Daniel Auclair

35.6k total citations · 1 hit paper
137 papers, 3.5k citations indexed

About

Daniel Auclair is a scholar working on Hematology, Molecular Biology and Cancer Research. According to data from OpenAlex, Daniel Auclair has authored 137 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Hematology, 62 papers in Molecular Biology and 22 papers in Cancer Research. Recurrent topics in Daniel Auclair's work include Multiple Myeloma Research and Treatments (67 papers), Protein Degradation and Inhibitors (25 papers) and Cancer Genomics and Diagnostics (17 papers). Daniel Auclair is often cited by papers focused on Multiple Myeloma Research and Treatments (67 papers), Protein Degradation and Inhibitors (25 papers) and Cancer Genomics and Diagnostics (17 papers). Daniel Auclair collaborates with scholars based in United States, France and Canada. Daniel Auclair's co-authors include Lan Bo Chen, Lauren Ambrogio, Philippe Lagrange, G. B. Mackaness, Hidefumi Sasaki, Louis H. Ferland, Sarah R. Walker, Michael C. Heinrich, Aaron McKenna and David A. Frank and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Daniel Auclair

133 papers receiving 3.4k 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.

Whole-exome sequencing identifies a recurrent NAB2-STAT6 fusion in solitary fibrous tumors

2013 422 citations Daniel Auclair et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Auclair United States	31	1.6k	817	807	421	385	137	3.5k
Sérgio Ferrari Italy	46	3.6k 2.3×	927 1.1×	962 1.2×	155 0.4×	236 0.6×	179	6.5k
Valentina Nardi United States	29	1.3k 0.8×	1.2k 1.4×	1.1k 1.4×	722 1.7×	384 1.0×	127	4.0k
Tor Knutsen Norway	32	1.0k 0.7×	350 0.4×	697 0.9×	398 0.9×	114 0.3×	97	3.3k
Joseph J. Catanese United States	34	1.3k 0.9×	425 0.5×	306 0.4×	146 0.3×	456 1.2×	64	4.7k
S Misawa Japan	31	1.4k 0.9×	2.5k 3.0×	597 0.7×	214 0.5×	153 0.4×	157	3.8k
Paul B. Yu United States	47	3.5k 2.3×	696 0.9×	600 0.7×	1.8k 4.2×	1.6k 4.1×	145	7.9k
David J. Curtis Australia	31	1.5k 1.0×	789 1.0×	429 0.5×	524 1.2×	65 0.2×	151	4.1k
M. Leonor Cancela Portugal	38	1.7k 1.1×	182 0.2×	207 0.3×	124 0.3×	434 1.1×	235	4.8k
Bruno Clément France	50	2.6k 1.7×	141 0.2×	1.4k 1.7×	763 1.8×	116 0.3×	183	7.8k
Pieter de Jong United States	17	1.8k 1.2×	130 0.2×	406 0.5×	1.1k 2.6×	282 0.7×	27	3.1k

Countries citing papers authored by Daniel Auclair

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Auclair

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Auclair

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Auclair. A scholar is included among the top collaborators of Daniel Auclair 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 Daniel Auclair. Daniel Auclair 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

Auclair, Daniel, Carol Mansfield, Mark A. Fiala, et al.. (2022). Preferences and Priorities for Relapsed Multiple Myeloma Treatments Among Patients and Caregivers in the United States. SHILAP Revista de lepidopterología. 9 indexed citations

Fernandez, Nicolas, Deepak Perumal, Adeeb Rahman, et al.. (2022). High Dimensional Immune Profiling of Smoldering Multiple Myeloma Distinguishes Distinct Tumor Microenvironments. Clinical Lymphoma Myeloma & Leukemia. 22(11). 853–862. 5 indexed citations

Boyle, Eileen M., Patrick Blaney, Yubao Wang, et al.. (2021). Unifying the Definition of High-Risk in Multiple Myeloma. Blood. 138(Supplement 1). 2714–2714.

D’Agostino, Mattia, Gian Maria Zaccaria, Bachisio Ziccheddu, et al.. (2020). Early Relapse Risk in Patients with Newly Diagnosed Multiple Myeloma Characterized by Next-generation Sequencing. Clinical Cancer Research. 26(18). 4832–4841. 38 indexed citations

Buadi, Francis K., Martha Q. Lacy, Gabriela Pérez, et al.. (2020). Phase 2 Trial of Pomalidomide, Ixazomib and Dexamethasone in Patients with Multiple Myeloma with Extramedullary Disease or Plasma Cell Leukemia. Blood. 136(Supplement 1). 34–35. 1 indexed citations

Soong, David, Jeran K. Stratford, Hervé Avet‐Loiseau, et al.. (2020). CNV Radar: an improved method for somatic copy number alteration characterization in oncology. BMC Bioinformatics. 21(1). 98–98. 11 indexed citations

Laganà, Alessandro, Dan Fu Ruan, David T. Melnekoff, et al.. (2018). Increased HLA-E Expression Correlates with Early Relapse in Multiple Myeloma. Blood. 132(Supplement 1). 59–59. 3 indexed citations

Furchtgott, Leon, Arnold Bolomsky, Fred K. Gruber, et al.. (2017). Multiple Myeloma Drivers of High Risk and Response to Stem Cell Transplantation Identified By Causal Machine Learning: Out-of-Cohort and Experimental Validation. Blood. 130. 3029–3029. 2 indexed citations

Nasser, Sara, Christophe Legendre, Daniel Auclair, et al.. (2017). Comprehensive Identification of Fusion Transcripts in Multiple Myeloma: An Mmrf Commpass Analysis. Blood. 130. 61–61. 4 indexed citations

10.

Legendre, Christophe, Jessica Aldrich, Sara Nasser, et al.. (2017). FGFR3 Mutations Are an Adverse Prognostic Factor in Patients with t(4;14)(p16;q32) Multiple Myeloma: An Mmrf Commpass Analysis. Blood. 130. 3027–3027. 6 indexed citations

11.

Barwick, Benjamin G., Vikas A. Gupta, Daniel Auclair, et al.. (2017). High-Risk Myeloma Is Demarcated By Immunoglobulin Lambda Light Chain Translocations. Blood. 130. 1780–1780. 1 indexed citations

12.

Auclair, Daniel, Carol Mansfield, Ajai Chari, et al.. (2017). Understanding the Preferences of Patients and Caregivers for Relapsed/Refractory Multiple Myeloma Treatment: A Mixed-Mode Patient-Centric Approach. Blood. 130. 5662–5662. 2 indexed citations

13.

Skerget, Sheri, Christophe Legendre, Jessica Aldrich, et al.. (2017). A Molecular Analysis of Cereblon-Related Immunomodulatory Drug Resistance in Commpass Multiple Myeloma Patients. Blood. 130. 1754–1754. 2 indexed citations

14.

Laganà, Alessandro, David T. Melnekoff, Violetta V. Leshchenko, et al.. (2017). Clonal Evolution in Newly Diagnosed Multiple Myeloma Patients: A Follow-up Study from the Mmrf Commpass Genomics Project. Blood. 130. 325–325. 2 indexed citations

15.

Salhia, Bodour, Angela Baker, Gregory Ahmann, et al.. (2010). DNA Methylation Analysis Determines the High Frequency of Genic Hypomethylation and Low Frequency of Hypermethylation Events in Plasma Cell Tumors. Cancer Research. 70(17). 6934–6944. 42 indexed citations

16.

Flichman, Guillermo, Marcello Donatelli, Eirik Romstad, et al.. (2006). Quantitative models of SEAMLESS-IF and procedures for up-and downscaling. Reports — Medical Cases Images and Videos. 4 indexed citations

17.

Chauhan, Dharminder, Daniel Auclair, Teru Hideshima, et al.. (2004). 2-Methoxyestardiol and bortezomib/proteasome-inhibitor overcome dexamethasone-resistance in multiple myeloma cells by modulating Heat Shock Protein-27. APOPTOSIS. 9(2). 149–155. 25 indexed citations

18.

Chauhan, Dharminder, Daniel Auclair, Teru Hideshima, et al.. (2002). Identification of genes regulated by Dexamethasone in multiple myeloma cells using oligonucleotide arrays. Oncogene. 21(9). 1346–1358. 134 indexed citations

19.

Sun, Yaping, Jinyang Zhang, Stine‐Kathrein Kraeft, et al.. (1999). Molecular Cloning and Characterization of Human Trabeculin-α, a Giant Protein Defining a New Family of Actin-binding Proteins. Journal of Biological Chemistry. 274(47). 33522–33530. 41 indexed citations

20.

Auclair, Daniel, et al.. (1995). Effect of active immunization against oestradiol in developing ram lambs on plasma gonadotrophin and testosterone concentrations, time of onset of puberty and testicular blood flow. Reproduction. 104(1). 7–16. 8 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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