22 citations · 34 across the 5 of their papers we have counts for
5 papers
Interpretable Causal Representation Learning for Biological Data in the Pathway Space
Jesus de la Fuente, Robert Lehmann, Carlos Ruiz-Arenas +8
Predicting the impact of genomic and drug perturbations in cellular function is crucial for understanding gene functions and drug effects, ultimately leading to improved therapies.…
REX: Causal discovery based on machine learning and explainability techniques
Jesus Renero, Idoia Ochoa, Roberto Maestre
Explainable Artificial Intelligence (XAI) techniques hold significant potential for enhancing the causal discovery process, which is crucial for understanding complex systems in ar…
Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution
Jesus de la Fuente, Naroa Legarra, Guillermo Serrano +8
Single-cell RNA-sequencing (scRNA-seq) stands as a powerful tool for deciphering cellular heterogeneity and exploring gene expression profiles at high resolution. However, its high…
Lossy Compression of Quality Values via Rate Distortion Theory
Himanshu Asnani, Dinesh Bharadia, Mainak Chowdhury +3
Motivation: Next Generation Sequencing technologies revolutionized many fields in biology by enabling the fast and cheap sequencing of large amounts of genomic data. The ever incre…
Reference Based Genome Compression
Bobbie Chern, Idoia Ochoa, Alexandros Manolakos +3
DNA sequencing technology has advanced to a point where storage is becoming the central bottleneck in the acquisition and mining of more data. Large amounts of data are vital for g…