Computational systems biology

Developing predictive models for precision medicine

Featured articles

Novel AI tools to accelerate cancer research,”
Matteo Manica and Joris Cadow, IBM Research blog, 22 July 2019.

Deciphering breast cancer heterogeneity using machine learning,”
Marianna Rapsomaniki, IBM Research blog, 16 May 2019.

“PaccMannRL: Designing Anticancer Drugs with Reinforcement Learning”
Jannis Born, TWIML AI Podcast, 23 January 2020

[1] Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, María Rodríguez Martínez,
PaccMannRL: Designing anticancer drugs from transcriptomic data via reinforcement learning,”
Workshop on Machine Learning and the Physical Sciences NeurIPS, 2019.

[2] Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez,
Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders,
Molecular Pharmaceutics, 2019.

[3] Ali Oskooei, Matteo Manica, Roland Mathis, María Rodríguez Martínez,
Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer,
Scientific Reports, 2019.

[4] Matteo Manica, Raphel Polig, Mitra Purandare, Roland Mathis, Christoph Hagleitner, María Rodríguez Martínez,
Accelerated analysis of Boolean gene regulatory networks via reconfigurable hardware,
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019.

[5] An-phi Nguyen, María Rodríguez Martínez,
MonoNet: Towards Interpretable Models by Learning Monotonic Features,
Workshop on Human-Centric Machine Learning NeurIPS, 2019.

[6] Johanna Wagner, Maria Anna Rapsomaniki, et al.,
A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer,”
Cell 177(5), 2019.

[7] Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, Maria Gabrani,
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs,”
Workshop on Representation Learning on Graphs and Manifolds ICLR, 2019.

[8] Marcel Jan Thomas, Ulf Klein, John Lygeros, María Rodríguez Martínez,
A Probabilistic Model of the Germinal Center Reaction,”
Frontiers in Immunology 10, 2019.

[9] Matteo Manica, Roland Mathis, Joris Cadow, María Rodríguez Martínez,
Context-specific interaction networks from vector representation of words,”
Nature Machine Intelligence 1(4), 181–190, 2019.

[10] Matteo Manica, Joris Cadow, Roland Mathis, María Rodríguez Martínez,
PIMKL: Pathway-Induced Multiple Kernel Learning,”
npj Systems Biology and Applications 5(1), 8, 2019.

[11] Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez- Rodríguez, María Rodríguez Martínez,
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks,”
Workshop on Machine Learning for Molecule and Materials NeurIPS, 2018.

[12] Maria Anna Rapsomaniki, Xiaokang Lun, Stefan Woerner, Marco Laumanns, Bernd Bodenmiller, and María Rodríguez Martínez,
CellCycleTRACER accounts for cell cycle and volume in mass cytometry data,”
Nature Communications 9, 632, 2018.
Get our free CellCycleTRACER web app.

[13] Manuel Le Gallo, Abu Sebastian, Roland Mathis, Matteo Manica, Tomas Tuma, Costas Bekas, Alessandro Curioni, Evangelos Eleftheriou,
Mixed-Precision Memcomputing,”
Nature Electronics 1, 246-253, 2018.

[14] Matteo Manica, Philippe Chouvarine, Roland Mathis, Ulrich Wagner, Kathrin Oehl, Karim Saba, Laura De Vargas Roditi, Arati N Pati, María Rodríguez Martínez, Peter J Wild,
Inferring clonal composition from multiple tumor biopsies,”
arXiv preprint arXiv:1701.07940, 2017.