AI and Neurological Rare & Neuroimmune Diseases ── 2026-10-20
New papers on neurological rare and neuroimmune diseases and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 9 disease groups, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by disease: Basic research（1） · ALS（0） · Parkinson's（0） · Hereditary neurodegeneration（0） · Neuromuscular（0） · Multiple sclerosis（0） · NMOSD/MOGAD（0） · Myasthenia gravis（0） · Autoimmune encephalitis/CIDP/GBS（0）
Neurodegeneration and neuroimmunology research and AI
- Abstract
Protein aggregation is a critical phenomenon associated with numerous neurodegenerative and systemic diseases. Understanding the propensity of proteins to aggregate is essential for unraveling the molecular basis of these disorders and for the design and engineering of novel proteins, as well as for modulating the activity/stability of enzymatic proteins. Here, we present APR-Score, a novel machine learning-based computational method designed to identify aggregation-prone regions within protein sequences. APR-Score leverages a combination of sequence-based features to predict regions of proteins that are prone to aggregation. It harnessed the information embedded in compiled sequence and structural features to achieve state-of-the-art accuracy. The method was evaluated through rigorous cross-validation on the training dataset and further validated on an independent test dataset. APR-Score demonstrated robustness and reliability in distinguishing aggregation-prone regions from non-aggregating ones, achieving a Matthews correlation coefficient (MCC) of 0.77, precision of 0.89, and F1-Score of 0.87. This tool offers significant value for researchers investigating protein aggregation-related diseases, as it can expedite the identification of aggregation-prone regions, thereby aiding the development of targeted therapies and diagnostic tools. Moreover, computational protein design and engineering efforts can be facilitated by APR-Score-based identification and screening of aggregation-prone protein sequences. APR-Score is available at https://github.com/TII-BRC/APR-Score.
Journal IF-equivalent: 6.4 (OpenAlex 2-year mean citedness, value as of 2026-10-19, retrieved 2026-10-20; not the official Clarivate IF)Reference: Kaushik R, Launey T. APR-Score: Identification and Scoring of Aggregation-prone Regions in Protein Sequences. Genomics, Proteomics & Bioinformatics. 2026 Oct 19 [Epub ahead of print]. doi:10.1093/gpbjnl/qzag104.Checked: Abstract only
See all 1 Basic research papers →
ALS: treatment, trials and AI
No new qualifying paper for this issue.
Parkinson's disease and parkinsonism: treatment, trials and AI
No new qualifying paper for this issue.
Hereditary neurodegenerative diseases: treatment, trials and AI
No new qualifying paper for this issue.
Neuromuscular diseases (muscular dystrophy, SMA): treatment, trials and AI
No new qualifying paper for this issue.
Multiple sclerosis: treatment, trials and AI
No new qualifying paper for this issue.
NMOSD and MOGAD: treatment, trials and AI
No new qualifying paper for this issue.
Myasthenia gravis: treatment, trials and AI
No new qualifying paper for this issue.
Autoimmune encephalitis, CIDP and Guillain-Barre syndrome: treatment, trials and AI
No new qualifying paper for this issue.