Miklós Csürös

33 papers A* 2B 4C 4Misc 2Journal 14Unranked 4
YearRankTypeTitle / Venue / Authors
2021 J jnl
Gain-loss-duplication models on a phylogeny: exact algorithms for computing the likelihood and its gradient.
CoRR
Miklós Csürös
2016 — ch.
Distance-Based Phylogeny Reconstruction (Fast-Converging).
Encyclopedia of Algorithms
Miklós Csürös
2013 — ch.
How to Infer Ancestral Genome Features by Parsimony: Dynamic Programming over an Evolutionary Tree.
Models and Algorithms for Genome Evolution
Miklós Csürös
2013 J jnl
Joint genotype inference with germline and somatic mutations.
BMC Bioinform.
Eric Bareke, Virginie Saillour, Jean-François Spinella, Ramon Vidal, Jasmine Healy, Daniel Sinnett, Miklós Csürös
2012 — conf
A novel mathematical basis for predicting somatic single nucleotide variants from next-generation sequencing.
BCB
Eric Bareke, Jean-François Spinella, Ramon Vidal, Jasmine Healy, Daniel Sinnett, Miklós Csürös
2011 J jnl
A Detailed History of Intron-rich Eukaryotic Ancestors Inferred from a Global Survey of 100 Complete Genomes.
PLoS Comput. Biol.
Miklós Csürös, Igor B. Rogozin, Eugene V. Koonin
2010 Misc conf
Approximate Counting with a Floating-Point Counter.
COCOON
Miklós Csürös
2010 C conf
Fast Mapping and Precise Alignment of AB SOLiD Color Reads to Reference DNA.
WABI
Miklós Csürös, Szilveszter Juhos, Attila Bérces
2009 J jnl
Approximate counting with a floating-point counter
CoRR
Miklós Csürös
2008 — conf
Ancestral Reconstruction by Asymmetric Wagner Parsimony over Continuous Characters and Squared Parsimony over Distributions.
RECOMB-CG
Miklós Csürös
2008 — ch.
Distance-Based Phylogeny Reconstruction (Fast-Converging).
Encyclopedia of Algorithms
Miklós Csürös
2008 J jnl
Malin: maximum likelihood analysis of intron evolution in eukaryotes.
Bioinform.
Miklós Csürös
2007 — conf
In search of lost introns.
ISMB/ECCB (Supplement of Bioinformatics)
Miklós Csürös, J. Andrew Holey, Igor B. Rogozin
2007 J jnl
Rapid Homology Search with Neighbor Seeds.
Algorithmica
Miklós Csürös, Bin Ma
2006 B conf
A Probabilistic Model for Gene Content Evolution with Duplication, Loss, and Horizontal Transfer.
RECOMB
Miklós Csürös, István Miklós
2006 J jnl
On the Estimation of Intron Evolution.
PLoS Comput. Biol.
Miklós Csürös
2006 C conf
Segmentation with an Isochore Distribution.
WABI
Miklós Csürös, Ming-Te Cheng, Andreas Grimm, Amine Halawani, Perrine Landreau
2005 J jnl
A linear-time algorithm for finding the longest segment which scores above a given threshold
CoRR
Miklós Csürös
2005 — conf
Likely Scenarios of Intron Evolution.
Comparative Genomics
Miklós Csürös
2005 Misc conf
Rapid Homology Search with Two-Stage Extension and Daughter Seeds.
COCOON
Miklós Csürös, Bin Ma
2005 J jnl
Single-user tracing and disjointly superimposed codes.
IEEE Trans. Inf. Theory
Miklós Csürös, Miklós Ruszinkó
2004 C conf
Algorithms for Finding Maximal-Scoring Segment Sets (Extended Abstract).
WABI
Miklós Csürös
2004 J jnl
Maximum-Scoring Segment Sets.
IEEE ACM Trans. Comput. Biol. Bioinform.
Miklós Csürös
2004 B conf
Performing Local Similarity Searches with Variable Length Seeds.
CPM
Miklós Csürös
2004 J jnl
Pooled Genomic Indexing (PGI): Analysis and Design of Experiments.
J. Comput. Biol.
Miklós Csürös, Aleksandar Milosavljevic
2004 B conf
Single user tracing superimposed codes.
ISIT
Miklós Csürös, Miklós Ruszinkó
2002 J jnl
Fast Recovery of Evolutionary Trees with Thousands of Nodes.
J. Comput. Biol.
Miklós Csürös
2002 C conf
Pooled Genomic Indexing (PGI): Mathematical Analysis and Experiment Design.
WABI
Miklós Csürös, Aleksandar Milosavljevic
2001 B conf
Fast recovery of evolutionary trees with thousands of nodes.
RECOMB
Miklós Csürös
2001 J jnl
Provably Fast and Accurate Recovery of Evolutionary Trees through Harmonic Greedy Triplets.
SIAM J. Comput.
Miklós Csürös, Ming-Yang Kao
2000 J jnl
Provably Fast and Accurate Recovery of Evolutionary Trees through Harmonic Greedy Triplets
CoRR
Miklós Csürös, Ming-Yang Kao
1999 A* conf
Recovering Evolutionary Trees Through Harmonic Greedy Triplets.
SODA
Miklós Csürös, Ming-Yang Kao
1997 A* conf
Learning Markov Chains with Variable Memory Length from Noisy Output.
COLT
Dana Angluin, Miklós Csürös