Cargando…

RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity

Multiple transcriptomic predictors of tumour cell radiosensitivity (RS) have been proposed, but they have not been benchmarked against one another or to control models. To address this, we present RadSigBench, a comprehensive benchmarking framework for RS signatures. The approach compares candidate...

Descripción completa

Detalles Bibliográficos
Autores principales:	O’Connor, John D, Overton, Ian M, McMahon, Stephen J
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Oxford University Press 2022
Materias:	Problem Solving Protocol
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921666/ https://www.ncbi.nlm.nih.gov/pubmed/35066588 http://dx.doi.org/10.1093/bib/bbab561

Ejemplares similares

IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline
por: Knight, Connor H, et al.
Publicado: (2023)

Toward a gold standard for benchmarking gene set enrichment analysis
por: Geistlinger, Ludwig, et al.
Publicado: (2020)

Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data
por: Sidorczuk, Katarzyna, et al.
Publicado: (2022)

Systematic benchmarking of statistical methods to assess differential expression of circular RNAs
por: Buratin, Alessia, et al.
Publicado: (2023)

Benchmarking cell-type clustering methods for spatially resolved transcriptomics data
por: Cheng, Andrew, et al.
Publicado: (2022)

Benchmarking network-based gene prioritization methods for cerebral small vessel disease
por: Zhang, Huayu, et al.
Publicado: (2021)

Machine learning for RNA 2D structure prediction benchmarked on experimental data
por: Justyna, Marek, et al.
Publicado: (2023)

RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria
por: Gelhausen, Rick, et al.
Publicado: (2022)

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data
por: Junttila, Sini, et al.
Publicado: (2022)

Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark
por: Li, Hongjian, et al.
Publicado: (2021)

RNA2HLA: HLA-based quality control of RNA-seq datasets
por: Chelysheva, Irina, et al.
Publicado: (2021)

Deciphering signatures of natural selection via deep learning
por: Qin, Xinghu, et al.
Publicado: (2022)

Validation of transcriptome signature reversion for drug repurposing in oncology
por: Koudijs, Karel K M, et al.
Publicado: (2022)

The repertoire of copy number alteration signatures in human cancer
por: Tao, Ziyu, et al.
Publicado: (2023)

cropCSM: designing safe and potent herbicides with graph-based signatures
por: Pires, Douglas E V, et al.
Publicado: (2022)

Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches
por: Wu, Andy J, et al.
Publicado: (2023)

CAFU: a Galaxy framework for exploring unmapped RNA-Seq data
por: Chen, Siyuan, et al.
Publicado: (2019)

A statistical framework for predicting critical regions of p53-dependent enhancers
por: Niu, Xiaohui, et al.
Publicado: (2020)

PCfun: a hybrid computational framework for systematic characterization of protein complex function
por: Sharma, Varun S, et al.
Publicado: (2022)

Integrating multi-omics summary data using a Mendelian randomization framework
por: Jin, Chong, et al.
Publicado: (2022)

PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data
por: Wang, Yiwen, et al.
Publicado: (2023)

ExosomePurity: tumour purity deconvolution in serum exosomes based on miRNA signatures
por: Wu, Tao, et al.
Publicado: (2023)

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps
por: He, Jiahua, et al.
Publicado: (2021)

NiPTUNE: an automated pipeline for noninvasive prenatal testing in an accurate, integrative and flexible framework
por: Duboc, Véronique, et al.
Publicado: (2021)

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles
por: Conev, Anja, et al.
Publicado: (2023)

An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F(2)
por: Wen, Yang-Jun, et al.
Publicado: (2018)

STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction
por: Basith, Shaherin, et al.
Publicado: (2021)

Vir2Drug: a drug repurposing framework based on protein similarities between pathogens
por: Minadakis, George, et al.
Publicado: (2022)

A universal framework for single-cell multi-omics data integration with graph convolutional networks
por: Gao, Hongli, et al.
Publicado: (2023)

PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs
por: Naderi Yeganeh, Pourya, et al.
Publicado: (2023)

PPMS: A framework to Profile Primary MicroRNAs from Single-cell RNA-sequencing datasets
por: Ji, Jiahui, et al.
Publicado: (2022)

HiC1Dmetrics: framework to extract various one-dimensional features from chromosome structure data
por: Wang, Jiankang, et al.
Publicado: (2021)

Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references
por: Deng, Yidi, et al.
Publicado: (2022)

RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins
por: Peng, Xinxin, et al.
Publicado: (2022)

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
por: Abdellah, Marwan, et al.
Publicado: (2022)

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
por: Xu, Gang, et al.
Publicado: (2021)

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming
por: Tian, Saisai, et al.
Publicado: (2023)

NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures
por: Martínez-Enguita, David, et al.
Publicado: (2023)

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs
por: Sen, Neeladri, et al.
Publicado: (2022)

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning
por: Hong, Jiajun, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_683k8ifg22kh82chp8hva5jl7i