Yli-Harja, Olli - Institute of Signal Processing, Tampere University of Technology

• Computational analysis of disease-related mutation effects on transcription factor binding
• Tampere International Center for Signal Processing. TICSP series # 41
• THE CHALLENGES OF SYSTEMS BIOLOGY A Data Integration Framework for Prediction
• Preprocessing: The data set was preprocessed as follows. First, the replicated background-subtracted
• We first present some example time series created according to Eq. (1) that have a sinusoidal component and additive noise. The example time
• Reconstruction and Validation of RefRec: A Global Model for the Yeast Molecular Interaction Network
• BIOINFORMATICS ORIGINAL PAPER Vol. 25 no. 22 2009, pages 29372944
• Preprint version Systematic analysis of disease-related regulatory mutation
• BioMed Central Page 1 of 16
• Probabilistic Inference of Transcription Factor Binding from Multiple Data Sources
• sBGMM: a stratified Beta-Gaussian mixture model for clustering genes with multiple data sources
• EFFECTS OF DISEASE-RELATED MUTATIONS ON TRANSCRIPTION FACTOR Kirsti Laurila and Harri Lhdesmki
• TESTING FOR DIFFERENTIAL EXPRESSION IN SIMULATED AND REAL CDNA MICROARRAY DATA USING FREQUENTIST AND BAYESIAN METHODS
• BGMM: A BETA-GAUSSIAN MIXTURE MODEL FOR CLUSTERING GENES WITH MULTIPLE DATA SOURCES
• Intrinsic Dimensionality in Gene Expression Analysis Harri Lhdesmki1,2
• TICSP Series # 24 2nd TICSP WORKSHOP ON COMPUTATIONAL
• A joint mixture model for clustering genes from Gaussian and beta distributed data Xiaofeng Dai, Harri Lahdesmaki & Olli Yli-Harja
• Probabilistic transcription factor binding prediction: joint inference from multiple data sources
• Probabilistic Inference of Transcription Factor Binding from Multiple Data Sources Harri Lahdesmaki, Alistair G. Rust & Ilya Shmulevich
• Inferring Dynamic Signalling Networks from Steady State Measurements
• Spectral Methods for Testing Membership in Certain Post Classes and the Class of Forcing Functions
• ROBUST DETECTION OF PERIODICALLY BEHAVING BIOLOGICAL TIME SERIES Miika Ahdesmaki1
• In Silico Microdissection of Microarray Data from Heterogeneous Cell Populations
• Each of the six functional gene sets was then used to perform knowledge-based MDS analyses and it showed that different
• BioMed Central Page 1 of 18
• Probabilistic data fusion for transcription factor binding prediction Harri Lahdesmaki1,2
• Learning the structure of an in vivo gene regulatory network using Gaussian processes
• Inferring Dynamic Signalling Networks from Steady State Measurements
• DECOMPOSING GENE EXPRESSION INTO REGULATORY AND DIFFERENTIAL PARTS WITH BAYESIAN DATA FUSION
• Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology
• Tampereen teknillinen yliopisto. Julkaisu 548 Tampere University of Technology. Publication 548
• PROBABILISTIC FRAMEWORK FOR TRANSCRIPTION FACTOR BINDING Harri Lahdesmaki and Ilya Shmulevich
• A UNIFIED PROBABILISTIC FRAMEWORK FOR CLUSTERING GENES FROM GENE EXPRESSION AND PROTEIN-PROTEIN INTERACTION DATA
• Inferring transcription factor targets from multiple genome-level data sources
• A PROBABILISTIC MODEL FOR COMPETITIVE BINDING OF TRANSCRIPTION Kirsti Laurila1
• Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics
• Computational modeling of transcriptional regulation using multiple heterogeneous data sources Computational modeling of transcriptional
• Genome-wide Profiling of Interleukin-4 and STAT6 Transcription Factor Regulation
• MODELLING TOOL The Modelling Tool can be used to infer Boolean models of genetic reg-
• Tampereen teknillinen yliopisto. Julkaisu 548 Tampere University of Technology. Publication 548
• ROBUST FISHER'S TEST FOR PERIODICITY DETECTION IN NOISY BIOLOGICAL TIME SERIES
• Effects of Disease-related Mutations on Transcription Factor Binding
• Signal Processing 83 (2003) 835858 www.elsevier.com/locate/sigpro