Susana Vinga

Regularized logistic regression-based gene selection for functional analysis of heterogeneous scRNA-seq macrophage populations in muscle aging and regeneration.

The paper introduces DisTwiner, a method for subgroup-aware gene selection in scRNA-seq data, showing it captures biologically relevant aging pathways in macrophages.

Uncovering Latent Structure in Gliomas Using Multi-Omics Factor Analysis

This study uses multi-omics factor analysis to identify molecular profiles and biomarkers in gliomas, revealing relationships between methylation, genetics, and transcriptional patterns.

Updating TCGA glioma classification through integration of molecular profiling data following the 2016 and 2021 WHO guidelines

This paper updates TCGA glioma classifications to align with WHO-2016 and WHO-2021 guidelines using molecular profiling data, achieving high reclassification rates.

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

The paper introduces a deep learning model to augment cancer cell line multi-omic data, increasing the number of profiles and uncovering drug resistance mechanisms.

Latent variable modelling and variational inference for scRNA-seq differential expression analysis

This paper introduces two new methods for differential expression analysis in scRNA-seq and benchmarks them against existing tools using two public datasets.

MOMO - Multi-Objective Metabolic mixed integer Optimization: application to yeast strain engineering

The paper introduces a multi-objective optimization framework for metabolic engineering to identify genetic manipulations that improve bioproductivity in yeast.

Benchmarking of alignment-free sequence comparison methods

This paper benchmarks alignment-free sequence comparison methods across five research applications and provides a web resource for evaluating and comparing these methods.

Scalable probabilistic matrix factorization for single-cell RNA-seq analysis

This paper introduces two probabilistic models for scRNA-seq data analysis that improve scalability and robustness in dimensionality reduction and clustering.

Consensus outlier detection in survival analysis using the rank product test

The paper proposes a consensus outlier detection method using the Rank Product test for survival analysis, showing its potential for identifying outliers and biomarkers in clinical datasets.

Sparse network-based regularization for the analysis of patientomics high-dimensional survival data

This paper introduces sparse network-based regularization for survival analysis, improving predictive performance and interpretability in breast cancer transcriptomic data.

MassBlast: A workflow to accelerate RNA-seq and DNA database analysis

MassBlast accelerates RNA-seq and DNA database analysis by combining BLAST+ with an automated workflow to improve annotation of sequencing databases.