Mikel Hernáez

RAPTORGraph improves causal discovery in single-cell perturbations by modeling gene interactions and isolating perturbation effects, outperforming existing methods on downstream analyses.

This study identifies transcriptional changes in stromal and endothelial cells during MGUS to myeloma progression and treatment response, revealing a tumor-permissive BME signature that is modulated by therapy.

This study engineers sdAb-based CAR T cells targeting BCMA, showing improved antitumor efficacy and persistence in multiple myeloma compared to existing therapies.

This study uses single-cell RNA sequencing to analyze EDA-CAR-T cell dynamics in the tumor microenvironment, revealing temporal reprogramming toward dysfunction and identifying immunosuppressive pathways that impair therapeutic efficacy in solid tumors.

This study uses single-cell multiomics to analyze CAR T cell dynamics in multiple myeloma, identifying regulatory mechanisms affecting persistence and dysfunction.

The paper constructs a comprehensive single-nucleus RNA-seq atlas of the human neurovascular unit in Alzheimer's disease, revealing sex-specific vascular dysfunctions and disrupted intercellular communication.

The paper constructs a CAR-T cell functional atlas from over one million cells across 13 studies to link product composition with clinical response and identify sex- and age-dependent effects.

This study characterizes the gene regulatory network of early human B-cell development and identifies regulatory elements linked to B-cell acute lymphoblastic leukemia.

JIND-Multi improves cell annotation in single-cell RNA and ATAC data by transferring labels across multiple datasets, reducing unclassified cells and maintaining accuracy.

The paper introduces ELATUS, a computational framework for detecting functional lncRNAs from scRNA-seq data, showing improved detection and biological relevance compared to traditional methods.

This study maps epigenetic and transcriptional changes in myeloma progression from precursor to advanced stages, identifying key transcription factors involved in disease development.

This study uses scRNA-seq to analyze del(5q) MDS patients, revealing shared and unique transcriptional alterations and lenalidomide's impact on disease and treatment response.

NetActivity improves gene set activity scores by using interpretable autoencoders, showing better consistency and biological relevance in prostate cancer analysis.

GeNNius uses a graph neural network to predict drug-target interactions more efficiently and accurately than existing methods across multiple datasets.

The paper uses single-cell transcriptomics to show how genetic variants influence gene regulatory networks in honeybee brains, affecting aggression-related behaviors through specific cell populations and pathways.

This study uses single-cell RNA sequencing to analyze changes in hematopoietic stem and progenitor cells during aging and myeloid malignancies, identifying altered gene expression and regulatory networks.

The paper identifies transcriptional network disruptions in drug-resistant prostate cancer using TraRe, uncovering key regulatory modules and transcription factors associated with resistance.

JIND improves automated cell-type annotation in single-cell RNA-seq by learning a latent space for reliable classification and batch alignment without retraining.

This study uses single-cell transcriptomics to track liver cell transitions during regeneration, revealing metabolic and proliferative dynamics in mouse liver.

GPress compresses and queries GFF and feature expression files efficiently, achieving significant size reduction without compromising retrieval speed.

The paper introduces SimiC, a method for inferring gene regulatory networks per cell phenotype and comparing them across conditions, revealing regulatory dynamics missed by single GRN approaches.

This paper benchmarks lncRNA quantification methods on cancer RNA-Seq data, finding that pseudoalignment methods with full transcriptome annotation outperform alignment-based methods.

The paper introduces SAMDUDE, a denoising method for aligned genomic data that improves variant calling by reducing false positives and increasing true variant detection in both chromosome and whole genome sequencing data.

This study compares the accuracy and performance of Sentieon DNASeq and GATK for single-sample variant calling, finding they are nearly identical in accuracy with Sentieon being faster and more scalable.

This study compares module-based and single gene methods for gene regulatory network inference, finding that a variational Bayes approach outperforms existing methods in both simulated and real data.

This paper introduces MPEG-G, an ISO standard for genomic data compression, highlighting its format, decoding process, and interoperability features.

FaStore compresses FASTQ files with lossy modes, achieving better compression ratios than existing methods while maintaining variant calling accuracy.