ReviewBiomolecules2021
Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts.
Review in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- AntiLipo: A Comprehensive Database, Deep Learning-Based Prediction Model, and Computational Study of Anti-Hyperlipidemic Peptides.Interdisciplinary sciences, computational life sciences · 2026Article
- GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- A self-attention-based deep learning model for identifying key genes in insect pupal metamorphosis.BMC genomics · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Fine-tuned GPT-based foundation models effectively reconstruct bacterial transcriptional regulatory networks from literature.Frontiers in artificial intelligence · 2026Article
- A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Large language models assisted multi-effect variants mining on cerebral cavernous malformation familial whole genome sequencing.Computational and structural biotechnology journal · 2024Article
- MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification.BMC bioinformatics · 2024Article
- Major Phytochemicals: Recent Advances in Health Benefits and Extraction Method.Molecules (Basel, Switzerland) · 2023Review
- A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites.Biomolecules · 2022Article
- Extraction of entity relations from Chinese medical literature based on multi-scale CRNN.Annals of translational medicine · 2022Article
- Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?Current issues in molecular biology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large-scale molecular interaction maps and database curation. Text mining and Natural-Language-Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and Machine-Learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention-based models, a special type of Neural-Network (NN)-based architecture that has recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at the sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conducted a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.
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.