Computer Science and Engineering
 Gothenburg University | Chalmers

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Publications (2002-)


2018

An Optimization Problem Related to Bloom Filters With Bit Patterns. P. Damaschke and A. Schliep. In SOFSEM 2018: Theory and Practice of Computer Science, Springer, Jan 2018. To appear. [details]


2017

Automatic blood glucose prediction with confidence using recurrent neural networks. C. Meijner, S. Persson, A. Schliep, B. Eliasson and O. Mogren. In Sep 2017. Under review. [details]

Probabilistic Modelling of Sensors in Autonomous Vehicles — Autoregressive Input/Output Hidden Markov Models for Time Series Analysis. E. Listo Zec. Master's Thesis, Chalmers University of Technology, May 2017. [details]


2016

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression. J. Wiedenhoeft, E. Brugel and A. Schliep. In Research in Computational Molecular Biology: 20th Annual Conference, RECOMB 2016, Santa Monica, CA, USA, April 17-21, 2016, Proceedings, Springer, 9649, 263, 2016. [details] [pdf] [supp]

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression. J. Wiedenhoeft, E. Brugel and A. Schliep. PLoS Computational Biology 2016, 12:5, e1004871. [details] [pdf] [supp] [PubMed]

Automatic learning of pre-miRNAs from different species. I.O.N. Lopes, A. Schliep and A.P.L.F. Carvalho. BMC Bioinformatics 2016, 17:224. [details] [pdf] [PubMed]


2015

Automatic learning of pre-miRNAs from different species. I.O.N. Lopes, A. Schliep and A.P.L.F. Carvalho. ArXiv 2015. arXiv:1508.00412. [details] [pdf]

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression. J. Wiedenhoeft, E. Brugel and A. Schliep. biorXiv 2015. [details] [pdf]


2014

Turtle: Identifying frequent k-mers with cache-efficient algorithms. R.S. Roy, D. Bhattacharya and A. Schliep. Bioinformatics 2014, 14:30, 1950–7. [details] [pdf] [PubMed]

Studying the single life of eukaryotic microbes: Single cell genomics of marine plankton. D. Bhattacharya, R.S. Roy, D.C. Price and A. Schliep. Biochemist Magazine 2014, 36:1. [details]

TreQ-CG: Clustering Accelerates High-Throughput Sequencing Read Mapping. M. Mahmud and A. Schliep. arxiv 2014. [details] [pdf] [supp]

The discriminant power of RNA features for pre-miRNA recognition. I.O.N. Lopes, A. Schliep and A.P.L.F. Carvalho. BMC Bioinformatics 2014, 15:124. [details] [pdf] [PubMed]

Single cell genome analysis of an uncultured heterotrophic stramenopile. R.S. Roy, D.C. Price, A. Schliep, G. Cai, A. Korobeynikov, E.C. Yang and D. Bhattacharya. Sci Rep 2014, 4:4780. [details] [pdf] [PubMed]

Improving genome assembly by identifying reliable sequencing data. R.S. Roy. Ph.D. Thesis, Oct 2014. [details] [pdf]

Reduced representations for efficient analysis of genomic data; from microarray to high throughput sequencing. M. Mahmud. Ph.D. Thesis, Oct 2014. [details] [pdf]


2013

The discriminant power of RNA features for pre-miRNA recognition. I.O.N. Lopes, A. Schliep and A.P.L.F. Carvalho. Arxiv 2013. arXiv:1312.5778. [details] [pdf]

Turtle: Identifying frequent k-mers with cache-efficient algorithms.. R.S. Roy, D. Bhattacharya and A. Schliep. 2013. Arxiv. [details] [pdf]


2012

SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding. R.S. Roy, K. Chen, A. Sengupta and A. Schliep. Journal of Computational Biology 2012, 19, 1162–75. [details] [pdf] [PubMed]

CLEVER: Clique-Enumerating Variant Finder. T. Marshall, I. Costa, S. Canzar, M. Bauer, G. Klau, A. Schliep and A. Schönhuth. Bioinformatics 2012. Accepted for Publication.. [details] [pdf] [PubMed]

From TER to trans- and paracellular resistance: Lessons from impedance spectroscopy. D. Günzel, S.S. Zakrzewski, T. Schmid, M. Pangalos, J. Wiedenhoeft, C. Blasse, C. Ozboda and S.M. Krug. Annals of the New York Academy of Science 2012, 1257, 142–151. [details] [PubMed]

Indel-tolerant Read Mapping with Trinucleotide Frequencies using Cache-Oblivious kd-Trees. M. Mahmud, J. Wiedenhoeft and A. Schliep. Bioinformatics 2012, 28:18, i325–i332. [details] [pdf] [supp] [PubMed]

CLEVER: Clique-Enumerating Variant Finder. T. Marshall, I. Costa, S. Canzar, M. Bauer, G. Klau, A. Schliep and A. Schönhuth. 2012. Arxiv. [details]


2011

Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions. C. Hafemeister, I.G. Costa, A. Schönhuth and A. Schliep. Bioinformatics 2011, 27:7, 946–52. [details] [pdf] [PubMed]

Selecting oligonucleotide probes for whole-genome tiling arrays with a cross-hybridization potential. C. Hafemeister, R. Krause and A. Schliep. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011. In press. [details] [pdf] [PubMed]

pGQL: A Probabilistic Graphical Query Language for Gene Expression Time Courses. R.B. Schilling, I.G. Costa and A. Schliep. BioData Mining 2011, 4:9. [details] [pdf] [PubMed]

Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models. M. Seifert, M. Strickert, A. Schliep and I. Grosse. Bioinformatics 2011, 27:12, 1645–1652. [details] [PubMed]

Speeding Up Bayesian HMM by the Four Russians Method. M. Mahmud and A. Schliep. In Algorithms in Bioinformatics, Springer Berlin / Heidelberg, 6833, 188–200, 2011. [details] [pdf]

Fast MCMC Sampling for Hidden Markov Models to Determine Copy Number Variations. M. Mahmud and A. Schliep. BMC Bioinformatics 2011, 12:1, 428. [details] [pdf] [PubMed]

The Plexus Model for the Inference of Ancestral Multi-Domain Proteins. J. Wiedenhoeft, R. Krause and O. Eulenstein. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011, 8:4, 890–901. [details] [supp] [PubMed]

Cocos: Constructing multi-domain protein phylogenies. M. Homilius, J. Wiedenhoeft, S. Thieme, C. Standfuß, I. Kel and R. Krause. PLoS Currents: Tree of Life 2011. [details] [PubMed]

SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding. R.S. Roy, K. Chen, A. Sengupta and A. Schliep. 2011. Arxiv. [details]


2010

PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data. B. Georgi, I. Gesteira Costa and I. Schliep. BMC Bioinformatics 2010, 11:9. [details] [pdf] [PubMed]

CATBox – An Interactive Course in Combinatorial Optimization. W. Hochstättler and A. Schliep. Springer, 2010. [details]

Inferring Evolutionary Scenarios for Protein Domain Compositions. J. Wiedenhoeft, R. Krause and O. Eulenstein. In 6th International Symposium on Bioinformatics Research and Applications, Springer, 6053, 179–190, 2010. [details]


2009

Constrained Mixture Estimation for Analysis and Robust Classification of Clinical Time Series. I.G. Costa, A. Schönhuth, C. Hafemeister and A. Schliep. Bioinformatics 2009, 12:25, i6–14. (ISMB 2009). [details] [pdf] [supp] [PubMed]

Context-specific Independence Mixture Models for Cluster Analysis of Biological Data. B. Georgi. Ph.D. Thesis, Freie Universität Berlin, Jun 2009. [details] [pdf]

Partially-supervised protein subclass discovery with simultaneous annotation of functional residues. B. Georgi, J. Schultz and A. Schliep. BMC Struct Biol. 2009, 9:68. [details] [pdf] [PubMed]

Semi-supervised Clustering of Yeast Gene Expression. A. Schönhuth, I.G. Costa and A. Schliep. In Cooperation in Classification and Data Analysis, Springer, 151–160, 2009. Proceedings of Two German-Japanese Workshops . [details] [pdf]


2008

Comparative Study on Normalization Procedures for Cluster Analysis of Gene Expression Datasets. M.C.P. de Souto, D.A.S. Araujo, I.G. Costa, R.G.F. Soares, T.B. Ludermir and A. Schliep. In Proceedings of the International Joint Conference on Neural Networks, IEEE Computer Society, 2008. [details] [pdf]

Ranking and Selecting Clustering Algorithms Using a Meta-learning Approach. M.C.P. de Souto, R.B.C. Prudencio, R.G.F. Soares, D.A.S. Araujo, I.G. Costa, T.B. Ludermir and A. Schliep. In Proceedings of the International Joint Conference on Neural Networks, IEEE Computer Society, 2008. [details] [pdf]

Efficient algorithms for the computational design of optimal tiling arrays. A. Schliep and R. Krause. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2008, 557–567. [details] [pdf] [PubMed]

Efficient Computation of Probe Qualities. C. Hafemeister. Master's Thesis, Freie Universität Berlin, May 2008. [details] [pdf]

Clustering cancer gene expression data: a comparative study. M. de Souto, I.G. Costa, D. de Araujo, T. Ludermir and A. Schliep. BMC Bioinformatics 2008, 9:1, 497. [details] [pdf] [PubMed]

Inferring differentiation pathways from gene expression. I.G. Costa, S. Roepcke, C. Hafemeister and A. Schliep. Bioinformatics 2008, 24:13, i156–164. [details] [pdf] [supp] [PubMed]

New, improved, and practical k-stem sequence similarity measures for probe design. A. Macula, A. Schliep, M. Bishop and T. Renz. J. Comput. Biol. 2008, 15, 525–534. [details] [pdf] [PubMed]

Mixture Models for the Analysis of Gene Expression: Integration of Multiple Experiments and Cluster Validation. I.G. Costa. Ph.D. Thesis, Freie Universität Berlin, May 2008. [details] [pdf]


2007

Elastic registration in 3D volume data. R. Schilling. Master's Thesis, Universität Freiburg, 2007. [details]

Validating Gene Clusterings by Selecting Informative Gene Ontology Terms with Mutual Information. I.G. Costa, M.C.P. de Souto and A. Schliep. In Advances in Bioinformatics and Computational Biology, Proceedings of the Brazilian Symposium on Bioinformatics, Springer Verlag, 81–92, 2007. [details] [pdf]

Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data. I.G. Costa, R. Krause, L. Optiz and A. Schliep. BMC Bioinformatics 2007, 8, S3. [details] [pdf] [supp] [PubMed]

Gene expression trees in lymphoid development. I. Costa, S. Roepcke and A. Schliep. BMC Immunol 2007, 8:1, 25 . [details] [pdf] [supp] [PubMed]

Partially-supervised context-specific independence mixture modeling. B. Georgi and A. Schliep. In workshop on Data Mining in Functional Genomics and Proteomics, ECML 2007, 2007. [details] [pdf]

Incomplete and inaccurate vocal imitation after knockdown of FoxP2 in songbird basal ganglia nucleus Area X. S. Haesler, C. Rochefort, P. Licznerski, B. Georgi, P. Osten and C. Scharff. PloS Biology 2007, 5:12, e321. [details] [pdf] [PubMed]

Context-Specific Independence Mixture Modelling for Protein Families. B. Georgi, J. Schultz and A. Schliep. In Knowledge Discovery in Databases: PKDD 2007, Springer Berlin / Heidelberg, Volume 4702/2007, 79–90, 2007. [details] [pdf] [supp]

Mixture model based group inference in fused genotype and phenotype data. B. Georgi, M.A. Spence, P. Flodman and A. Schliep. In Studies in Classification, Data Analysis, and Knowledge Organization, Springer, 2007. [details] [pdf] [supp]

Analysis of fused in-situ hybridization and gene expression data. L. Opitz, A. Schliep and S. Posch. In Advances in Data Analysis, Springer, 577–584, 2007. Proceedings of the GfKl 2006. [details] [pdf]

Efficient Computational Design of Tiling Arrays Using a Shortest Path Approach. A. Schliep and R. Krause. In Algorithms in Bioinformatics, Springer Berlin / Heidelberg, Volume 4645/2007, 383–394, 2007. [details] [pdf]

Integer linear programming approaches for non-unique probe selection. G.W. Klau, S. Rahmann, A. Schliep, M. Vingron and K. Reinert. Discrete Appl. Math. 2007, 155:6-7, 840–856. [details] [pdf]

Identifying protein complexes directly from high-throughput TAP data with Markov random fields. W. Rungsarityotin, R. Krause, A. Schodl and A. Schliep. BMC Bioinformatics 2007, 8, 482. [details] [pdf] [supp] [PubMed]

Algorithms to identify protein complexes from high-throughput data. W. Rungsarityotin. Ph.D. Thesis, Freie Universität Berlin, Nov 2007. [details]

A Lattice Model of Basic Diatonic Progressions. J. Wiedenhoeft. In Society for Mathematics and Computation in Music, Staatliches Institut für Musikforschung Berlin, 376–381, 2007. [details]


2006

On the feasibility of Heterogeneous Analysis of Large Scale Biological Data. I.G. Costa and A. Schliep. In Proceedings of ECML/PKDD 2006 Workshop on Data and Text Mining for Integrative Biology, 55–60, 2006. [details] [pdf]

Context-specific independence mixture modeling for positional weight matrices. B. Georgi and A. Schliep. Bioinformatics 2006, 22:14, e166–e173. [details] [pdf] [PubMed]

Structure Learning of Conditional Trees. C. Hafemeister. Bachelor's Thesis, Freie Universität Berlin, Jun 2006. [details] [pdf]

Analyse von Bildern der mRNA-in Situ-Hybridisierung. L. Opitz. Master's Thesis, Martin-Luther-Universität Halle, 2006. [details]

Decoding non-unique oligonucleotide hybridization experiments of targets related by a phylogenetic tree. A. Schliep and S. Rahmann. Bioinformatics 2006, 22:14, e424–e430. [details] [pdf] [PubMed]

Analyzing Microarray Data Using Homogenous and Inhomogenous Hidden Markov Models. M. Seifert. Master's Thesis, Martin-Luther-Universität Halle, 2006. [details]

An indicator for the number of modes in a mixture model using a linear map to simplex structure. M. Weber, W. Rungsarityotin and A. Schliep. In From Data and Information Analysis to Knowledge Engineering, Springer, 103–110, 2006. Proceedings of the GfKl 2005. [details] [pdf]


2005

On external indices for mixtures: validating mixtures of genes. I.G. Costa and A. Schliep. In From Data and Information Analysis to Knowledge Engineering, Springer 2005, 662–669, 2005. [details] [pdf]

The Graphical Query Language: a tool for analysis of gene expression time-courses. I.G. Costa, A. Schönhuth and A. Schliep. Bioinformatics 2005, 21:10, 2544–5. [details] [pdf]

Mixture Modeling and Group Inference in Fused Genotype and Phenotype Data. B. Georgi. Master's Thesis, Freie Universität Berlin, 2005. [details]

Development of a Pair HMM based Gene Finder for the Paramecium Genome. M. Heinig. Master's Thesis, Freie Universität Berlin, 2005. [details]

Analyzing gene expression time-courses. A. Schliep, I.G. Costa, C. Steinhoff and A. Schönhuth. IEEE/ACM Trans Comput Biol Bioinform 2005, 2:3, 179–193. [details] [pdf] [PubMed]

The General Hidden Markov Model Library: Analyzing Systems with Unobservable States. A. Schliep, B. Georgi, W. Rungsarityotin, I.G. Costa and A. Schönhuth. In Forschung und wissenschaftliches Rechnen: Beiträge zum Heinz-Billing-Preis 2004, Gesellschaft für wissenschaftliche Datenverarbeitung, 121–135, 2005. [details] [pdf]

MACAT - MicroArray Chromosome Analysis Tool. J. Tödling, S. Schmeier, M. Heinig, B. Georgi and S. Röpcke. Bioinformatics 2005, 21:9, 2112–2113. [details]


2004

Comparative Analysis of Clustering Methods for Gene Expression Time Course Data. I.G. Costa, F.A.T.D. Carvalho and M.C.P.D. Souto. Genetics and Molecular Biology 2004, 27:4, 623–631. [details]

Discriminative Learning in Hidden Markov Models. J. Grunau. Bachelor's Thesis, Freie Universität Berlin, 2004. [details]

Optimal robust non-unique probe selection using Integer Linear Programming. G.W. Klau, S. Rahmann, A. Schliep, M. Vingron and K. Reinert. Bioinformatics 2004, 20 Suppl 1, i186–i193. [details] [pdf] [PubMed]

Chromosome-wide Expression for Improving ab-initio Gene Prediction. A. Riemer. Bachelor's Thesis, Freie Universität Berlin, 2004. [details]

Graph-based clustering for biological data. W. Rungsarityotin. Master's Thesis, Freie Universität Berlin, 2004. [details]

Robust inference of groups in gene expression time-courses using mixtures of HMMs. A. Schliep, C. Steinhoff and A. Schönhuth. Bioinformatics 2004, 20 Suppl 1, i283–i289. [details] [pdf] [supp] [PubMed]

Perron Cluster Analysis and Its Connection to Graph Partitioning for Noisy Data. M. Weber, W. Rungsarityotin and A. Schliep. Technical report, Zuse Institute Berlin (ZIB), 2004. [details] [pdf]


2003

A Graph-Based Approach to Clustering of Profile Hidden Markov Models. B. Georgi. Bachelor's Thesis, Freie Universität Berlin, 2003. [details]

Selection of Family-Specific Probes for Microarrays. J. Heise. Bachelor's Thesis, Freie Universität Berlin, 2003. [details]

Model-Based Clustering With Hidden Markov Models and its Application to Financial Time-Series Data. B. Knab, A. Schliep, B. Steckemetz and B. Wichern. In Between Data Science and Applied Data Analysis, Springer, 561–569, 2003. Proceedings of the GfKl 2002. [details] [pdf]

Using hidden Markov models to analyze gene expression time course data. A. Schliep, A. Schönhuth and C. Steinhoff. Bioinformatics 2003, 19 Suppl 1, i255–i263. [details] [pdf] [supp] [PubMed]

Group testing with DNA chips: generating designs and decoding experiments. A. Schliep, D.C. Torney and S. Rahmann. Proc IEEE Comput Soc Bioinform Conf 2003, 2, 84–91. [details] [pdf] [PubMed]

Recognition of Circular Permutations in Proteins with Hidden Markov Models. A. Weisse. Bachelor's Thesis, Freie Universität Berlin, 2003. [details]


2002

Selecting signature oligonucleotides to identify organisms using DNA arrays. L. Kaderali and A. Schliep. Bioinformatics 2002, 18:10, 1340–1349. [details] [pdf] [PubMed]

ProClust: improved clustering of protein sequences with an extended graph-based approach. P. Pipenbacher, A. Schliep, S. Schneckener, A. Schönhuth, D. Schomburg and R. Schrader. Bioinformatics 2002, 18 Suppl 2, S182–S191. [details] [pdf] [PubMed]

Developing Gato and CATBox with Python: Teaching graph algorithms through visualization and experimentation. A. Schliep and W. Hochstättler. In Multimedia Tools for Communicating Mathematics, Springer-Verlag, 291–310, 2002. [details] [pdf]