ArticleBioData mining2026
CRM-TI: an enhanced pipeline for computationally assigning the target genes of cis-regulatory modules by considering comprehensive long-range regulation mechanisms.
Article in BioData mining, 2026. 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetazoan transcriptional regulation is mediated by modular DNA segments termed cis-regulatory modules (CRMs). The precise identification of CRM target genes is essential for understanding cellular functions and disease mechanisms. While reporter assays can validate CRM targets, selecting candidates remains challenging because CRMs often act over long distances, bypassing nearby genes. Simple nearest-gene assignments are therefore unreliable, and methods based on epigenetic correlations are still limited. Enhancer-promoter interaction (EPI) prediction has improved CRM target identification. However, lots of existing EPI tools suffer from overfitting and overlook alternative mechanisms such as non-coding RNAs (ncRNAs) transcribed from CRMs.
resultsTo address these gaps, we developed CRM-Target Identifier (CRM-TI), a deep learning pipeline that integrates mechanisms of chromatin interactions and CRM ncRNA-transcription-related gene regulation. Using a chromosome-wise partitioning scheme, CRM-TI achieved an unbiased test auROC of 90.0%, outperforming existing methods by at least 10%. Additionally, considering both CRM-gene interactions and ncRNA-transcription-related regulation further improved auROC performance by 21.9% over the model that considers only CRM-gene interactions.
conclusionsThese results demonstrate that combining comprehensive transcriptional regulatory mechanisms on CRMs provides a more complete framework for CRM-target gene assignment. CRM-TI is available at https://github.com/cobisLab/CRM-TI .
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.