ArticleGenome biology2016
Choosing panels of genomics assays using submodular optimization.
Article in Genome biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Article
- Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.Briefings in bioinformatics · 2024Article
- Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data.Bioinformatics (Oxford, England) · 2021Article
- Accelerated knowledge discovery from omics data by optimal experimental design.Nature communications · 2020Article
- Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions.Advances in neural information processing systems · 2018Article
- PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition.Nature communications · 2018Article
- Choosing non-redundant representative subsets of protein sequence data sets using submodular optimization.Proteins · 2018Article
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
Abstract
Due to the high cost of sequencing-based genomics assays such as ChIP-seq and DNase-seq, the epigenomic characterization of a cell type is typically carried out using a small panel of assay types. Deciding a priori which assays to perform is, thus, a critical step in many studies. We present the submodular selection of assays (SSA), a method for choosing a diverse panel of genomic assays that leverages methods from submodular optimization. More generally, this application serves as a model for how submodular optimization can be applied to other discrete problems in biology.
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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.