ArticleBioinformatics (Oxford, England)2017
Integrative deep models for alternative splicing.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed, 93 citations in OpenAlex.
- Article
- A deep dive into statistical modeling of RNA splicing QTLs reveals variants that explain neurodegenerative disease.American journal of human genetics · 2025Article
- Rapid depletion and super-resolution microscopy reveal dual roles of SRSF5 in coordinating nuclear speckle-paraspeckle crosstalk during cellular stress.Nucleic acids research · 2025Article
- Alternative splicing across the C. elegans nervous system.Nature communications · 2025Article
- From computational models of the splicing code to regulatory mechanisms and therapeutic implications.Nature reviews. Genetics · 2025Review
- Machine learning-optimized targeted detection of alternative splicing.Nucleic acids research · 2025Article
- The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.PLoS computational biology · 2025Article
- Machine learning-optimized targeted detection of alternative splicing.bioRxiv : the preprint server for biology · 2024Article
- Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.Genome research · 2024Article
- Current understanding of functional peptides encoded by lncRNA in cancer.Cancer cell international · 2024Review
- Accurate isoform quantification by joint short- and long-read RNA-sequencing.bioRxiv : the preprint server for biology · 2024Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.bioRxiv : the preprint server for biology · 2024Article
- CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Seq-RBPPred: Predicting RNA-Binding Proteins from Sequence.ACS omega · 2024Article
- hGRAD: A versatile "one-fits-all" system to acutely deplete RNA binding proteins from condensates.The Journal of cell biology · 2024Article
- Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing.Genome biology · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Alternative Splicing Variation: Accessing and Exploiting in Crop Improvement Programs.International journal of molecular sciences · 2023Review
- Intestinal Epithelial Cell-Related Alternative Splicing Events in Dextran Sodium Sulfate-Induced Acute Colitis.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023Article
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 at 1 institution in 1 country.
Funding
Abstract
motivationAdvancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The first involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work, we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors.
resultsWe perform a detailed comparison of two previous modeling approaches, Bayesian and Deep Neural networks, dissecting the confounding effects of datasets and target functions. We then develop a new target function for AS prediction in exon skipping events and show it significantly improves model accuracy. Next, we develop a modeling framework that leverages transfer learning to incorporate CLIP-Seq, knockdown and over expression experiments, which are inherently noisy and suffer from missing values. Using several datasets involving key splice factors in mouse brain, muscle and heart we demonstrate both the prediction improvements and biological insights offered by our new models. Overall, the framework we propose offers a scalable integrative solution to improve splicing code modeling as vast amounts of relevant genomic data become available. AVAILABILITY AND IMPLEMENTATION: Code and data available at: majiq.biociphers.org/jha_et_al_2017/. CONTACT: yosephb@upenn.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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.