ML Disis

816 citations
20 papers · 699 · h-index 7

Impact in

  • Immunology top 5%
    • Immunotherapy and Immune Responses
    • T-cell and B-cell Immunology
    • Immune Cell Function and Interaction
  • Virology top 10%

Papers in

ML Disis

19 papers receiving 688 citations

Peers

ML Disis
Comparison fields: 5 of 55
  • Immunology 579
  • Virology 42
  • Oncology 229
  • Radiology, Nuclear Medicine and Imaging 184
  • Molecular Biology 264
Replace Antonio Scardino with:
Antonio Scardino France
Gemma Pidelaserra-Martí Germany
Adam Gulbranson‐Judge United Kingdom
Konstadinos Kosmatopoulos France
Jordana Griffiths United Kingdom
David J. Kittlesen United States
Yanal Murad Canada
Teresa A. Colella United States
Nadine Bizouarne France
Peter Molloy United Kingdom
ML Disis relative to Antonio Scardino France Antonio Scardino's profile →
Citations per field
00.5×1.5×2.1×
Antonio Scardino · 1×
Citations per year

Countries citing papers authored by ML Disis

Since Specialization
Citations

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

Fields of papers citing papers by ML Disis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside ML Disis, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with ML Disis Line = papers co-authored together ML Disis links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 1996260
2 1996227
3 1996158
4 20159
5 20087
6 20167
7 20177
8 20054
9 20043
10 20082
11 20062
12 20122
13 20042
14 20162
15 20042
16 20061
17 20111
18 20111
19 20111
20 20081

About ML Disis

ML Disis is a scholar working on Immunology, Radiology, Nuclear Medicine and Imaging, Molecular Biology, Oncology and Cancer Research, having authored 20 papers that have together received 699 indexed citations. Recurring topics across this work include Immunotherapy and Immune Responses (13 papers), Monoclonal and Polyclonal Antibodies Research (10 papers), Cancer Immunotherapy and Biomarkers (8 papers), vaccines and immunoinformatics approaches (5 papers), Cancer Genomics and Diagnostics (4 papers), Cancer Research and Treatments (3 papers), Ovarian cancer diagnosis and treatment (3 papers) and Reproductive System and Pregnancy (2 papers). The work is most often cited by research in Immunology (579 citations), Virology (42 citations), Oncology (229 citations), Radiology, Nuclear Medicine and Imaging (184 citations) and Molecular Biology (264 citations). ML Disis has collaborated with scholars based in United States. Frequent co-authors include H. Bernhard, Steven Gillis, JR Gralow, Martin A. Cheever, Julie R. Gralow, Susan L. Hand, Jennifer S. Childs, Casey Cunningham, John Fikes and Glenn Ishioka. Their work appears in journals such as Journal of Clinical Oncology, Cancer Research, Blood, International Journal of Gynecological Cancer and The Journal of Immunology.

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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