About: BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance
Creator	Jiang, Yihan Kannan, Sreeram Mao, Shunfu Mathew, Edwin
Source	PMC
abstract	High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts from those fragments (RNA-Seq assembly) is still a difficult task. For example, RNA-Seq assembly tools typically require hyper-parameter tuning to achieve good performance for particular datasets. This kind of tuning is usually unintuitive and time-consuming. Consequently, users often resort to default parameters, which do not guarantee consistent good performance for various datasets. Results: Here we propose BOAssembler, a framework that enables end-to-end automatic tuning of RNA-Seq assemblers, based on Bayesian Optimization principles. Experiments show this data-driven approach is effective to improve the overall assembly performance. The approach would be helpful for downstream (e.g. gene, protein, cell) analysis, and more broadly, for future bioinformatics benchmark studies. Availability: https://github.com/shunfumao/boassembler.
has issue date	2020-02-01(xsd:dateTime)
bibo:doi	10.1007/978-3-030-42266-0_15
has license	no-cc
sha1sum (hex)	87ba625767179c70a86cb012d88ef62d1c9f25df
schema:url	https://doi.org/10.1007/978-3-030-42266-0_15
resource representing a document's title	BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance
has PubMed Central identifier	PMC7197064
schema:publication	Algorithms for Computational Biology
resource representing a document's body	covid:87ba625767179c70a86cb012d88ef62d1c9f25df#body_text
is schema:about of	named entity 'RNA-Seq' named entity 'DIFFICULT' named entity 'VARIOUS' named entity 'fragments' named entity 'For' named entity 'users' named entity 'tools' named entity 'good' named entity 'RNA-Seq' named entity 'short' named entity 'High' named entity 'throughput' named entity 'RNA transcripts' named entity 'RNA-Seq' named entity 'hyper-parameter' named entity 'transcripts' named entity 'gene' named entity 'assembler' named entity 'gradient' named entity 'HESC' named entity 'transcriptome' named entity 'assembler' named entity 'graph' named entity 'variant calling' named entity 'posterior distributions' named entity 'Gaussian Process' named entity 'closed-form' named entity 'Reinforcement Learning' named entity 'objective functions' named entity 'ground truth' named entity 'Bayesian optimization' named entity 'expression levels' named entity 'F1 score' named entity 'NLP' named entity 'RNA-Seq' named entity 'assembler' named entity 'assembler' named entity 'heuristic approaches' named entity 'Natural Language Processing' named entity 'assembler' named entity 'RNA-Seq' named entity 'Illumina' named entity 'designed experiments' named entity 'overfitting' named entity 'NHGRI' named entity 'assembler' named entity 'global optimization' named entity 'assembler' named entity 'cell functions' named entity 'hg19' named entity 'human embryonic stem cells' named entity 'cross-validation' named entity 'deep learning' named entity 'RNA transcript' named entity 'good performance' named entity 'RNA transcripts' named entity 'reference genome' named entity 'gradient-based optimization' named entity 'Sequence assembly' named entity 'good performance' named entity 'graph' named entity 'ground truth' named entity 'genome' named entity 'Bayesian Optimization' named entity 'Github' named entity 'Channel Coding' named entity 'exonic' named entity 'RNA-Seq' named entity 'RNA transcripts' named entity 'RNA transcripts' named entity 'assembler' named entity 'Algorithm' named entity 'Python code' named entity 'F1 score' named entity 'Baysian' named entity 'HESC' named entity 'RNA transcripts' named entity 'black box' named entity 'Illumina' named entity 'HESC'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software