Application of artificial intelligence for the prediction of amyloidoses

Authors

Michael P. Dunne (ORCID: https://orcid.org/0000-0001-7517-3860)
Andrey V. Kajava (ORCID: https://orcid.org/0000-0002-2342-6886)
Valentin Gonay (ORCID: https://orcid.org/0009-0001-6570-9593)

Institutions

Centre National de la Recherche Scientifique (FR)
Université de Montpellier (FR)
Medical Protective (US)

Publication Details

Journal: Neural Regeneration Research
Published: 2025-09-03
DOI: https://doi.org/10.4103/nrr.nrr-d-25-00482
Primary Topic: Artificial Intelligence in Healthcare
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Application of artificial intelligence for the prediction of amyloidoses

Michael P. Dunne, Andrey V. Kajava, Valentin Gonay

Sep 3, 2025

Neural Regeneration Research

Artificial Intelligence in Healthcare

article

Application of artificial intelligence for the prediction of amyloidoses

Michael P. Dunne, Andrey V. Kajava, Valentin Gonay

September 3, 2025article en

Abstract

No abstract available for this paper.

Neural Regeneration Research

Centre National de la Recherche Scientifique (FR), Université de Montpellier (FR), Medical Protective (US)

Openalex Percentile: Top 73%

Artificial Intelligence in Healthcare

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Application of artificial intelligence for the prediction of amyloidoses — Michael P. Dunne, Andrey V. Kajava, et al. · Neural Regeneration Research (2025) | TGRS Research Map | TGRS