A new mutation mechanism was found in human and bacterial genomes
An international research team has found a new replacement mechanism that causes mutations in both humans and bacteria. The mechanism can cause several changes to a short stretch of DNA simultaneously. The research was conducted by observing fragments of DNA sequence that contained plenty of mutations.
ELFI: Engine for Likelihood-Free Inference
Machine Learning Coffee Seminar
Starting January 9, Helsinki region machine learning researchers will start our week by an exciting machine learning talk and discussion over coffee before and after the talk. The talks will start 9:15, with coffee served from 9:00. http://www.hiit.fi/mlseminar
EEG reveals information essential to users
For the first time, information retrieval is possible with the help of EEG interpreted with machine learning.
In a study conducted by the Helsinki Institute for Information Technology (HIIT) and the Centre of Excellence in Computational Inference (COIN), laboratory test subjects read the introductions of Wikipedia articles of their own choice. During the reading session, the test subjects’ EEG was recorded, and the readings were then used to model which key words the subjects found interesting.
Preethi Lahoti feels very privileged to be part of the Data Mining research group
Honours programme student Preethi Lahoti conducts research in graph mining and social-networks analysis.
Exceptionally qualified Master’s students have joined the honours programme in computer science. Altogether 15 Master’s students from all over the world will have hands-on experience in the actual computer science research. Majority of the honours programme students are specialized in the machine learning, data mining and probabilistic modelling research area.
Model checking verifies the correctness of nuclear power plant safety systems
The study utilises model checking to address the insufficiencies of testing and simulation in the verification of safety systems.
The object of Jussi Lahtinen’s dissertation was to find a more formal and mathematical approach to system verification and to develop model checking practices that are suitable for the nuclear industry. The traditional system verification methods, such as testing and simulation, do not have enough coverage to address the increasing digitalisation of safety automation systems.
Regression modelling reconstructs weather forecasts for the past from animal teeth
Research data was collected from Kenyan national parks over the past 60 years, combined with traits of the teeth of herbivorous mammals.
In the new study, the annual rainfall and average temperatures in the national parks are inferred from the teeth of herbivorous mammals. Such reverse engineering opens up new opportunities for interpreting fossil records. The results were recently published in the journal PNAS.
Giant leap in ABC inference scalability
HIIT scientists Michael Gutmann and Corander published a machine learning based ABC inference approach in the Journal of Machine Learning Research. Their method (BOLFI) is based on Bayesian optimization with Gaussian processes and is generally applicable to simulator models with intractable likelihoods. Without sacrificing accuracy, BOLFI speeds up posterior computation by 3-4 orders of magnitude compared with the state-of-the-art sequential Monte Carlo algorithms.
HIIT participated to the 10th International Workshop on Machine Learning in Systems Biology
Fri, 16.09.201610th International Workshop on Machine Learning in Systems BiologySeptember 3-4 2016, World Forum, The Hague
The tenth edition of MLSB was organized as a two-day satellite meeting before ECCB2016, the European Conference on Computational Biology (September 5-7, 2016) by Dick de Ridder and Aalt-Jan van Dijk (Wageningen University, The Netherlands) and Juho Rousu and Harri Lähdesmäki (Aalto University, Finland) at the World Foru
HIIT scientists present the most advanced GWAS method for bacteria to date
A HIIT-wide team led by professor Jukka Corander included several scientists from both UH and Aalto with a joint mission to create the most advanced and computationally best scalable method for genome-wide association (GWAS) studies in bacteria. The team had a close collaboration with the Pathogen Genomics Group at the Wellcome Trust Sanger Institute where GWAS is an important step towards unraveling the secrets behind evolution and success of numerous major human pathogens from large-scale population genomic data.