ReviewDrug discovery today2021
Deep learning in next-generation sequencing.
Review in Drug discovery today, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- The CDC/APHL next generation sequencing quality initiative: practical guidance to implementing quality management systems in laboratories performing next generation sequencing (phase 2).Journal of clinical microbiology · 2026Review
- Hematopathology Practice in the Digital Era: What has Changed?International journal of laboratory hematology · 2026Review
- Transformative Applications and Innovations in Next-Generation Sequencing Data Analysis.Journal of applied genetics · 2026Review
- Potential Applications of Genome-Wide Association Studies in Establishing Climate Resilience in Livestock: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Review
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- Using extension-based mRNA display to design antibody-like proteinogenic peptides for human PD-L1.Protein science : a publication of the Protein Society · 2025Article
- The Ongoing Struggle to Find a Gold Standard for PJI Diagnosis.Reports (MDPI) · 2025Review
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- MPSE identifies newborns for whole genome sequencing within 48 h of NICU admission.NPJ genomic medicine · 2025Article
- Genome-resolved metagenomics from short-read sequencing data in the era of artificial intelligence.Functional & integrative genomics · 2025Review
- DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.Briefings in bioinformatics · 2025Article
- Review
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Artificial Intelligence and Chromothripsis.Methods in molecular biology (Clifton, N.J.) · 2025Review
- En masse evaluation of RNA guides (EMERGe) for ADARs.Methods in enzymology · 2025Article
- Decoding the mystery: AI-assisted bioinformatics and functional genomics technologies in medicinal plants.Frontiers in plant science · 2025Article
- A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.Functional & integrative genomics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addressing questions such as variant calling, metagenomic classification and quantification, genomic feature detection, or downstream analysis in larger biological or medical contexts. In addition to classical algorithmic approaches, machine-learning (ML) techniques are often used for such tasks. In particular, deep learning (DL) methods that use multilayered artificial neural networks (ANNs) for supervised, semisupervised, and unsupervised learning have gained significant traction for such applications. Here, we highlight important network architectures, application areas, and DL frameworks in a NGS context.
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.