ArticlePeerJ. Computer science2025
Predicting amyloid proteins using attention-based long short-term memory.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is one of the genetically inherited neurodegenerative disorders that mostly occur when people get old. It can be recognized by severe memory impairment in the late stage, affecting cognitive function and general daily living. Reliable evidence confirms that the enhanced symptoms of AD are linked to the accumulation of amyloid proteins. The dense population of amyloid proteins forms insoluble fibrillar structures, causing significant pathological impacts in various tissues. Understanding amyloid protein's mechanisms and identifying them at an early stage plays an essential role in treating AD as well as prevalent amyloid-related diseases. Recently, although several machine learning methods proposed for amyloid protein identification have shown promising results, most of them have not yet fully exploited the sequence information of the amyloid proteins. In this study, we develop a computational model for
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.