ArticleNucleic acids research2013
kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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
59 citing papers in PubMed, 138 citations in OpenAlex.
- Non-local modeling of enhancer-promoter interactions, a correspondence on "LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions".Applied intelligence (Dordrecht, Netherlands) · 2025Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- MLSNet: a deep learning model for predicting transcription factor binding sites.Briefings in bioinformatics · 2024Article
- A novel method for identifying key genes in macroevolution based on deep learning with attention mechanism.Scientific reports · 2023Article
- Predicting active enhancers with DNA methylation and histone modification.BMC bioinformatics · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier.International journal of molecular sciences · 2023Article
- MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data.Bioinformatics (Oxford, England) · 2022Article
- Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction.International journal of molecular sciences · 2022Article
- CMash: fast, multi-resolution estimation of k-mer-based Jaccard and containment indices.Bioinformatics (Oxford, England) · 2022Article
- From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.Human genomics · 2022Review
- Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods.PloS one · 2022Article
- Structure-aware machine learning identifies microRNAs operating as Toll-like receptor 7/8 ligands.RNA biology · 2021Article
- Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.Briefings in bioinformatics · 2021Article
- MSCFS: inferring circRNA functional similarity based on multiple data sources.BMC bioinformatics · 2021Article
- Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture.Molecular therapy. Nucleic acids · 2021Article
- DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.International journal of molecular sciences · 2021Article
- Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes.Frontiers in genetics · 2021Article
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- Intrinsic laws of k-mer spectra of genome sequences and evolution mechanism of genomes.BMC evolutionary biology · 2020Article
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 at 2 institutions in 1 country.
Funding
Abstract
Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167-80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org.
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.