Mikel
Hernaez Arrazola
Profesional Investigador
Stanford University
Stanford, Estados UnidosPublicacións en colaboración con investigadores/as de Stanford University (17)
2023
-
Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM
Cell Reports Methods, Vol. 3, Núm. 1
2020
-
Comparison of single and module-based methods for modeling gene regulatory networks
Bioinformatics, Vol. 36, Núm. 2, pp. 558-567
2019
-
Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples
GigaScience, Vol. 8, Núm. 12
-
Denoising of Aligned Genomic Data
Scientific Reports, Vol. 9, Núm. 1
-
Genomic Data Compression
Annual Review of Biomedical Data Science, Vol. 2, pp. 19-37
-
SPRING: A next-generation compressor for FASTQ data
Bioinformatics, Vol. 35, Núm. 15, pp. 2674-2676
2017
-
Effect of lossy compression of quality scores on variant calling
Briefings in Bioinformatics, Vol. 18, Núm. 2, pp. 183-194
-
GeneComp, a New Reference-Based Compressor for SAM Files
Data Compression Conference Proceedings
2016
-
A Cluster-Based Approach to Compression of Quality Scores
Data Compression Conference Proceedings
-
An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values
Data Compression Conference Proceedings
-
CROMqs: An infinitesimal successive refinement lossy compressor for the quality scores
2016 IEEE Information Theory Workshop, ITW 2016
-
Comment on: 'ERGC: an efficient referential genome compression algorithm'
Bioinformatics, Vol. 32, Núm. 7, pp. 1115-1117
-
Denoising of Quality Scores for Boosted Inference and Reduced Storage
Data Compression Conference Proceedings
-
GTRAC: Fast retrieval from compressed collections of genomic variants
Bioinformatics
2015
-
IDoComp: A compression scheme for assembled genomes
Bioinformatics, Vol. 31, Núm. 5, pp. 626-633
-
QVZ: Lossy compression of quality values
Bioinformatics, Vol. 31, Núm. 19, pp. 3122-3129
2014
-
Aligned genomic data compression via improved modeling
Journal of Bioinformatics and Computational Biology