Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation

in Scientometrics (2021)

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a ... [more ▼]

Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a field and between fields based on manual inspection impossible. Automatic techniques to support the process of literature review are required to find the epistemic and social patterns that are embedded in scientific publications. In computer sciences, new tools have been developed to deal with large volumes of data. In particular, deep learning techniques open the possibility of automated end-to-end models to project observations to a new, low-dimensional space where the most relevant information of each observation is highlighted. Using deep learning to build new representations of scientific publications is a growing but still emerging field of research. The aim of this paper is to discuss the potential and limits of deep learning for gathering insights about scientific research articles. We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs). We explore the different outcomes generated by those techniques. Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded. [less ▲]

Towards optimal decomposition of Boolean networks

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021), 18(6), 2167-2176

Proceedings of the AI4Health Lecture Series (2021)

Scientific Conference (2021)

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one ... [more ▼]

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one hand, this is due to the emergence of medical mass data and their use for AI-related fields, such as machine learning, human-computer interfaces and natural language-processing systems, and on the other hand, it is also due to the steadily growing social interest, which is not determined by the current Covid 19 pandemic. To this end, the lecture series is intended to provide an opportunity for scientific exchange. [less ▲]

An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

in Big Data and Cognitive Computing (2021), 5(1), 5

CABEAN: a software for the control of asynchronous Boolean networks

in Bioinformatics (2021), 36(6), 879-881

Hilbert Sinkhorn Divergence for Optimal Transport

in Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition - CVPR'21 (2021)

Proceedings of the 6th Global Conference on Artificial Intelligence (GCAI 2020)

in 6th Global Conference on Artificial Intelligence (2020, May)

An efficient approach towards the source-target control of Boolean networks

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020), 17(6), 1932-1945

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target ... [more ▼]

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimised if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach. [less ▲]

A Dynamics-based Approach for the Target Control of Boolean Networks

in Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (2020)

Preface (Special section on software systems 2020)

in Journal of Computer Science and Technology (2020), 35(6), 1231-1233