ArticleBMC biology2025
DeepB
Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- DeepIR-Pred: identification of insulin receptors using metaheuristic optimization of biologically informed multi-view features with deep recurrent learning.Briefings in bioinformatics · 2026Article
- Brain peptides in Alzheimer's disease - pathophysiology and therapeutic advances.Cell and tissue research · 2026Review
- Brain-Targeting siRNA Delivery to Tackle Alzheimer's Disease.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe blood-brain barrier (B
resultsIn the proposed method, we used three types of novel features namely Pseduo residue energy content matric (PseRECM), graphical and statistical-based feature engineering (GSFE), and composition-transition and distribution (CTD)-based features. These features capture the energy-, graphical-, and compositional-based properties of from the primary peptide sequences. The data skewness is recognized as an inevitable issue that was tackled by employing a random under sampling technique. The extracted data were fed into various deep learning, i.e., stacked bidirectional gated recurrent unit (BiGRU), Deep Forest, and machine learning models, i.e., CatBoost, Support Vector Machine. BiGRU-based DeepB
conclusionWe believe our research will accelerate the peptide-based drug discovery for neurological diseases in particular.
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.