Bioinformatics Lab at MUN

  • We apply machine learning and statistical methods to solve molecular biological problems.
  • We analyze large-scale experimental data to obtain biological insights
  • Looking for our software? Check our github repository
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Lourdes Pena-Castillo

Lourdes Pena-Castillo

Professor

Memorial University

Biography

Dr. Peña-Castillo is a Professor at the Departments of Biology and Computer Science (jointly appointed) at Memorial University Faculty of Science. Her current main area of research is the application of statistical- and machine learning-based methods to decipher bacterial gene regulation. Throughout her academic career, she has developed and/or applied artificial intelligence or machine learning methods in various areas such as biomedical sciences, games, and augmented virtuality.

Interests

  • Bioinformatics
  • Machine Learning
  • Transcriptomics
  • Gene regulation
  • Microbiology

Education

  • Postdoc in Bioinformatics

    University of Toronto, Canada

  • Ph.D. in Computer Science (Doktoringenieurin)

    Otto von Guericke Universitaet Magdeburg, Germany

  • M.Sc. in Computer Science

    University of Alberta, Canada

  • BSc in Information Systems Engineering

    Technological Institute of Monterrey (ITESM), Mexico

Selected Publications

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Learning-induced mRNA alterations in olfactory bulb mitral cells in neonatal rats

In the olfactory bulb, a cAMP/PKA/CREB-dependent form of learning occurs in the first week of life that provides a unique mammalian …

Private rare deletions in SEC16A and MAMDC4 may represent novel pathogenic variants in familial axial spondyloarthritis

OBJECTIVE: Axial spondyloarthritis (AxSpA) represents a group of inflammatory axial diseases that share common clinical and …

Two-color cell array screen reveals interdependent roles for histone chaperones and a chromatin boundary regulator in histone gene repression

We describe a fluorescent reporter system that exploits the functional genomic tools available in budding yeast to systematically …

Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins

Metazoan genomes encode hundreds of RNA-binding proteins (RBPs) but RNA-binding preferences for relatively few RBPs have been well …

Predicting the binding preference of transcription factors to individual DNA k-mers

MOTIVATION: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. …

A library of yeast transcription factor motifs reveals a widespread function for Rsc3 in targeting nucleosome exclusion at promoters

The sequence specificity of DNA-binding proteins is the primary mechanism by which the cell recognizes genomic features. Here, we …

Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences

Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong …

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

BACKGROUND: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of …

Learning Minesweeper with Multirelational Learning

Learning Minesweeper with Multirelational Learning

Probabilities and simulations in poker.

Using Probabilistic Knowledge and Simulation to Play Poker

Using Probabilistic Knowledge and Simulation to Play Poker

Contact

  • lourdes at mun dot ca
  • 709 864 6769
  • Department of Computer Science, Memorial University of Newfoundland, St. John's, NL A1B3X5
  • My office ER6034 is located in the Earth Sciences Building (ER) on Floor 6.