Matteo Manica

According to our database, Matteo Manica authored at least 37 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Transforming the Hybrid Cloud for Emerging AI Workloads.

Deming Chen, Alaa Youssef, Ruchi Pendse, André Schleife, Bryan K. Clark, Hendrik F. Hamann, Jingrui He, Teodoro Laino, Lav R. Varshney, Yuxiong Wang, Avirup Sil, Reyhaneh Jabbarvand, Tianyin Xu, Volodymyr V. Kindratenko, Carlos H. A. Costa, Sarita V. Adve, Charith Mendis, Minjia Zhang, Santiago Núñez-Corrales, Raghu K. Ganti, Mudhakar Srivatsa, Nam Sung Kim, Josep Torrellas, Jian Huang, Seetharami R. Seelam, Klara Nahrstedt, Tarek F. Abdelzaher, Tamar Eilam, Huimin Zhao, Matteo Manica, Ravishankar K. Iyer, Martin Hirzel, Vikram S. Adve, Darko Marinov, Hubertus Franke, Hanghang Tong, Elizabeth A. Ainsworth, Han Zhao, Deepak Vasisht, Minh Do, Fábio Oliveira, Giovanni Pacifici, Ruchir Puri, Priya Nagpurkar

CoRR, 2024

2023

Regression Transformer enables concurrent sequence regression and generation for molecular language modelling.

Jannis Born, Matteo Manica

Nat. Mac. Intell., April, 2023

Domain-agnostic and Multi-level Evaluation of Generative Models.

Girmaw Abebe Tadesse, Jannis Born, Celia Cintas, William Ogallo, Dmitry Zubarev, Matteo Manica, Komminist Weldemariam

CoRR, 2023

Unifying Molecular and Textual Representations via Multi-task Language Modelling.

Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica

Proceedings of the International Conference on Machine Learning, 2023

Zero-Shot-BERT-Adapters: a Zero-Shot Pipeline for Unknown Intent Detection.

Daniele Comi, Dimitrios Christofidellis, Pier Francesco Piazza, Matteo Manica

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022

On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction.

Jannis Born, Yoel Shoshan, Tien Huynh, Wendy D. Cornell, Eric J. Martin, Matteo Manica

J. Chem. Inf. Model., 2022

Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model.

Jannis Born, Tien Huynh, Astrid Stroobants, Wendy D. Cornell, Matteo Manica

J. Chem. Inf. Model., 2022

Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection.

Daniele Comi, Dimitrios Christofidellis, Pier Francesco Piazza, Matteo Manica

CoRR, 2022

GT4SD: Generative Toolkit for Scientific Discovery.

Matteo Manica, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Jannis Born, Dean Clarke, Yves Gaetan Nana Teukam, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang-Han Hsu, Federico Zipoli, Oliver Schilter, Giorgio Giannone, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith

CoRR, 2022

Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens.

Jannis Born, Matteo Manica

CoRR, 2022

PCfun: a hybrid computational framework for systematic characterization of protein complex function.

Varun S. Sharma, Andrea Fossati, Rodolfo Ciuffa, Marija Buljan, Evan G. Williams, Zhen Chen, Wenguang Shao, Patrick G. A. Pedrioli, Anthony W. Purcell, María Rodríguez Martínez, Jiangning Song, Matteo Manica, Ruedi Aebersold, Chen Li

Briefings Bioinform., 2022

A Fully Differentiable Set Autoencoder.

Nikita Janakarajan, Jannis Born, Matteo Manica

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Multitask Prompted Training Enables Zero-Shot Task Generalization.

Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, Alexander M. Rush

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

On the role of artificial intelligence in medical imaging of COVID-19.

Jannis Born, David Beymer, Deepta Rajan, Adam Coy, Vandana V. Mukherjee, Matteo Manica, Prasanth Prasanna, Deddeh Ballah, Michal Guindy, Dorith Shaham, Pallav L. Shah, Emmanouil Karteris, Jan L. Robertus, Maria Gabrani, Michal Rosen-Zvi

Patterns, 2021

Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2.

Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, Nikita Janakarajan, Antonio Cardinale, Teodoro Laino, María Rodríguez Martínez

Mach. Learn. Sci. Technol., 2021

Multitask Prompted Training Enables Zero-Shot Task Generalization.

Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, Alexander M. Rush

CoRR, 2021

On the feasibility of deep learning applications using raw mass spectrometry data.

Joris Cadow, Matteo Manica, Roland Mathis, Tiannan Guo, Ruedi Aebersold, María Rodríguez Martínez

Bioinform., 2021

Understood in Translation: Transformers for Domain Understanding.

Dimitrios Christofidellis, Matteo Manica, Leonidas Georgopoulos, Hans Vandierendonck

Proceedings of the Workshop on Scientific Document Understanding co-located with 35th AAAI Conference on Artificial Inteligence, 2021

2020

FPGA Accelerated Analysis of Boolean Gene Regulatory Networks.

Matteo Manica, Raphael Polig, Mitra Purandare, Roland Mathis, Christoph Hagleitner, María Rodríguez Martínez

IEEE ACM Trans. Comput. Biol. Bioinform., 2020

PaccMann: a web service for interpretable anticancer compound sensitivity prediction.

Joris Cadow, Jannis Born, Matteo Manica, Ali Oskooei, María Rodríguez Martínez

Nucleic Acids Res., 2020

Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks.

Modestas Filipavicius, Matteo Manica, Joris Cadow, María Rodríguez Martínez

CoRR, 2020

PaccMann^RL on SARS-CoV-2: Designing antiviral candidates with conditional generative models.

Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, María Rodríguez Martínez

CoRR, 2020

Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue.

Pierre Colombo, Emile Chapuis, Matteo Manica, Emmanuel Vignon, Giovanna Varni, Chloé Clavel

CoRR, 2020

PaccMann^RL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning.

Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, María Rodríguez Martínez

Proceedings of the Research in Computational Molecular Biology, 2020

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models.

Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Hierarchical Pre-training for Sequence Labelling in Spoken Dialog.

Emile Chapuis, Pierre Colombo, Matteo Manica, Matthieu Labeau, Chloé Clavel

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction.

Pierre Colombo, Emile Chapuis, Matteo Manica, Emmanuel Vignon, Giovanna Varni, Chloé Clavel

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Context-specific interaction networks from vector representation of words.

Matteo Manica, Roland Mathis, Joris Cadow, María Rodríguez Martínez

Nat. Mach. Intell., 2019

AI Enables Explainable Drug Sensitivity Screenings.

Matteo Manica, Ali Oskooei, Jannis Born

ERCIM News, 2019

Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles.

Jannis Born, Matteo Manica, Ali Oskooei, María Rodríguez Martínez

CoRR, 2019

An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries.

Matteo Manica, Christoph Auer, Valéry Weber, Federico Zipoli, Michele Dolfi, Peter W. J. Staar, Teodoro Laino, Costas Bekas, Akihiro Fujita, Hiroki Toda, Shuichi Hirose, Yasumitsu Orii

CoRR, 2019

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders.

Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

CoRR, 2019

NeuNetS: An Automated Synthesis Engine for Neural Network Design.

Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir R. Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati

CoRR, 2019

2018

Exploring Multi-Modal Learning Approaches Towards Precision Medicine.

Matteo Manica

PhD thesis, 2018

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks.

Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

CoRR, 2018

Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer.

Ali Oskooei, Matteo Manica, Roland Mathis, María Rodríguez Martínez

CoRR, 2018

2017

Mixed-Precision Memcomputing.

Manuel Le Gallo, Abu Sebastian, Roland Mathis, Matteo Manica, Tomas Tuma, Costas Bekas, Alessandro Curioni, Evangelos Eleftheriou

CoRR, 2017