvaggi, Scott Antonia, Fred R. Hirsch, Margaret K. Callahan and Paolo A. Ascierto and has published in prestigious journals such as Bioinformatics, Cancer Cell and Cancer Research.

In The Last Decade

Ariella Sasson

15 papers receiving 957 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.

Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer

2018 670 citations Matthew D. Hellmann, Margaret K. Callahan et al. Cancer Cell profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ariella Sasson United States	9	632	267	250	213	210	16	967
Kirsten Kübler Germany	15	339 0.5×	344 1.3×	73 0.3×	88 0.4×	243 1.2×	27	924
Tomoki Yamano Japan	18	540 0.9×	364 1.4×	126 0.5×	74 0.3×	91 0.4×	54	972
Lerong Li United States	14	827 1.3×	699 2.6×	215 0.9×	232 1.1×	175 0.8×	21	1.4k
David Heinz United States	15	409 0.6×	266 1.0×	103 0.4×	286 1.3×	69 0.3×	20	973
Arnavaz Danesh Canada	9	332 0.5×	838 3.1×	149 0.6×	113 0.5×	258 1.2×	20	1.3k
Satoshi Amada Japan	21	276 0.4×	402 1.5×	155 0.6×	232 1.1×	119 0.6×	38	1.5k
Sunita Badola United States	9	233 0.4×	207 0.8×	131 0.5×	82 0.4×	103 0.5×	16	729
Tsui‐Lien Mao Taiwan	14	321 0.5×	500 1.9×	161 0.6×	93 0.4×	188 0.9×	24	1.0k
Tong Ding China	11	386 0.6×	283 1.1×	107 0.4×	85 0.4×	242 1.2×	17	943
Dan Sha China	7	372 0.6×	204 0.8×	210 0.8×	45 0.2×	211 1.0×	16	644

Countries citing papers authored by Ariella Sasson

Since Specialization

Citations

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

Fields of papers citing papers by Ariella Sasson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ariella Sasson

This figure shows the co-authorship network connecting the top 25 collaborators of Ariella Sasson. A scholar is included among the top collaborators of Ariella Sasson 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 Ariella Sasson. Ariella Sasson 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:

16 of 16 papers shown

Srinivasan, Sujaya, Natallia Kalinava, Rafael Aldana, et al.. (2020). Misannotated Multi-Nucleotide Variants in Public Cancer Genomics Datasets Lead to Inaccurate Mutation Calls with Significant Implications. Cancer Research. 81(2). 282–288. 7 indexed citations

Kalinava, Natallia, Sujaya Srinivasan, Ming‐Shan Chien, et al.. (2020). Modeling Performance of Sample Collection Sites Using Whole Exome Sequencing Metrics. BioTechniques. 69(6). 421–427. 2 indexed citations

Kirov, Stefan, Ariella Sasson, Scott D. Chasalow, et al.. (2019). Degradation of the extracellular matrix is part of the pathology of ulcerative colitis. Molecular Omics. 15(1). 67–76. 25 indexed citations

Han, Chang, Ariella Sasson, Sujaya Srinivasan, et al.. (2019). Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer. Molecular Diagnosis & Therapy. 23(4). 507–520. 38 indexed citations

Hellmann, Matthew D., Margaret K. Callahan, Mark M. Awad, et al.. (2018). Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer. Cancer Cell. 33(5). 853–861.e4. 670 indexed citations breakdown →

Chang, Han, Sujaya Srinivasan, Ariella Sasson, et al.. (2018). Toward the standardization of bioinformatics methods for the accurate assessment of tumor mutational burden (TMB). Annals of Oncology. 29. viii20–viii21. 1 indexed citations

Dapprich, Johannes, Deborah Ferriola, Kate Mackiewicz, et al.. (2016). The next generation of target capture technologies - large DNA fragment enrichment and sequencing determines regional genomic variation of high complexity. BMC Genomics. 17(1). 486–486. 52 indexed citations

Pinney, Sara E., Karthik Ganapathy, Jonathan P. Bradfield, et al.. (2013). Dominant Form of Congenital Hyperinsulinism Maps to HK1 Region on 10q. Hormone Research in Paediatrics. 80(1). 18–27. 53 indexed citations

Duke, Jamie L., Ariella Sasson, Kate Mackiewicz, et al.. (2013). 48-OR. Human Immunology. 74. 38–38.

10.

Zhang, Zhe, Jeremy Leipzig, Ariella Sasson, et al.. (2013). Efficient digest of high-throughput sequencing data in a reproducible report. BMC Bioinformatics. 14(S11). S3–S3. 8 indexed citations

11.

Santis, Dianne De, Jamie L. Duke, Cherie L. Holcomb, et al.. (2013). 16^th IHIW : Review of HLA typing by NGS. International Journal of Immunogenetics. 40(1). 72–76. 62 indexed citations

12.

Lind, Curt, Kate Mackiewicz, Jamie L. Duke, et al.. (2012). 19-OR. Human Immunology. 73. 16–16. 1 indexed citations

13.

Lind, Curt, Deborah Ferriola, Kate Mackiewicz, et al.. (2012). Filling the gaps – The generation of full genomic sequences for 15 common and well-documented HLA class I alleles using next-generation sequencing technology. Human Immunology. 74(3). 325–329. 14 indexed citations

14.

Lind, Curt, Kate Mackiewicz, Jamie L. Duke, et al.. (2012). 137-P. Human Immunology. 73. 135–135. 2 indexed citations

15.

Sasson, Ariella & Todd P. Michael. (2010). Filtering error from SOLiD Output. Bioinformatics. 26(6). 849–850. 26 indexed citations

16.

Brown, William M., Ariella Sasson, Donald R. Bellew, et al.. (2008). Efficient Calculation of Molecular Properties from Simulation Using Kernel Molecular Dynamics. Journal of Chemical Information and Modeling. 48(8). 1626–1637. 6 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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