ArticleMolecular systems biology2024
Development and validation of AI/ML derived splice-switching oligonucleotides.
Article in Molecular systems biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed, 6 citations in OpenAlex.
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- PERK orchestrates an endoplasmic reticulum stress alternative splicing program via CLK1/SRSF1.Nature communications · 2026Article
- Alternative splicing rewires breast cancer and opens therapeutic avenues.Cell communication and signaling : CCS · 2026Review
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- Model selection in preclinical nucleic acid therapeutics research.Communications biology · 2026Review
- Toward an Extensible Regulatory Framework for N-of-1 to N-of-Few Personalized RNA Therapy Design.Therapeutic innovation & regulatory science · 2025Review
- Tipping the balance of cell death: alternative splicing as a source of MCL-1S in cancer.Cell death & disease · 2024Review
- Mechanisms of RNA alternative splicing dysregulation in triple-negative breast cancer.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Review
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
Abstract
Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.
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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.