Publications

2024

Evolution shapes and conserves genomic signatures in viruses. M. Holmudden, J. Gustafsson, Y.J.K. Bertrand, A. Schliep and P. Norberg. Communications Biology 7 2024, 1412 (2024). 10.1038/s42003-024-07098-1 PDF

2023

Compressed computations using wavelets for hidden Markov models with continuous observations. L. Bello, J. Wiedenhöft and A. Schliep. PLOS One 2023, 6:18, e0286074. 10.1371/journal.pone.0286074

AI for infectious diseases: Deep regression for VLMC distances.. F. Helmroth and E. Söderpalm. Master's Thesis, Chalmers, Jun 2023.

Predicting Oligonucleotide Thermodynamics using Deep Learning. E. Wingärdh and M. Karlsson. Master's Thesis, Jun 2023.

de Novo Generated Combinatorial Library Design. S.V. Johansson, M. Chehreghani, O. Engkvist and A. Schliep. Digital Discovery 2023, issue 1, 2024, 122–135. First published 27 Nov 2023. 10.1039/D3DD00095H

Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes. S.V. Johansson, O. Engkvist, M. Chehreghani and A. Schliep. In 2023 IEEE International Conference on Big Data (BigData), IEEE Computer Society, 5260–5265, Dec 2023. 10.1109/BigData59044.2023.10386642

2022

Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-Based SVM. S. Tavara, A. Schliep and D. Basu. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II, Springer, 459–467, 2022. 10.1007/978-3-030-93733-1_34 PDF

Using active learning to develop machine learning models for reaction yield prediction. S. Viet Johansson, H. Gummesson Svensson, E. Bjerrum, A. Schliep, M. Haghir Chehreghani, C. Tyrchan and O. Engkvist. Molecular Informatics 2022, 41, 2200043. 10.1002/minf.202200043 PDF

2021

Effects of Network Topology On the Performance of Consensus and Distributed Learning of SVMs Using ADMM. S. Tavara and A. Schliep. PeerJ Computer Science 2021, 7, e397. 10.7717/peerj-cs.397 Dataset

Deep Learning for Deep Waters: An Expert-in-the-loop Machine Learning Framework for Marine Sciences. I. Ryazanov, A. Trygvesdotter Nylund, D. Basu, I. Hassellöv and A. Schliep. Journal of Marine Science and Engineering 2021, 9:2, 169. 10.3390/jmse9020169

Fast parallel construction of variable-length Markov chains. J. Gustafsson, P. Norberg, J.R. Qvick-Wester and A. Schliep. BMC Bioinformatics 2021, 22:1, 1–23. 10.1186/s12859-021-04387-y PDF PubMed

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease. H.V. Dansson, L. Stempfle, H. Egilsdóttir, A. Schliep, E. Portelius, K. Blennow, H. Zetterberg, F.D. Johannson and A. Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimer’s Research & Therapy 2021, 13:1, 151. To appear.. 10.1186/s13195-021-00886-5 PDF PubMed

Reinforcement Learning as an Alternative to Reachability Analysis for Falsification of AD Functions. T. Johansson, A.M. Acosta, A. Schliep and P. Falcone. In Dec 2021. To appear at ML4AD (NeurIPS workshop on Autonomous Driving).

Computational analysis and data mining of drug-induced transcriptional changes. K. Ferenc. Master's Thesis, Gothenburg, Jun 2021.

2020

Blood glucose prediction with variance estimation using recurrent neural networks. J. Martinsson, A. Schliep, B. Eliasson and O. Mogren. Journal of Healthcare Informatics Research 2020, 4, 1–18. 10.1007/s41666-019-00059-y

Parallel construction of variable length Markov models for DNA sequences. J. Qvick. Master's Thesis, Chalmers University of Technology, Feb 2020. PDF

Prediction of Liver Toxicity using Machine Learning to aid Drug Discovery. D. Brunnsåker. Master's Thesis, Chalmers University of Technology, Feb 2020. PDF

AI-assisted synthesis prediction. S. Johansson, A. Thakkar, T. Kogej, E. Bjerrum, S. Genheden, T. Bastys, C. Kannas, C. Schliep, H. Chen and O. Engkvist. Drug Discovery Today: Technologies 2020. 10.1016/j.ddtec.2020.06.002

Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment. N. Judd, B. Sauce, J. Wiedenhoeft, J. Tromp, B. Chaarani, A. Schliep, B. van Noort, J. Penttilä, Y. Grimmer, C. Insensee, A. Becker, T. Banaschewski, A.L.W. Bokde, E.B. Quinlan, S. Desrivières, H. Flor, A. Grigis, P. Gowland, A. Heinz, B. Ittermann, J. Martinot, M. Paillère Martinot, E. Artiges, F. Nees, D. Papadopoulos Orfanos, T. Paus, L. Poustka, S. Hohmann, S. Millenet, J.H. Fröhner, M.N. Smolka, H. Walter, R. Whelan, G. Schumann, H. Garavan and T. Klingberg. Proceedings of the National Academy of Sciences 2020, 117:22, 12411–12418. 10.1073/pnas.2001228117 PDF

The Global Museum: natural history collections and the future of evolutionary science and public education. F.T. Bakker, A. Antonelli, J.A. Clarke, J.A. Cook, S.V. Edwards, P.G. Ericson, S. Faurby, N. Ferrand, M. Gelang, R.G. Gillespie, M. Irestedt, K. Lundin, E. Larsson, P. Matos-Maraví, J. Müller, T. von Proschwitz, G.K. Roderick, A. Schliep, N. Wahlberg, J. Wiedenhoeft and M. Källersjö. PeerJ 2020, 8:e8225. 10.7717/peerj.8225 PDF PubMed

Machine Learning for Predicting Progression of Alzheimer’s Disease. H. Egilsdottir and H. Valur. Master's Thesis, Chalmers, Jun 2020. PDF

Machine learning for big sequence data: Wavelet-compressed Hidden Markov Models. L. Bello. Master's Thesis, Chalmers, Jun 2020. PDF

AI graders for Python programming tasks. T. Ariuntuya and S. Holgersson. Master's Thesis, Chalmers, Jun 2020.

Compressed Machine Learning on Time Series Data. N. Gocht and F. Finger. Master's Thesis, University of Gothenburg, Jul 2020. PDF

2019

Parallel Computing of Support Vector Machines: A Survey. S. Tavara. ACM Computing Surveys 2019, 51:6, 123. 10.1145/3280989 Supplement

High-Performance Computing For Support Vector Machines. S. Tavara. University of Skövde, Feb 2019. (Licentiate Thesis). PDF

Bayesian optimization in ab initio nuclear physics. A. Ekström, C. Forssén, C. Dimitrakakis, D. Dubhashi, H. Johansson, A. Muhammad, H. Salomonsson and A. Schliep. Journal of Physics G: Nuclear and Particle Physics 2019, 46:095101. 10.1088/1361-6471/ab2b14

Prototype-based compression of time series from telecommunication data. G. Alpsten and S. Sabi. Master's Thesis, Chalmers University of Technology, Jun 2019. PDF

A Modeling Approach for Bioinformatics Workflows: A Design Science Study. L. Heckmann Barbalho de Figueroa, R. Salman, J. Horkoff, S. Chauhan, M. Davila, F. Gomes de Oliveira Neto and A. Schliep. In IFIP Working Conference on The Practice of Enterprise Modeling, Springer, 167–183, Nov 2019. Proceedings of the Practice of Enterprise Modelling Conference (PoEM).

Using clickers to predict students final courses grades, an artificial intelligence approach. F. Delahunty and A. Schliep. Technical report, Jan 2019. Extended Abstract for Conference on Teaching and Learning (KUL2019). PDF

Bayesian localization of CNV candidates in WGS data within minutes. J. Wiedenhoeft, A. Cagan, R. Kozhemyakina, R. Gulevich and A. Schliep. Algorithms for Molecular Biology 2019, 14:20. 10.1186/s13015-019-0154-7 PDF PubMed

City Safety Event Classification using Machine Learning. N. Jurczyńska. Master's Thesis, University of Gothenburg, Jun 2019. PDF

Predicting Mechanisms of Toxicity for Drug Development. S.R. Stahlschmidt. Master's Thesis, University of Göteborg, Jun 2019. PDF

Machine Learning for Suicide Risk Assessment on Depressed Patients. A. Moulis. Master's Thesis, University of Göteborg, Jun 2019.

DNA Sequence Classification Using Variable Length Markov Models. S. Norlin. Master's Thesis, Chalmers University of Technology, Jun 2019.

Embracing heterogeneity: building the Tree of Life and the future of phylogenomics. G.A. Bravo, A. Antonelli, C.D. Bacon, K. Bartoszek, M.P.K. Blom, S. Huynh, G. Jones, L.L. Knowles, S. Lamichhaney, T. Marcussen, H. Morlon, L.K. Nakhleh, B. Oxelman, B. Pfeil, A. Schliep, N. Wahlberg, F.P. Werneck, J. Wiedenhoeft, S. Willows-Munro and S.V. Edwards. PeerJ 2019, 7:e6399. 10.7717/peerj.6399 PDF Supplement PubMed

Assessment of Machine Learning Approaches for the Construction of an In Silico Liver Toxicity Model for Drug Development. D. Brunnsåker. Master's Thesis, Chalmers, Jun 2019.

Parallel training of variable length Markov chain based-models for biomedical data. J. Qvick. Master's Thesis, Chalmers, jun 2019.

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, 10706, 525–538, Jan 2018. 10.1007/978-3-319-73117-9_37 PDF

Dynamically Compressed Bayesian Hidden Markov Models using Haar Wavelets. J. Wiedenhoeft. Ph.D. Thesis, Rutgers, The State University of New Jersey, Oct 2018. PDF

Automatic Blood Glucose Prediction with Confidence Using Recurrent Neural Networks. J. Martinsson, A. Schliep, B. Eliasson, C. Meijner, S. Persson and O. Mogren. In Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI), 64–68, Jul 2018. PDF

Clustering Vehicle Maneuver Trajectories Using Mixtures of Hidden Markov Models. J. Martinsson, N. Mohammadiha and A. Schliep. In 21st International Conference on Intelligent Transportation Systems {ITSC}, IEEE, 3698–3705, Nov 2018. PDF

Statistical Sensor Modelling for Autonomous Driving Using Autoregressive Input-Output HMMs. E. Listo Zec, N. Mohammadiha and A. Schliep. In 21st International Conference on Intelligent Transportation Systems {ITSC}, IEEE, 1331–1336, Nov 2018. 10.1109/ITSC.2018.8569592 PDF

Effect Of Network Topology On The Performance Of ADMM-based SVMs. S. Tavara and A. Schliep. In 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), IEEE, 388–393, Sep 2018. 10.1109/CAHPC.2018.8645857 PDF

Using HaMMLET for Bayesian Segmentation of WGS Read-Depth Data.. J. Wiedenhoeft and A. Schliep. Methods Mol Biol 2018, 1833, 83–93. 10.1007/978-1-4939-8666-8_6 PubMed

Clustering genomic signatures. J. Gustafsson and E. Norlander. Master's Thesis, Chalmers University of Technology, Jun 2018. PDF

Improved Pattern Generation for Bloom Filters with Bit Patterns. B. Hedström and I. Josefsson. Master's Thesis, Chalmers University of Technology, Jun 2018. PDF

2017

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. PDF

Bird Species Identification using Convolutional Neural Networks. J. Martinsson. Master's Thesis, Chalmers University of Technology, Jul 2017. PDF

Detecting Network Degradation Using Machine Learning. A. Gashi Rojas and N.O. Nordholm. Master's Thesis, Chalmers University of Technology, May 2017. PDF

Machine Learning for Personalized Diabetes Management using Continuous Sensing. S. Persson and C. Meijner. Master's Thesis, Jun 2017. PDF

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. 10.1007/978-3-319-31957-5 PDF Supplement

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. 10.1371/journal.pcbi.1004871 PDF Supplement Dataset 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. 10.1186/s12859-016-1036-3 PDF Dataset PubMed

2015

Automatic learning of pre-miRNAs from different species. I.O.N. Lopes, A. Schliep and A.P.L.F. Carvalho. Technical report, Jul 2015. arXiv:1508.00412. PDF

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression. J. Wiedenhoeft, E. Brugel and A. Schliep. biorXiv 2015. http://dx.doi.org/10.1101/023705 PDF

2014

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.

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. 10.1186/1471-2105-15-124 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. 10.1038/srep04780 PDF PubMed

Improving genome assembly by identifying reliable sequencing data. R.S. Roy. Ph.D. Thesis, Oct 2014. PDF

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

Turtle: Identifying frequent k-mers with cache-efficient algorithms. R.S. Roy, D. Bhattacharya and A. Schliep. Bioinformatics 2014, 14:30, 1950–7. 10.1093/bioinformatics/btu132 PDF PubMed

TreQ-CG: Clustering Accelerates High-Throughput Sequencing Read Mapping. M. Mahmud and A. Schliep. Technical report, 2014. PDF Supplement

2013

The discriminant power of RNA features for pre-miRNA recognition. I.O.N. Lopes, A. Schliep and A.P.L.F. Carvalho. Technical report, Oct 2013. arXiv:1312.5778. PDF

Turtle: Identifying frequent k-mers with cache-efficient algorithms.. R.S. Roy, D. Bhattacharya and A. Schliep. Technical report, May 2013. Arxiv. PDF

2012

CLEVER: Clique-Enumerating Variant Finder. T. Marshall, I.G. Costa, S. Canzar, M. Bauer, G. Klau, A. Schliep and A. Schönhuth. Bioinformatics 2012, 28:22, 2875–82.. 10.1093/bioinformatics/bts566 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. 10.1111/j.1749-6632.2012.06540.x PubMed

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

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. 10.1093/bioinformatics/bts380 PDF Supplement PubMed

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. 10.1089/cmb.2011.0263 PDF PubMed

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. 10.1093/bioinformatics/btr037 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, 8:6, 1642–1652. 10.1109/TCBB.2011.39 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. 10.1186/1756-0381-4-9 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. 10.1093/bioinformatics/btr199 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. 10.1007/978-3-642-23038-7_17 PDF

Fast MCMC Sampling for Hidden Markov Models to Determine Copy Number Variations. M. Mahmud and A. Schliep. BMC Bioinformatics 2011, 12:1, 428. 10.1186/1471-2105-12-428 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. 10.1109/TCBB.2011.22 Supplement 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. 10.1371/currents.RRN1240 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. 10.1007/978-3-642-23038-7_17 PDF

Fast MCMC Sampling for Hidden Markov Models to Determine Copy Number Variations. M. Mahmud and A. Schliep. BMC Bioinformatics 2011, 12:1, 428. 10.1186/1471-2105-12-428 PDF PubMed

SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding. R.S. Roy, K. Chen, A. Sengupta and A. Schliep. Technical report, Nov 2011. Arxiv.

2010

PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data. B. Georgi, I.G. Costa and A. Schliep. BMC Bioinformatics 2010, 11:9. 10.1186/1471-2105-11-9 PDF PubMed

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

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. 10.1007/978-3-642-13078-6_21

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). 10.1093/bioinformatics/btp222 PDF Supplement PubMed

Context-specific Independence Mixture Models for Cluster Analysis of Biological Data. B. Georgi. Ph.D. Thesis, Freie Universität Berlin, Jun 2009. 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. 10.1186/1472-6807-9-68 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 . 10.1007/978-3-642-00668-5_16 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. 10.1109/IJCNN.2008.4634191 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. 10.1109/IJCNN.2008.4634333 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. 10.1109/TCBB.2008.50 PDF PubMed

Efficient Computation of Probe Qualities. C. Hafemeister. Master's Thesis, Freie Universität Berlin, May 2008. PDF

Inferring differentiation pathways from gene expression. I.G. Costa, S. Roepcke, C. Hafemeister and A. Schliep. Bioinformatics 2008, 24:13, i156–164. PDF Supplement 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. PDF

08301 Final Report -- Group Testing in the Life Sciences. A. Schliep, A. Shokrollahi and N. Thierry-Mieg. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany, 2008.

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. 10.1186/1471-2105-9-497 PDF Supplement Dataset PubMed

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

2007

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

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. 10.1007/978-3-540-73731-5_8 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. 10.1186/1471-2105-8-S10-S3 PDF Supplement PubMed

Gene expression trees in lymphoid development. I.G. Costa, S. Roepcke and A. Schliep. BMC Immunol 2007, 8:1, 25. 10.1186/1471-2172-8-25 PDF Supplement 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. 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. 10.1371/journal.pbio.0050321 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. 10.1007/978-3-540-74976-9_11 PDF Supplement

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. PDF Supplement

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. 10.1007/978-3-540-74126-8_36 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. http://dx.doi.org/10.1016/j.dam.2005.09.021 PDF

Identifying protein complexes directly from high-throughput TAP data with Markov random fields. W. Rungsarityotin, R. Krause, A. Schödl and A. Schliep. BMC Bioinformatics 2007, 8, 482. PDF Supplement PubMed

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

A Lattice Model of Basic Diatonic Progressions. J. Wiedenhoeft. In First International Conference on Mathematics and Computation in Music (Summaries), Society for Mathematics and Computation in Music, Staatliches Institut für Musikforschung Berlin, 376–381, 2007.

Proceedings of the German Conference on Bioinformatics, GCB 2007, September 26-28, 2007, Potsdam, Germany. Falter, Claudia and Schliep, Alexander and Selbig, Joachim and Vingron, Martin and Walther, Dirk (Ed.). GI, 2007.

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. PDF

Lernen von CSI Mixturen mit MCMC Methoden. M. Turewicz. Master's Thesis, Martin-Luther Universität Halle-Wittenberg, 2007.

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. PDF

Context-specific independence mixture modeling for positional weight matrices. B. Georgi and A. Schliep. Bioinformatics 2006, 22:14, e166–e173. 10.1093/bioinformatics/btl249 PDF PubMed

Structure Learning of Conditional Trees. C. Hafemeister. Bachelor's Thesis, Freie Universität Berlin, Jun 2006. PDF

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

Decoding non-unique oligonucleotide hybridization experiments of targets related by a phylogenetic tree. A. Schliep and S. Rahmann. Bioinformatics 2006, 22:14, e424–e430. 10.1093/bioinformatics/btl254 PDF PubMed

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

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. 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. 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. 10.1093/bioinformatics/bti311 PDF

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

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

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. 10.1109/TCBB.2005.31 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. PDF

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

2004

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

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

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. 10.1093/bioinformatics/bth936 PDF PubMed

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

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

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. 10.1093/bioinformatics/bth937 PDF Supplement 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. PDF

2003

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

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

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. 10.1093/bioinformatics/btg1036 PDF Supplement 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. 10.1109/CSB.2003.1227307 PDF PubMed

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

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. 10.1007/978-3-642-18991-3_64 PDF

2002

Selecting signature oligonucleotides to identify organisms using DNA arrays. L. Kaderali and A. Schliep. Bioinformatics 2002, 18:10, 1340–1349. 10.1093/bioinformatics/18.10.1340 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. 10.1093/bioinformatics/18.suppl_2.s182 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. 10.1007/978-3-642-56240-2_18 PDF

2001

Clustering protein sequences-structure prediction by transitive homology. E. Bolten, A. Schliep, S. Schneckener, D. Schomburg and R. Schrader. Bioinformatics 2001, 17:10, 935–41. 10.1093/bioinformatics/17.10.935 PDF PubMed

Selecting target specific probes for DNA arrays. L. Kaderali. Master's Thesis, University of Cologne, 2001. 10.1093/bioinformatics/18.10.1340

Evaluation and extension of a graph based clustering approach for the detection of remote homologs. P. Pipenbacher. Master's Thesis, University of Cologne, 2001.

A new Algorithm for Accelerating Pair-Wise Computations of Melting Temperature. L. Kaderali and A. Schliep. In Electron. Notes Discret. Math., 8, 46–49, 2001. Extended Abstract, 1st Cologne-Twente Workshop on Graphs and Combinatorial Optimization. 10.1016/S1571-0653(05)80076-9

Strongly Connected Components can Predict Protein Structure. E. Bolten, A. Schliep, S. Schneckener, D. Schomburg and R. Schrader. In Electron. Notes Discret. Math., 8, 10–13, 2001. Extended Abstract, 1st Cologne-Twente Workshop on Graphs and Combinatorial Optimization. 10.1016/S1571-0653(05)80066-6