The chart shows the distribution of publications by all Research.com ranked scientists in the field of Medicine in 2026. The highlighted bar marks where Nicholas Topley sits on this spectrum.
This scientist: 217 publications — 4th percentile
4% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 1,796 publications or more.
The chart shows the D-index (discipline H-index) distribution of Medicine scientists ranked by Research.com in 2026. The highlighted bar marks where Nicholas Topley sits on this spectrum.
This scientist: 76 D-Index — 8th percentile
8% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 217 D-Index or more.
Nicholas Topley is affiliated with Cardiff University in the United Kingdom. Their research spans multiple fields, primarily focusing on medicine, health professions, and computer science. Within these broad areas, their work delves into subfields such as artificial intelligence, immunology, nephrology, oncology, and health information management.
The scientist's publication record includes recent papers addressing topics related to dialysis, immune response, cytokine signaling, and machine learning applications in healthcare. Notable papers published by Nicholas Topley include:
Topley's frequent collaborators include Matthias Eberl, David Millrine, Ana Cardus Figueras, Javier Uceda Fernandez, and Robert Andrews. Collaboration with these co-authors has produced multiple publications across different research topics.
The venues where Nicholas Topley publishes underline the diversity of their research. These include bioRxiv (Cold Spring Harbor Laboratory), Kidney International, The Journal of Immunology, PLoS ONE, and Bioinformatics Advances.
The principal topics addressed in their research cover:
Nicholas Topley's work reflects an interdisciplinary approach combining clinical medicine with advanced computational techniques. Their focus on diagnostic technologies, inflammatory and immune mechanisms, and the application of interpretable machine learning methods highlights their contributions to both biomedical research and healthcare innovation.
John David Williams;Kathrine Jane Craig;Nicholas Topley;Christopher John Von Ruhland
Suzanne Maria Hurst;Thomas S. Wilkinson;Rachel Mary McLoughlin;Suzanne Jones
Simon A. Jones;Sankichi Horiuchi;Nicholas Topley;Naoki Yamamoto
Ceri Alan Fielding;Rachel Mary McLoughlin;Louise McLeod;Chantal Sophie Colmont
John David Williams;Nicholas Topley;Kathrine Jane Craig;Ruth Kathryn MacKenzie
Yoshindo Kawaguchi;Hideki Kawanishi;Salim Mujais;Nicholas Topley
Rachel Mary McLoughlin;Brendan J. Jenkins;Dianne Grail;Anwen Sian Williams
Ceri A. Fielding;Gareth Wyn Jones;Rachel Mary McLoughlin;Louise McLeod
Heinfried H. Radeke;Beate Meier;Nicholas Topley;Jürgen Flöge
Brendan John Jenkins;Dianne Grail;Thao Nheu;Meri Najdovska
Mari Ann Nowell;Peter James Richards;Sankichi Horiuchi;Naoki Yamamoto
Rachel Mary McLoughlin;Janusz Witowski;Rachel L. Robson;Thomas S. Wilkinson
Kawaguchi Y;Kawanishi H;Mujais S;Topley N
Olivier Devuyst;Peter J. Margetts;Nicholas Topley
Nicholas Topley;Achim Jörres;Werner Luttmann;Meryl M. Petersen
Mari Ann Nowell;Anwen Sian Williams;Sara Madelaine Carty;Jurgen Scheller
Xiao Liang Zhang;Nicholas Topley;Takafumi Ito;Aled Owain Phillips
Mari Ann Nowell;Peter James Richards;Ceri Alan Fielding;Simona Ognjanovic
Gareth Wyn Jones;Rachel Mary McLoughlin;Victoria Jayne Hammond;Clare Rhian Parker
Nicholas Topley;Haifa M.M. Alobaidi;Malcolm Davies;Gerald A. Coles
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