Author

Mateo Rojas-Carulla

Max Planck Society

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Bioinformatics· 4

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 4 Approach:deep learning· 3 Assay:ChIP-seq· 2 Assay:metagenomics· 2 Organism:bacteria (other)· 1 Research Focus:database / resource· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Bernhard SchölkopfiD· 4 shared Alex Hawkins‐HookeriD· 2 shared Gabriele SchweikertiD· 2 shared Ruth E. LeyiD· 2 shared Anshul Bharat KundajeiD· 1 shared Arif CanakogluiD· 1 shared B BernsteiniD· 1 shared Carles A. BoixiD· 1 shared

Activity by Year

2019-2023 · peak 2/yr

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10 top collaborators · 37 mutual co-author links

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4 shared papersBioinformatics

Bernhard SchölkopfORCID

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4 papers

Recent·Most cited

Getting Personal with Epigenetics: Towards Machine-Learning-Assisted Precision Epigenomics

Alex Hawkins‐Hooker, Giovanni Visonà, Tanmayee Narendra, Mateo Rojas-Carulla, Bernhard Schölkopf, Gabriele Schweikert

The paper introduces eDICE, a deep learning model for imputing epigenomic tracks, showing improved performance on reference data and successful application to individual-specific epigenomic patterns.

Nature Communications · Aug 7, 2023Peer-reviewedBioinformatics

The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles

Jacob Matthew Schreiber, Carles A. Boix, Jin wook Lee, Hongyang Li, Yuanfang Guan, Chun-Chieh Chang, Jen-Chien Chang, Alex Hawkins‐Hooker, Bernhard Schölkopf, Gabriele Schweikert, Mateo Rojas-Carulla, Arif Canakoglu, Francesco Guzzo, Luca Nanni, Marco Masseroli, , ENCODE Imputation Challenge Participants

The paper evaluates 23 imputation models for cross-cell type epigenomic data, finding quality assessment challenging due to distributional shifts, data availability, and redundant metrics.

Genome biology · Apr 18, 2023Peer-reviewedBioinformatics

DeepMAsED: Evaluating the quality of metagenomic assemblies

Olga Mineeva, Mateo Rojas-Carulla, Ruth E. Ley, Bernhard Schölkopf, Nicholas D. Youngblut

DeepMAsED evaluates metagenomic assembly quality using deep learning, identifying misassembled contigs without references and showing high accuracy on large assemblies.

Bioinformatics · Feb 18, 2020Peer-reviewedBioinformatics

GeNet: Deep Representations for Metagenomics

Mateo Rojas-Carulla, Ilya Tolstikhin, Guillermo Luque, Nicholas Youngblut, Ruth E. Ley, Bernhard Schölkopf

GeNet provides deep representations for metagenomics classification, achieving competitive precision and recall with lower memory use and enabling effective downstream tasks.

bioRxiv · Feb 1, 2019 · PreprintBioinformatics