ReviewComputational and structural biotechnology journal2026
Artificial Intelligence in Bulk RNA-Seq: Challenges and Potential Solutions.
Review in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Integrative Transcriptomic Analysis Reveals Distinct and Shared Host Responses in Dengue and Chikungunya Infections.International journal of molecular sciences · 2026Article
- Transcriptional signatures of respiratory syncytial virus (RSV) infection using relaxed magnitude altitude score with hub-centric network analysis (RMAS-HCNA).Frontiers in genetics · 2026Article
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
Bulk RNA sequencing (RNA-seq) produces high-dimensional gene expression data where the number of measured features greatly exceeds the number of available samples, which challenges artificial intelligence (AI)-based modeling. In this setting, models are highly susceptible to overfitting and may fail to generalize across independent datasets when feature dimensionality is not adequately controlled. Feature (gene) selection is therefore essential for reliable inference, particularly when it is performed strictly within training data to prevent information leakage. This review examines how high dimensionality and limited sample size constrain AI-based analysis of bulk RNA-seq data and surveys feature selection strategies used to address these challenges. Emphasis is placed on statistically guided, training-only frameworks, with a focus on generalized linear models with quasi-likelihood
Identifiers
What Socratic 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 Socratic graph.