Author

Luis Fernando Iglesias-Martinez

University College DublinORCID 0000-0002-9110-2189Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Walter KölchiD· 3 shared Barbara De KegeliD· 1 shared Dan Yang Wester· 1 shared Sodiq Ayobami HameediD· 1 shared Vadim ZhernovkoviD· 1 shared

Activity by Year

2021-2025 · peak 2/yr

Co-Author Collaboration Network

5 top collaborators · 5 mutual co-author links

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3 shared papersBioinformatics

Walter KölchORCID

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3 papers

Recent·Most cited

Inferring physical cell-cell communication networks from scRNAseq data using univariate linear models.

Sodiq Ayobami Hameed, Luis Fernando Iglesias-Martinez, Walter Kölch, Vadim Zhernovkov

This study develops a method to infer physical cell-cell communication networks from scRNAseq data by identifying multiplets and using univariate linear models to predict interactions.

bioRxiv · Jul 7, 2025 · PreprintBioinformatics

SAEG: A Novel Deep Learning Architecture for Somatic Alterations

Luis Fernando Iglesias-Martinez, Dan Yang Wester, Walter Kölch

SAEG is a deep learning model that predicts gene knockout susceptibility by integrating mutations, copy number alterations, and protein interactions, outperforming existing methods.

bioRxiv · Feb 21, 2025 · PreprintBioinformatics

KBoost: a new method to infer gene regulatory networks from gene expression data

Luis Fernando Iglesias-Martinez, Barbara De Kegel, Walter Kölch

KBoost infers gene regulatory networks from gene expression data using kernel PCA regression, boosting, and Bayesian model averaging, showing improved performance across datasets and applied to breast cancer data.

Scientific Reports · Jul 29, 2021Peer-reviewedBioinformatics