On the public citation graph
Classification of RNA-Seq Data via Bagging Support Vector Machines
0 signed · 63 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-45014-9_1openalex-snapshot
Ensemble Methods in Machine Learning
inferredcites →https://doi.org/10.1007/3-540-45665-1_31openalex-snapshot
Support Vector Machine Ensemble with Bagging
inferredcites →https://doi.org/10.1007/978-0-387-21606-5openalex-snapshot
The Elements of Statistical Learning
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/978-3-540-36668-3_30openalex-snapshot
Feature Selection for Bagging of Support Vector Machines
inferredcites →https://doi.org/10.1007/978-3-540-72849-8_61openalex-snapshot
Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues
inferredcites →https://doi.org/10.1016/0377-2217(85)90321-2openalex-snapshot
Classification and regression trees
inferredcites →https://doi.org/10.1016/j.artmed.2007.09.004openalex-snapshot
An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer
inferredcites →https://doi.org/10.1016/j.jtbi.2009.04.013openalex-snapshot
Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates
inferredcites →https://doi.org/10.1016/j.ymeth.2013.10.002openalex-snapshot
Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs
inferredcites →https://doi.org/10.1016/s0031-3203(03)00175-4openalex-snapshot
Constructing support vector machine ensemble
inferredcites →https://doi.org/10.1038/nmeth.1226openalex-snapshot
Mapping and quantifying mammalian transcriptomes by RNA-Seq
inferredcites →https://doi.org/10.1038/npre.2010.4282.2openalex-snapshot
Differential expression analysis for sequence count data
inferredcites →https://doi.org/10.1038/nrg2484openalex-snapshot
RNA-Seq: a revolutionary tool for transcriptomics
inferredcites →https://doi.org/10.1093/bioinformatics/16.10.906openalex-snapshot
Support vector machine classification and validation of cancer tissue samples using microarray expression data
inferredcites →https://doi.org/10.1093/bioinformatics/btm158openalex-snapshot
Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach
inferredcites →https://doi.org/10.1093/bioinformatics/btm453openalex-snapshot
Moderated statistical tests for assessing differences in tag abundance
inferredcites →https://doi.org/10.1093/bioinformatics/btp450openalex-snapshot
ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data
inferredcites →https://doi.org/10.1093/bioinformatics/btp616openalex-snapshot
edgeR : a Bioconductor package for differential expression analysis of digital gene expression data
inferredcites →https://doi.org/10.1093/bioinformatics/btq033openalex-snapshot
BEDTools: a flexible suite of utilities for comparing genomic features
inferredcites →https://doi.org/10.1093/bioinformatics/btr449openalex-snapshot
A powerful and flexible approach to the analysis of RNA sequence count data
inferredcites →https://doi.org/10.1093/bioinformatics/bts635openalex-snapshot
STAR: ultrafast universal RNA-seq aligner
inferredcites →https://doi.org/10.1093/bioinformatics/btt674openalex-snapshot
A classification approach for DNA methylation profiling with bisulfite next-generation sequencing data
inferredcites →https://doi.org/10.1093/biostatistics/5.3.427openalex-snapshot
Classification of gene microarrays by penalized logistic regression
inferredcites →https://doi.org/10.1093/nar/gkq622openalex-snapshot
MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery
inferredcites →https://doi.org/10.1101/gr.079558.108openalex-snapshot
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
inferredcites →https://doi.org/10.1109/csb.2003.1227396openalex-snapshot
Minimum redundancy feature selection from microarray gene expression data
inferredcites →https://doi.org/10.1109/ijcnn.2003.1223688openalex-snapshot
Bagged ensembles of Support Vector Machines for gene expression data analysis
inferredcites →https://doi.org/10.1111/j.0006-341x.2004.172_2.xopenalex-snapshot
Statistical Analysis of Gene Expression Microarray Data.
inferredcites →https://doi.org/10.1126/science.1158441openalex-snapshot
The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing
inferredcites →https://doi.org/10.1186/1471-2105-11-94openalex-snapshot
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
inferredcites →https://doi.org/10.1186/1471-2105-14-307openalex-snapshot
Modeling the next generation sequencing sample processing pipeline for the purposes of classification
inferredcites →https://doi.org/10.1186/1471-2105-15-33openalex-snapshot
QTrim: a novel tool for the quality trimming of sequence reads generated using the Roche/454 sequencing platform
inferredcites →https://doi.org/10.1186/1471-2105-7-3openalex-snapshot
Gene selection and classification of microarray data using random forest
inferredcites →https://doi.org/10.1186/1471-2105-8-35openalex-snapshot
Classification of microarray data using gene networks
inferredcites →https://doi.org/10.1186/1471-2105-9-319openalex-snapshot
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
inferredcites →https://doi.org/10.1186/1471-2164-10-221openalex-snapshot
Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays
inferredcites →https://doi.org/10.1186/1471-2164-15-196openalex-snapshot
AlgaePath: comprehensive analysis of metabolic pathways using transcript abundance data from next-generation sequencing in green algae
inferredcites →https://doi.org/10.1186/1741-7007-8-58openalex-snapshot
Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls
inferredcites →https://doi.org/10.1186/gb-2010-11-10-r106openalex-snapshot
Differential expression analysis for sequence count data
inferredcites →https://doi.org/10.1186/gb-2010-11-3-r25openalex-snapshot
A scaling normalization method for differential expression analysis of RNA-seq data
inferredcites →https://doi.org/10.1186/gb-2013-14-4-r36openalex-snapshot
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions
inferredcites →https://doi.org/10.1186/gb-2014-15-2-r29openalex-snapshot
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
inferredcites →https://doi.org/10.1186/s13059-014-0550-8openalex-snapshot
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
inferredcites →https://doi.org/10.1198/016214502753479248openalex-snapshot
Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
inferredcites →https://doi.org/10.1201/9780203011232openalex-snapshot
Statistical Analysis of Gene Expression Microarray Data
inferredcites →https://doi.org/10.18637/jss.v028.i05openalex-snapshot
Building Predictive Models in R Using the caret Package
inferredcites →https://doi.org/10.2202/1544-6115.1627openalex-snapshot
A Two-Stage Poisson Model for Testing RNA-Seq Data
inferredcites →https://doi.org/10.5121/ijcses.2011.2302openalex-snapshot
Review On Feature Selection Techniques And The Impact Of Svm For Cancer Classification Using Gene Expression Profile
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-319-65981-7_6openalex-snapshot
Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data
inferred← citeshttps://doi.org/10.1007/s00441-017-2734-5openalex-snapshot
Computational systems biology approaches for Parkinson’s disease
inferred← citeshttps://doi.org/10.1007/s11571-021-09724-8openalex-snapshot
Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification approach
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2022.101822openalex-snapshot
An approach to multi-class imbalanced problem in ecology using machine learning
inferred← citeshttps://doi.org/10.1109/access.2020.3011508openalex-snapshot
Spark-Based Parallel Deep Neural Network Model for Classification of Large Scale RNAs into piRNAs and Non-piRNAs
inferred← citeshttps://doi.org/10.1186/s13040-017-0134-8openalex-snapshot
Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.