Joana Gonçalves bio photo

Joana Gonçalves

Computer scientist. Interests: algorithms, data mining, machine learning, computational biology.


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Career and Research

Short biography

In October 2014 I joined both the Pattern Recognition and Bioinformatics group at the Delft University of Technology (PRB-TUDelft) and the Computational Cancer Biology group at The Netherlands Cancer Institute (CCB-NKI) as a postdoctoral fellow, working with Lodewyk Wessels. The year before I was as an ERCIM / Marie Curie postdoctoral fellow in the Algorithmic Computational Biology group led by Gunnar Klau at the Netherlands Centre for Mathematics and Computer Science (CWI). Previously, I received a PhD in computer science from the Technical University of Lisbon. I conducted my doctoral researcher at INESC-ID, was a visiting scholar at the University of Leuven, and a research assistant at Imperial College London. My research interests include algorithms, data mining, machine learning and computational biology. I specialise in combinatorial pattern mining for biclustering and triclustering of time series data.

Research topics

I develop data mining methods, especially using combinatorial problem formulations, for relevant knowledge discovery challenges in different application domains. My current research topics are:

  • local pattern mining and subspace clustering (of time series data)
  • integrative mining of heterogeneous data
  • construction of global models from local patterns

Application domains

Most of my research has been applied within the fields of molecular biology and functional genomics to further the understanding of biological processes and perturbations of these leading to disease. In particular, I have addressed the analysis of gene expression, TF-target binding or protein interaction data, as well as combinations of these, to identify response patterns and functional modules potentially involved in:

  • response of yeast to different environmental stresses (microbiology)
  • human medical conditions, namely cancer and neurodegenerative diseases (biomedicine)

Some of the methods I developed are independent from the application domain and can be used in other research fields:

  • economic and financial sciences (e.g. health insurance, fraud detection)
  • social science (e.g. behavioral patterns, information and disease spreading)
  • education and research (e.g. impact of funding in research, return on investment)