AI and Neurological Rare & Neuroimmune Diseases ── 2026-11-06
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（1） · 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
MOTIVATION: Amyloids are predominantly β-sheet-rich, stable protein structures that can maintain their presence in the human body for multiple years. Amyloid protein aggregates contribute to the development of multiple neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, and are involved in different vital functions, such as memory formation and immune system function. RESULTS: Here, we used advanced machine learning and deep learning techniques to predict amyloid propensity from the amino acid sequence. First, we aggregated labeled amino acid sequence data from multiple sources, obtaining a roughly balanced dataset of 2365 sequences for binary classification. We leveraged that data to both fine-tune the ESM2 model and to train new models based on protein embeddings from ESM2 and UniRep. The predictions from these models were then unified into a single soft voting ensemble model, yielding highly robust and accurate results. We further made a tool where users can provide the amino acid sequence and get the amyloid formation probabilities of different segments of the input sequence. AVAILABILITY AND IMPLEMENTATION: Users can access the light version of AmyloDeep through the web server at https://amylodeep.com/, and the full model is available as a Python package at https://pypi.org/project/amylodeep/.
Journal IF-equivalent: 3.1 (OpenAlex 2-year mean citedness, value as of 2026-11-05, retrieved 2026-11-06; not the official Clarivate IF)Reference: Davtyan A, Khachatryan A, Petrosyan R. AmyloDeep: protein language model-based ensemble model for predicting amyloid propensity from the amino acid sequence. Bioinformatics Advances. 2026 Oct 6;6(1):vbag296. doi:10.1093/bioadv/vbag296.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
- Abstract
Parkinson's disease (PD) is a growing public health challenge as populations age. Current diagnosis relies on motor symptoms and invasive procedures, making early detection difficult. Here we present Quest2Dx, a generalized artificial intelligence model that analyzes health questionnaires to enable low-cost, non-invasive PD screening. Conventional models are built for each questionnaire, and missing responses degrade their performance. Quest2Dx uses language modeling to encode the meaning of each question and answer, allowing it to transfer across questionnaires while enhancing interpretability. On the Parkinson's Progression Markers Initiative (PPMI) Online and Fox Insight datasets, Quest2Dx achieved areas under the receiver operating characteristic curve (AUROCs) of 0.977 and 0.974, respectively, outperforming existing methods. Additionally, cross-questionnaire validation achieved AUROCs of 0.920 and 0.952, respectively, from PPMI Online to Fox Insight and vice versa. Quest2Dx also identified key predictors from the questions to provide further insights. These results indicate a promising path to PD screening in primary care.
Journal IF-equivalent: 19.0 (OpenAlex 2-year mean citedness, value as of 2026-11-05, retrieved 2026-11-06; not the official Clarivate IF)Reference: Machado Reyes D, Hahn J, Shen L, Yan P. Language modeling screens Parkinson’s disease with self-reported questionnaires. Nat Aging. 2026 Nov 4 [Epub ahead of print]. doi:10.1038/s43587-026-01260-6.Checked: Abstract only
See all 1 Parkinson's papers →
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.