Bioinformatics of carbonic anhydrases
The focus of this research consists of bioinformatic studies of carbonic anhydrases (CA), ubiquitous enzymes which are found in all kingdoms of life, focusing on CAs in animals. The ultimate goal is to increase our understanding of how and why various CA isoforms and CAs in different species are different from each other. This would be useful in understanding drug specificity, and in the design of novel, improved forms of CAs. An intermediate goal in this process is the discovery of most likely evolutionary histories of CAs.
The project is run by Martti Tolvanen in collaboration with the group of Seppo Parkkila (University of Tampere).
Conserved domain and evolution of secreted phospholipases A(2)
Secreted phospholipases A(2) (sPLA(2) s) are lipolytic enzymes present in organisms ranging from prokaryotes to eukaryotes but their origin and emergence are poorly understood. We study conserved domains of sPLA(2)
and proposed a model for their evolution.
Protein hydropathy predictions
We investigated the prediction accuracy of 56 hydropathy scales by correlating predicted values with the accessible surface area in known protein structures. Results for different amino acids vary greatly within each scale. We also investigated prediction accuracies of amino acids separately in secondary structural elements and in protein fold families.
Gene expression on the theory of phase synchronization
Using phase synchronization it is possible to detect biological
associations for gene pairs with cell cycle-specific expression profiles. Phase-synchronization clustering is able to detect biologically associated gene pairs that have linearly correlated (simultaneous and inverted) as well as time-delayed expression profiles.
Tools for the submission of mutations to databases and maintenance of locus-specific mutation databases. Advanced, integrated computer systems are needed to store and organize the increasing mutation information.
Turku BioNLP Group
The main focus of the BioNLP group is the development of text mining algorithms and their utilization in knowledge discovery in biology and medicine. The flagship resources developed by the group are the TEES text mining system which won several text mining competitions, and the EVEX resource comprising detailed information about genes, proteins, and their mutual relationships mined from the entire publicly available biomedical literature. The BioNLP group has a number of collaborative projects with universities worldwide.
More information about the group’s projects and publications can be found on its homepage.
Research utilizing the Auria biobank data
The Auria biobank is a project to centralize the storage of patient samples and related information from Turku area hospitals and to provide the means for scientists to utilize this large dataset for research purposes. The IT-department is involved in the Auria-project and we will utilize our expertise in machine learning and large scale data mining to apply the biobank data to different research questions.
A System for Identifying and Exploring Text Repetition in Large Historical Document Corpora
Aleksi Vesanto, Asko Nivala, Tapio Salakoski, Hannu Salmi, Filip Ginter
Applying BLAST to Text Reuse Detection in Finnish Newspapers and Journals, 1771–1910
Aleksi Vesanto, Asko Nivala, Heli Rantala, Tapio Salakoski, Hannu Salmi, Filip Ginter
How Reliable are Trial-based Prognostic Models in Real-world Patients with Metastatic Castration-resistant Prostate Cancer?
Fatemeh Seyednasrollah, Mehrad Mahmoudian, Liisa Rautakorpi, Outi Hirvonen, Tarja Laitinen, Sirkku Jyrkkiö, Laura L. Elo
Tactile Maps - Safety and Usability
Ojala S, Lahtinen R, Hirn H
Medical Warning System Based on Internet of Things Using Fog Computing
Iman Azimi, Arman Anzanpour, Amir M. Rahmani, Pasi Liljeberg, and Tapio Salakoski
Overview of the CLEF eHealth Evaluation Lab 2016
Liadh Kelly, Lorraine Goeuriot, Hanna Suominen, Aurélie Névéol, João Palotti, Guidon Zuccon
Universal Dependencies v1: A Multilingual Treebank Collection
Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajic, Christopher D. Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, Reut Tsarfaty, Daniel Zeman
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks
Juhani Luotolahti, Jenna Kanerva, Filip Ginter
How students’ programming process differs from experts – a case study with a robot programming exercise
E. Lokkila, T. Rajala, A. Veerasamy, P. Enges-Pyykönen, M.J. Laakso, T. Salakoski
Technologically Enhanced Lectures
T. Rajala, A. Aine, P. Larsson, R. Lindén, M.-J. Laakso