User:Leo Lahti/Publications

Publications by Leo Lahti (order RSS feed)

Investigation, development, and application of novel computational techniques for data integration and analysis. In particular, adaptive learning algorithms that can be used to discover relevant structure in the observed data with minimal human intervention. The main application fields are functional genomics and open data initiatives. Open source implementations are provided to enhance transparency, reproducibility, and accelerated adoption of the new methods.

Journal articles

1. Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review. Leo Lahti, Martin Schäfer, Hans-Ulrich Klein, Silvio Bicciato and Martin Dugas. Briefings in Bioinformatics. In press. (arXiv preprint; Source code).
2. Intestinal microbiota in healthy adults: Temporal analysis reveals individual and common core and relation to digestive symptoms. Jonna Jalanka-Tuovinen, Anne Salonen, Janne Nikkilä, Outi Immonen, Riina Kekkonen, Leo Lahti, Airi Palva and Willem M. de Vos. PLoS One 6(7):e23035, 2011.
3. Integrative Analysis of microRNA, mRNA and aCGH Data Reveals Asbestos- and Histology-Related Changes in Lung Cancer. Penny Nymark, Mohamed Guled, Ioana Borze, Ali Faisal, Leo Lahti, Kaisa Salmenkivi, Eeva Kettunen, Sisko Anttila and Sakari Knuutila. Genes, Chromosomes and Cancer 50(8):585-597, 2011.
4. Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays. Leo Lahti, Laura L. Elo, Tero Aittokallio, and Samuel Kaski. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(1):217-225, 2011. (abstract; preprint (pdf); R/BioConductor package (RPA)).
5. Global modeling of transcriptional responses in interaction networks. Leo Lahti, Juha E.A. Knuuttila, and Samuel Kaski. Bioinformatics 26(21):2713-2720, 2010. abstract; preprint (pdf); preprint (arXiv); implementation in R and Matlab)
6. Array Comparative Genomic Hybridization Reveals Frequent Alterations of G1/S Checkpoint Genes in Undifferentiated Pleomorphic Sarcoma of Bone. Tarja Niini, Leo Lahti, Francesca Michelacci, Shinsuke Ninomiya, Claudia Maria Hattinger, Mohamed Guled, Tom Böhling, Piero Picci, Massimo Serra, and Sakari Knuutila. Genes, Chromosomes and Cancer 50(5)291-306, 2011.
7. MicroRNA microarrays on archive bone marrow core biopsies of leukemias - method validation. Ioana Borze, Mohamed Guled, Suad Musse, Anna Raunio, Erkki Elonen, Ulla Saarinen-Pihkala, Marja-Liisa Karjalainen-Lindsberg, Leo Lahti, and Sakari Knuutila. Leukemia Research 35(2)188-195, 2011.
8. Unique microRNA profile in Dupuytren's contracture supports deregulation of β-catenin pathway. Neda Mosakhani, Mohamed Guled, Leo Lahti, Ioana Borze, Minna Forsman, Jorma Ryhänen, and Sakari Knuutila. Modern Pathology 23:1544-1522, 2010. The International Dupuytren Award 2011
9. CDKN2A, NF2 and JUN Are Dysregulated Among Other Genes by miRNAs in Malignant Mesothelioma - a miRNA Microarray Analysis. Mohamed Guled, Leo Lahti, Pamela M Lindholm, Kaisa Salmenkivi, Izhar Bagwan, Andrew G Nicholson and Sakari Knuutila. Genes, Chromosomes and Cancer 48:615-23, 2009. (full text)
10. Gene Expression Profiles in Asbestos-exposed Epithelial and Mesothelial Lung Cell Lines. Penny Nymark, Pamela M Lindholm, Mikko V Korpela, Leo Lahti, Salla Ruosaari, Samuel Kaski, Jaakko Hollmen, Sisko Anttila, Vuokko L Kinnula, and Sakari Knuutila. BMC Genomics 8:62, 2007 (abstract)
11. Integrating probe-level expression changes across generations of Affymetrix arrays. Laura L. Elo, Leo Lahti, Heli Skottman, Minna Kyläniemi, Riitta Lahesmaa and Tero Aittokallio. Nucleic Acids Research 2005 33(22):e193 (abstract, full text, pdf, check also R package written by L. Elo)
12. Associative clustering for exploring dependencies between functional genomics data sets. Samuel Kaski, Janne Nikkilä, Janne Sinkkonen, Leo Lahti, Juha E.A. Knuuttila, and Christophe Roos. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Special Issue on Machine Learning for Bioinformatics -- Part 2, 2(3):203-216, 2005 (abstract, pdf. This is the most thorough description of associative clustering, including two bioinformatics case studies.)

Conference Proceedings

1. Dependency detection with similarity constraints. Leo Lahti, Samuel Myllykangas, Sakari Knuutila, and Samuel Kaski. In Tülay Adali, Jocelyn Chanussot, Christian Jutten, and Jan Larsen, editors, Proceedings of the 2009 IEEE International Workshop on Machine Learning for Signal Processing XIX, pages 89--94. IEEE, Piscataway, NJ, USA, 2009. (pdf; arXiv preprint; R/BioConductor package (pint))
2. Associative Clustering. Janne Sinkkonen, Janne Nikkilä, Leo Lahti and Samuel Kaski. ECML 2004, Pisa, Italy. In: Boulicaut, Esposito, Giannotti, Pedreschi (eds.): Machine Learning: ECML2004 (Proceedings of 15th European Conference on Machine Learning), Lecture Notes in Computer Science 3201, pages 396-406, 2004. (abstract, pdf) ©Springer-Verlag

Theses

• D.Sc. (Tech.), Probabilistic analysis of the human transcriptome with side information, PhD thesis; Aalto University School of Science and Technology, Faculty of Information and Natural Sciences, Espoo, Finland, 2010. (Väitöstiedote (pdf), Summary, electronic version, LaTeX sources, pdf). Thesis introduction released with CC-BY license.

• M.Sc. (Tech.), Comparative functional genome analysis using associative clustering (M.Sc. thesis; in Finnish), Helsinki University of Technology, Department of Engineering Physics and Mathematics, 2003. (pdf).

• B.Sc. Simon Blackburnin kvasi-realismi ja moraalisen keskustelun turvaaminen (Concerning Blackburn's quasi-realism in securing moral discussion; B.Sc. thesis, in Finnish), University of Helsinki, Faculty of political sciences, 2009. (LaTex; pdf). CC-BY license.

Scientific Open Source Software

• Benchmarking pipeline for integrative cancer gene discovery algorithms (intcomp). R-forge. An R package for quantitative comparison of various cancer gene discovery algorithms based on integrative analysis of gene expression and copy number data (arXiv preprint).

• Functional network analysis (netresponse). R/BioConductor, Matlab Probabilistic algorithms for global modeling of context-specific network activation patterns. Provides tools to study transcriptional responses in genome-scale interaction networks across organism-wide collections of gene expression data.(Bioinformatics 2011 abstract; preprint with supplementary figures (pdf))

• Probabilistic data integration for functional genomics (pint) R/BioConductor Integrative analysis of high-throughput RNA and DNA profiling data for cancer gene discovery and functional analysis of chromosomal aberrations. (MLSP 2009 pdf; arXiv preprint)

• Robust probabilistic averaging (RPA): R/BioConductor for probe reliability analysis and gene expression preprocessing. Recommended preprocessing method for Affymetrix data (TCBB/IEEE 2011 arXiv application note, journal preprint).

• Dependency modeling toolkit (DMT) R/CRAN. Unified framework for probabilistic dependency models. Probabilistic versions of PCA, factor analysis and CCA, their regularized variants, dependency-based dimensionality reduction etc (ICML/MLOSS workshop 2010).

• Chipster contributions Contributed tools to the Chipster bioinformatics analysis platform maintained by the Finnish IT Center for Science (CSC)

• sorvi - avoimen datan työkalupakki R-Forge. Analysis tools for Open government data in Finland. Apps4Finland 2011 double award - Data Opening Category: both the official competition and the public votes (Apps4Finland presentation slides, in Finnish)

Technical Reports and Workshop papers

• Biomarker discovery via dependency analysis of multi-view functional genomics data. A Faisal, R Louhimo, L Lahti, S Kaski, S Hautaniemi. Personalized medicine workshop, NIPS 2011, Granada, Spain, December 2011. Spotlight presentation.

• Probabilistic dependency models for data integration in functional genomics. L Lahti and S Kaski. Machine Learning for Systems Biology workshop, ISMB, Vienna, Austria, July 2011.

• Systematic Use of Computational Methods Allows Stratifying Treatment Responders in Glioblastoma Multiforme. R Louhimo, V Aittomäki, A Faisal, M Laakso, P Chen, K Ovaska, E Valo, L Lahti, V Rogojin, S Kaski and S Hautaniemi. Critical Assessment of Massive Data Analysis (CAMDA) workshop, ISMB, Vienna, Austria, July 2011.

• Associative Clustering (AC): Technical Details. J Sinkkonen, S Kaski, J Nikkilä, and L Lahti. Technical Report A84, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, April 2005. (ps, pdf; accompanying report including only additional technical details and derivations of the method.)

• Associative Clustering by Maximizing a Bayes Factor. J Sinkkonen, J Nikkilä, L Lahti and S Kaski. Technical Report A68, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, June 2003. (postscript)

Selected Posters

• Meta-analysis of human gut microbiota: Community composition and health associations ISMB, Vienna 2011.

• Combining multiple data sources in functional genomics for improving genome-wide inferences SYSBIO symposium, Helsinki, Finland 2007.

• Modeling gene expression in biological networks ECCB 2008, Cagliari, September 2008.

• Probabilistic analysis of probe performance on short nucleotide arrays ISMB, Vienna, 2007.

• Method for exploring dependencies in the expression of orthologous man-mouse gene pairs SYSBIO Symposium, Meripuisto, Finland, 2005. Best poster award.

Other material

• Presentation slides available through SlideShare

• A brief overview on the BioPAX and SBML standards for machine-readable representations of cell-biological processes. (arXiv:1109.4919v1). CC-BY licence.

• A quantum computing survey (2001 (doc)).

• Julian joukko iteraatioden valuma-altaan reunana (Matematiikan erikoistyö, 2001 pdf, tex). CC-BY licence.

• Itsesimilaaristen joukkojen Hausdorffin dimension määrittäminen (Matematiikan erikoistyö, 2002 pdf, tex). CC-BY licence.

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