Matches in SemOpenAlex for { <https://semopenalex.org/work/W2952091268> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W2952091268 endingPage "2164" @default.
- W2952091268 startingPage "2162" @default.
- W2952091268 abstract "Abstract Motivation Hidden Markov models (HMMs) are powerful tools for modeling processes along the genome. In a standard genomic HMM, observations are drawn, at each genomic position, from a distribution whose parameters depend on a hidden state, and the hidden states evolve along the genome as a Markov chain. Often, the hidden state is the Cartesian product of multiple processes, each evolving independently along the genome. Inference in these so-called Factorial HMMs has a naïve running time that scales as the square of the number of possible states, which by itself increases exponentially with the number of sub-chains; such a running time scaling is impractical for many applications. While faster algorithms exist, there is no available implementation suitable for developing bioinformatics applications. Results We developed FactorialHMM, a Python package for fast exact inference in Factorial HMMs. Our package allows simulating either directly from the model or from the posterior distribution of states given the observations. Additionally, we allow the inference of all key quantities related to HMMs: (i) the (Viterbi) sequence of states with the highest posterior probability; (ii) the likelihood of the data and (iii) the posterior probability (given all observations) of the marginal and pairwise state probabilities. The running time and space requirement of all procedures is linearithmic in the number of possible states. Our package is highly modular, providing the user with maximal flexibility for developing downstream applications. Availability and implementation https://github.com/regevs/factorial_hmm Supplementary information Supplementary data are available at Bioinformatics online." @default.
- W2952091268 created "2019-06-27" @default.
- W2952091268 creator A5027186771 @default.
- W2952091268 creator A5049650069 @default.
- W2952091268 creator A5059774133 @default.
- W2952091268 date "2018-11-15" @default.
- W2952091268 modified "2023-09-23" @default.
- W2952091268 title "FactorialHMM: fast and exact inference in factorial hidden Markov models" @default.
- W2952091268 cites W1528056001 @default.
- W2952091268 cites W181601661 @default.
- W2952091268 cites W1975450451 @default.
- W2952091268 cites W1978122642 @default.
- W2952091268 cites W1985311670 @default.
- W2952091268 cites W1998158007 @default.
- W2952091268 cites W2016879826 @default.
- W2952091268 cites W2018363492 @default.
- W2952091268 cites W2035947278 @default.
- W2952091268 cites W2059319227 @default.
- W2952091268 cites W2069084739 @default.
- W2952091268 cites W2071496798 @default.
- W2952091268 cites W2087284982 @default.
- W2952091268 cites W2105306382 @default.
- W2952091268 cites W2114029728 @default.
- W2952091268 cites W2118379383 @default.
- W2952091268 cites W2125838338 @default.
- W2952091268 cites W2135881136 @default.
- W2952091268 cites W2145177374 @default.
- W2952091268 cites W2151909331 @default.
- W2952091268 cites W2166444569 @default.
- W2952091268 cites W2602431927 @default.
- W2952091268 cites W2963065819 @default.
- W2952091268 cites W4245668478 @default.
- W2952091268 doi "https://doi.org/10.1093/bioinformatics/bty944" @default.
- W2952091268 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30445428" @default.
- W2952091268 hasPublicationYear "2018" @default.
- W2952091268 type Work @default.
- W2952091268 sameAs 2952091268 @default.
- W2952091268 citedByCount "1" @default.
- W2952091268 countsByYear W29520912682022 @default.
- W2952091268 crossrefType "journal-article" @default.
- W2952091268 hasAuthorship W2952091268A5027186771 @default.
- W2952091268 hasAuthorship W2952091268A5049650069 @default.
- W2952091268 hasAuthorship W2952091268A5059774133 @default.
- W2952091268 hasBestOaLocation W29520912682 @default.
- W2952091268 hasConcept C101468663 @default.
- W2952091268 hasConcept C107673813 @default.
- W2952091268 hasConcept C111919701 @default.
- W2952091268 hasConcept C11413529 @default.
- W2952091268 hasConcept C119857082 @default.
- W2952091268 hasConcept C134306372 @default.
- W2952091268 hasConcept C153180895 @default.
- W2952091268 hasConcept C154945302 @default.
- W2952091268 hasConcept C183763347 @default.
- W2952091268 hasConcept C23224414 @default.
- W2952091268 hasConcept C2776214188 @default.
- W2952091268 hasConcept C33923547 @default.
- W2952091268 hasConcept C41008148 @default.
- W2952091268 hasConcept C57830394 @default.
- W2952091268 hasConcept C60582962 @default.
- W2952091268 hasConcept C98763669 @default.
- W2952091268 hasConceptScore W2952091268C101468663 @default.
- W2952091268 hasConceptScore W2952091268C107673813 @default.
- W2952091268 hasConceptScore W2952091268C111919701 @default.
- W2952091268 hasConceptScore W2952091268C11413529 @default.
- W2952091268 hasConceptScore W2952091268C119857082 @default.
- W2952091268 hasConceptScore W2952091268C134306372 @default.
- W2952091268 hasConceptScore W2952091268C153180895 @default.
- W2952091268 hasConceptScore W2952091268C154945302 @default.
- W2952091268 hasConceptScore W2952091268C183763347 @default.
- W2952091268 hasConceptScore W2952091268C23224414 @default.
- W2952091268 hasConceptScore W2952091268C2776214188 @default.
- W2952091268 hasConceptScore W2952091268C33923547 @default.
- W2952091268 hasConceptScore W2952091268C41008148 @default.
- W2952091268 hasConceptScore W2952091268C57830394 @default.
- W2952091268 hasConceptScore W2952091268C60582962 @default.
- W2952091268 hasConceptScore W2952091268C98763669 @default.
- W2952091268 hasFunder F4320322252 @default.
- W2952091268 hasIssue "12" @default.
- W2952091268 hasLocation W29520912681 @default.
- W2952091268 hasLocation W29520912682 @default.
- W2952091268 hasLocation W29520912683 @default.
- W2952091268 hasOpenAccess W2952091268 @default.
- W2952091268 hasPrimaryLocation W29520912681 @default.
- W2952091268 hasRelatedWork W1976776478 @default.
- W2952091268 hasRelatedWork W1989324306 @default.
- W2952091268 hasRelatedWork W2100982643 @default.
- W2952091268 hasRelatedWork W2111581792 @default.
- W2952091268 hasRelatedWork W2151409566 @default.
- W2952091268 hasRelatedWork W2401728283 @default.
- W2952091268 hasRelatedWork W2952091268 @default.
- W2952091268 hasRelatedWork W3098163168 @default.
- W2952091268 hasRelatedWork W3157920306 @default.
- W2952091268 hasRelatedWork W86309964 @default.
- W2952091268 hasVolume "35" @default.
- W2952091268 isParatext "false" @default.
- W2952091268 isRetracted "false" @default.
- W2952091268 magId "2952091268" @default.
- W2952091268 workType "article" @default.