Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training.

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A critical assessment of hidden markov model sub-optimal sampling strategies applied to the generation of peptide 3D models.

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Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training.

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2010 nî lūn-bûn

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2010年の論文

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2010年論文

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2010年論文

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Efficient algorithms for train ...... training and Viterbi training.

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Efficient algorithms for train ...... astic expectation maximization

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Efficient algorithms for train ...... training and Viterbi training.

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Efficient algorithms for train ...... astic expectation maximization

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Efficient algorithms for train ...... training and Viterbi training.

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Efficient algorithms for train ...... astic expectation maximization

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P1476

Efficient algorithms for train ...... training and Viterbi training.

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P2093

Irmtraud M Meyer

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Tin Y Lam

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P2860

The Pfam protein families database

EasyGene--a prokaryotic gene finder that ranks ORFs by statistical significance

A linear memory algorithm for Baum-Welch training

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes

GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions

Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts

OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif.

AUGUSTUS: ab initio prediction of alternative transcripts.

Querying pathways in protein interaction networks based on hidden Markov models.

Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training

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1748-7188-5-38

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scholarly article

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10.1186/1748-7188-5-38

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2010-12-09T00:00:00Z

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49669941

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1050105287

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0909.0737

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3019189

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Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training.

about

P2860

P2860

Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training.

description

name

type

label

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P1433

P1476

P2093

P2860

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P356

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P577

P5875

P6179

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P818

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P356

P1433

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