Helena Andrés-Terré

University of Cambridge — United Kingdom

Biochemistry, Genetics and Molecular Biology · Life Sciences

7h-index288citations24works0.02yr avg
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Research Topics

Gene expression and cancer classification(9), Bioinformatics and Genomic Networks(7), Single-cell and spatial transcriptomics(7), Cell Image Analysis Techniques(6), Generative Adversarial Networks and Image Synthesis(4)

Publications24 total

A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth
Frontiers in Big Data·2022Open Access
Metabolically Driven Latent Space Learning for Gene Expression Data
WORLD SCIENTIFIC (EUROPE) eBooks·2022
Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases
Bioinformatics·2021· 2 citedOpen Access
CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks
Bioinformatics·2021· 40 citedOpen Access
Graph Representation Learning on Tissue-Specific Multi-Omics
arXiv (Cornell University)·2021· 3 citedOpen Access
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
arXiv (Cornell University)·2021· 1 citedOpen Access
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
arXiv (Cornell University)·2021· 4 citedOpen Access
REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare
2021· 2 citedOpen Access
Adversarial generation of gene expression data
Bioinformatics·2021· 41 citedOpen Access
CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks
2020· 3 citedOpen Access
Using ontology embeddings for structural inductive bias in gene\n expression data analysis
arXiv (Cornell University)·2020Open Access
Using ontology embeddings for structural inductive bias in gene expression data analysis
arXiv (Cornell University)·2020· 2 citedOpen Access
Incorporating network based protein complex discovery into automated model construction
arXiv (Cornell University)·2020Open Access
Unsupervised generative and graph representation learning for modelling cell differentiation
Scientific Reports·2020· 21 citedOpen Access
Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice
Frontiers in Genetics·2019· 129 citedOpen Access
Interpreting Deep Learning for cell differentiation. Supervised and Unsupervised models viewed through the lens of information and perturbation theory.
Apollo (University of Cambridge)·2019Open Access
Adversarial generation of gene expression data
2019· 4 citedOpen Access
Decoupling feature propagation from the design of graph auto-encoders
arXiv (Cornell University)·2019Open Access
Unsupervised generative and graph representation learning for modelling cell differentiation
2019Open Access
Variational autoencoders for cancer data integration: design principles and computational practice
2019· 10 citedOpen Access
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Frequent Co-authors

Píetro Lió(23), Mateja Jamnik(10), Nikola Simidjievski(7), Paul Scherer(7), Zohreh Shams(7), Ifrah Tariq(4), Ramón Viñas(4), Ioana Bica(4), Maja Trębacz(4), Botty Dimanov(3), Cristian Bodnar(2), Kevin Bryson(2), David Buterez(2), Ana Cvejic(2), Marco Barsacchi(2), Dmitry Kazhdan(2), Adrian Weller(2), Ezra Webb(1), Ben Day(1), A. Tarjuelo Amor(1)