Sepp Hochreiter

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Sepp Hochreiter (born 1967 in Mühldorf am Inn) is a computer scientist working in the fields of bioinformatics and machine learning. Since 2006 he has been head of the Institute of Bioinformatics[1] at the Johannes Kepler University of Linz. Before, he was at the Technical University of Berlin, at the University of Colorado at Boulder, and at the Technical University of Munich. At the Johannes Kepler University of Linz, he founded the Bachelors Program in Bioinformatics,[2] which is a cross-border, double-degree study program together with the University of South-Bohemia in České Budějovice (Budweis), Czech Republic. He also established the Masters Program in Bioinformatics[3] at the Johannes Kepler University of Linz, where he is still the acting dean of both studies. Sepp Hochreiter launched the Bioinformatics Working Group at the Austrian Computer Society, he is founding board member of different bioinformatics start-up companies, he was program chair of the conference Bioinformatics Research and Development,[4] he is conference chair of the conference Critical Assessment of Massive Data Analysis (CAMDA),[5] he is editor, program committee member, and reviewer for international journals and conferences.

Scientific Contributions[edit]


Sepp Hochreiter developed "HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data"[6] for detecting short segments of identity by descent. A DNA segment is identical by state (IBS) in two or more individuals if they have identical nucleotide sequences in this segment. An IBS segment is identical by descent (IBD) in two or more individuals if they have inherited it from a common ancestor, that is, the segment has the same ancestral origin in these individuals. HapFABIA identifies 100 times smaller IBD segments than current state-of-the-art methods: 10kbp for HapFABIA vs. 1Mbp for state-of-the-art methods. HapFABIA is tailored to next generation sequencing data and utilizes rare variants for IBD detection but also works for microarray genotyping data. HapFABIA allows to enhance evolutionary biology, population genetics, and association studies because it decomposed the genome into short IBD segments which describe the genome with very high resolution. HapFABIA was used to analyze the IBD sharing between Humans, Neandertals (Neanderthals), and Denisovans.[7][8]

Next-Generation Sequencing[edit]

Sepp Hochreiter's research group is member of the SEQC/MAQC-III consortium, coordinated by the US Food and Drug Administration. This consortium examined Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites regarding RNA sequencing (RNA-seq) performance.[9] Within this project standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments have been defined.[10] For analyzing the structural variation of the DNA, Sepp Hochreiter's research group proposed "cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation data with a low false discovery rate"[11] for detecting copy number variations in next generation sequencing data. cn.MOPS estimates the local DNA copy number, is suited for both whole genome sequencing and exom sequencing, and can be applied to diploid and haploid genomes but also to polyploid genomes. For identifying differential expressed transcripts in RNA-seq (RNA sequencing) data, Sepp Hochreiter's group suggested "DEXUS: Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions".[12] In contrast to other RNA-seq methods, DEXUS can detect differential expression in RNA-seq data for which the sample conditions are unknown and for which biological replicates are not available. In the group of Sepp Hochreiter, sequencing data was analyzed to gain insights into chromatin remodeling. The reorganization of the cell's chromatin structure was determined via next-generation sequencing of resting and activated T cells. The analyses of these T cell chromatin sequencing data identified GC-rich long nucleosome-free regions that are hot spots of chromatin remodeling.[13]

Microarray Preprocessing and Summarization[edit]

Sepp Hochreiter developed "Factor Analysis for Robust Microarray Summarization" (FARMS).[14] FARMS has been designed for preprocessing and summarizing high-density oligonucleotide DNA microarrays at probe level to analyze RNA gene expression. FARMS is based on a factor analysis model which is optimized in a Bayesian framework by maximizing the posterior probability. On Affymetrix spiked-in and other benchmark data, FARMS outperformed all other methods. A highly relevant feature of FARMS is its informative/ non-informative (I/NI) calls.[15] The I/NI call is a Bayesian filtering technique which separates signal variance from noise variance. The I/NI call offers a solution to the main problem of high dimensionality when analyzing microarray data by selecting genes which are measured with high quality.[16][17] FARMS has been extended to cn.FARMS[18] for detecting DNA structural variants like copy number variations with a low false discovery rate.


Sepp Hochreiter developed "Factor Analysis for Bicluster Acquisition" (FABIA)[19] for biclustering that is simultaneously clustering rows and columns of a matrix. A bicluster in transcriptomic data is a pair of a gene set and a sample set for which the genes are similar to each other on the samples and vice versa. In drug design, for example, the effects of compounds may be similar only on a subgroup of genes. FABIA is a multiplicative model that assumes realistic non-Gaussian signal distributions with heavy tails and utilizes well understood model selection techniques like a variational approach in the Bayesian framework. FABIA supplies the information content of each bicluster to separate spurious biclusters from true biclusters.

Support Vector Machines[edit]

Support vector machines (SVMs) are supervised learning methods used for classification and regression analysis by recognizing patterns and regularities in the data. Standard SVMs require a positive definite kernel to generate a squared kernel matrix from the data. Sepp Hochreiter proposed the "Potential Support Vector Machine" (PSVM),[20] which can be applied to non-square kernel matrices and can be used with kernels that are not positive definite. For PSVM model selection he developed an efficient sequential minimal optimization algorithm.[21] The PSVM minimizes a new objective which ensures theoretical bounds on the generalization error and automatically selects features which are used for classification or regression.

Feature Selection[edit]

Sepp Hochreiter applied the PSVM to feature selection, especially to gene selection for microarray data.[22][23][24] The PSVM and standard support vector machines were applied to extract features that are indicative coiled coil oligomerization.[25]

Deep Learning, Learning Representations and Low Complexity Neural Networks[edit]

Neural networks are different types of simplified mathematical models of biological neural networks like those in human brains. If data mining is based on neural networks, overfitting reduces the network's capability to correctly process future data. To avoid overfitting, Sepp Hochreiter developed algorithms for finding low complexity neural networks like "Flat Minimum Search" (FMS),[26] which searches for a "flat" minimum — a large connected region in the parameter space where the network function is constant. Thus, the network parameters can be given with low precision which means a low complex network that avoids overfitting. Low complexity neural networks are well suited for deep learning because they control the complexity in each network layer and, therefore, learn hierarchical representations of the input.

Deep Neural Networks, Recurrent Neural Networks and Long Short-Term Memory (LSTM)[edit]

Recurrent neural networks scan and process sequences and supply their results to the environment. Sepp Hochreiter developed the long short term memory,[27][28] which overcomes the problem of previous recurrent and deep networks to forget information over time or, equivalently, through layers (vanishing or exploding gradient).[29][30] LSTM learns from training sequences to solve numerous tasks like automatic music composition, speech recognition, reinforcement learning, and robotics. LSTM with an optimized architecture was successfully applied to very fast protein homology detection without requiring a sequence alignment.[31] LSTM has been used to learn a learning algorithm, that is, LSTM substitutes a Turing machine or a computer on which a learning algorithm is executed. Since the learning machine is a neural network, it can be improved and novel learning algorithms be developed. It turns out that the learned new learning techniques are superior to those designed by humans.[32][33]


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  6. ^ Hochreiter, S. (2013). "HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data". Nucleic Acids Research 41 (22): e202. doi:10.1093/nar/gkt1013. PMC 3905877. PMID 24174545.  edit
  7. ^ Povysil, G.; Hochreiter, S. (2014). "Sharing of Very Short IBD Segments between Humans, Neandertals, and Denisovans". doi:10.1101/003988.  edit
  8. ^ Research Report
  9. ^ "A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium". Nature Biotechnology 32 (9): 903–914. September 2014. doi:10.1038/nbt.2957. PMID 25150838.  edit
  10. ^ Sarah A. Munro, Steven P. Lund, P. Scott Pine, Hans Binder, Djork-Arne Clevert, Ana Conesa, Joaquin Dopazo, Mario Fasold, Sepp Hochreiter, Huixiao Hong, Nadereh Jafari, David P. Kreil, Pawel P. Labaj, Sheng Li, Yang Liao, Simon M. Lin, Joseph Meehan, Christopher E. Mason, Javier Santoyo-Lopez, Robert A. Setterquist, Leming Shi, Wei Shi, Gordon K. Smyth, Nancy Stralis-Pavese, Zhenqiang Su, Weida Tong, Charles Wang, Jian Wang, Joshua Xu, Zhan Ye, Yong Yang, Ying Yu & Marc Salit (2014). "Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures". Nature Communications 5: 5125. doi:10.1038/ncomms6125. PMID 25254650.  edit
  11. ^ Klambauer, G.; Schwarzbauer, K.; Mayr, A.; Clevert, D. A.; Mitterecker, A.; Bodenhofer, U.; Hochreiter, S. (2012). "Cn.MOPS: Mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate". Nucleic acids research 40 (9): e69. doi:10.1093/nar/gks003. PMC 3351174. PMID 22302147.  edit
  12. ^ Klambauer, G.; Unterthiner, T.; Hochreiter, S. (2013). "DEXUS: Identifying differential expression in RNA-Seq studies with unknown conditions". Nucleic Acids Research 41 (21): e198. doi:10.1093/nar/gkt834. PMID 24049071.  edit
  13. ^ Schwarzbauer, K.; Bodenhofer, U.; Hochreiter, S. (2012). Campbell, Moray, ed. "Genome-wide chromatin remodeling identified at GC-rich long nucleosome-free regions". PloS one 7 (11): e47924. doi:10.1371/journal.pone.0047924. PMC 3489898. PMID 23144837.  edit
  14. ^ Hochreiter, S.; Clevert, D. -A.; Obermayer, K. (2006). "A new summarization method for affymetrix probe level data". Bioinformatics 22 (8): 943–949. doi:10.1093/bioinformatics/btl033. PMID 16473874.  edit
  15. ^ Talloen, W.; Clevert, D. -A.; Hochreiter, S.; Amaratunga, D.; Bijnens, L.; Kass, S.; Gohlmann, H. W. H. (2007). "I/NI-calls for the exclusion of non-informative genes: A highly effective filtering tool for microarray data". Bioinformatics 23 (21): 2897–2902. doi:10.1093/bioinformatics/btm478. PMID 17921172.  edit
  16. ^ Talloen, W.; Hochreiter, S.; Bijnens, L.; Kasim, A.; Shkedy, Z.; Amaratunga, D.; Gohlmann, H. (2010). "Filtering data from high-throughput experiments based on measurement reliability". Proceedings of the National Academy of Sciences 107 (46): E173–E174. doi:10.1073/pnas.1010604107. PMC 2993399. PMID 21059952.  edit
  17. ^ Kasim, A.; Lin, D.; Van Sanden, S.; Clevert, D. A.; Bijnens, L.; Göhlmann, H.; Amaratunga, D.; Hochreiter, S.; Shkedy, Z.; Talloen, W. (2010). "Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach". Statistical Applications in Genetics and Molecular Biology 9. doi:10.2202/1544-6115.1460.  edit
  18. ^ Clevert, D. -A.; Mitterecker, A.; Mayr, A.; Klambauer, G.; Tuefferd, M.; De Bondt, A. D.; Talloen, W.; Göhlmann, H.; Hochreiter, S. (2011). "Cn.FARMS: A latent variable model to detect copy number variations in microarray data with a low false discovery rate". Nucleic Acids Research 39 (12): e79. doi:10.1093/nar/gkr197. PMC 3130288. PMID 21486749.  edit
  19. ^ Hochreiter, S.; Bodenhofer, U.; Heusel, M.; Mayr, A.; Mitterecker, A.; Kasim, A.; Khamiakova, T.; Van Sanden, S.; Lin, D.; Talloen, W.; Bijnens, L.; Göhlmann, H. W. H.; Shkedy, Z.; Clevert, D. -A. (2010). "FABIA: Factor analysis for bicluster acquisition". Bioinformatics 26 (12): 1520–1527. doi:10.1093/bioinformatics/btq227. PMC 2881408. PMID 20418340.  edit
  20. ^ Hochreiter, S.; Obermayer, K. (2006). "Support Vector Machines for Dyadic Data". Neural Computation 18 (6): 1472–1510. doi:10.1162/neco.2006.18.6.1472. PMID 16764511.  edit
  21. ^ Knebel, T.; Hochreiter, S.; Obermayer, K. (2008). "An SMO Algorithm for the Potential Support Vector Machine". Neural Computation 20 (1): 271–287. doi:10.1162/neco.2008.20.1.271. PMID 18045009.  edit
  22. ^ Hochreiter, S.; Obermayer, K. (2006). "Nonlinear Feature Selection with the Potential Support Vector Machine". Feature Extraction, Studies in Fuzziness and Soft Computing 207. pp. 419–438. doi:10.1007/978-3-540-35488-8_20. ISBN 978-3-540-35487-1.  edit
  23. ^ Hochreiter, S.; Obermayer, K. (2003). "Classification and Feature Selection on Matrix Data with Application to Gene-Expression Analysis". 54th Session of the International Statistical Institute. 
  24. ^ Hochreiter, S.; Obermayer, K. (2004). "Gene Selection for Microarray Data". Kernel Methods in Computational Biology (MIT Press): 319–355. 
  25. ^ Mahrenholz, C. C.; Abfalter, I. G.; Bodenhofer, U.; Volkmer, R.; Hochreiter, S. (2011). "Complex Networks Govern Coiled-Coil Oligomerization - Predicting and Profiling by Means of a Machine Learning Approach". Molecular & Cellular Proteomics 10 (5): M110.004994–M110.004994. doi:10.1074/mcp.M110.004994. PMC 3098589. PMID 21311038.  edit
  26. ^ Hochreiter, S.; Schmidhuber, J. R. (1997). "Flat Minima". Neural Computation 9 (1): 1–42. doi:10.1162/neco.1997.9.1.1. PMID 9117894.  edit
  27. ^ Hochreiter, Sepp (1991). Untersuchungen zu dynamischen neuronalen Netzen (diploma thesis). Technical University Munich, Institute of Computer Science. 
  28. ^ Hochreiter, S.; Schmidhuber, J. R. (1997). "Long Short-Term Memory". Neural Computation 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735. PMID 9377276.  edit
  29. ^ Hochreiter, Sepp (1998). "The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06 (02): 107–116. doi:10.1142/S0218488598000094. ISSN 0218-4885. 
  30. ^ Hochreiter, Sepp; Bengio, Yoshua; Frasconi, Paolo; Schmidhuber, Jürgen (2000). Kolen, John F.; Kremer, Stefan C., eds. "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies". A Field Guide to Dynamical Recurrent Networks. New York City: IEEE Press. pp. 237–244. 
  31. ^ Hochreiter, S.; Heusel, M.; Obermayer, K. (2007). "Fast model-based protein homology detection without alignment". Bioinformatics 23 (14): 1728–1736. doi:10.1093/bioinformatics/btm247. PMID 17488755.  edit
  32. ^ Hochreiter, Sepp; Younger, A. Steven; Conwell, Peter R. (2001). "Learning to Learn Using Gradient Descent". Lecture Notes in Computer Science - ICANN 2001 2130: 87–94. doi:10.1007/3-540-44668-0_13. ISSN 0302-9743.  edit
  33. ^ Learning to Learn Using Gradient Descent]

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