Hidden Markov models in computational biology. Applications to protein modeling.

By Anders Krogh. 1993

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4. Sean R. Eddy
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Citations in the corpus

• Learning generative models for protein fold families (2011, background): “These statistical patterns are often learned from multiple sequence alignments (MSA) and then encoded using probabilistic graphical models (e.g., [1, 2, 3, 4, 5]).”
• Accelerated Profile HMM Searches (2011, methodology): “Since BLAST’s introduction, some important advances have been made in the theory of sequence comparison, particularly by using probabilistic inference methods based on profile hidden Markov models (profile HMMs) [4].”
• Biological Sequence Modeling with Convolutional Kernel Networks (2017, neutral): “Early methods for studying the genotype-phenotype relationship relied on pre-determined fingerprints [3] or hidden Markov models [19].”
• The Spectrum Kernel: A String Kernel for SVM Protein Classification (2001, methodology): “Many approaches have been presented for the protein classi(cid:12)cation problem, including methods based on pairwise similarity of sequences 1 ; 2 ; 3 , pro(cid:12)les for protein families 4 , consensus patterns using motifs 5 ; 6 and hidden Markov models 7 ; 8 ; 9 .”
• 20 years of the SMART protein domain annotation resource (2017, methodology): “The SMART database (12) integrates manually curated hidden Markov models (11,17) for many domains with a powerful web-based interface offering various analysis and visualization tools.”
• Hidden Markov Models and their Applications in Biological Sequence Analysis (2009, background): “One model that is especially useful for representing the profile of a multiple sequence alignment is the profile hidden Markov model (profile-HMM) [24, 25].”
• Toward the solution of the protein structure prediction problem (2021, methodology): “Pro fi le HMMs were introduced in structural bioinformatics in 1994 (90) and have remained one of the most effective methods for identifying templates and constructing MSAs (89, 91, 92).”
• The Dfam community resource of transposable element families, sequence models, and genome annotations (2020, background): “In addition, a unified eukaryotic TE classification scheme and HMMs for DNA transposon termini now on Dfam, provide additional details for researchers to utilize in their TE research.”
• The ASTRAL Compendium in 2004 (2003, methodology): “A hidden Markov model (7) (HMM) is created from the multiple sequence alignment for each superfamily using the HMMER (6) tools hmmbuild and hmmcalibrate (with all default options).”
• Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. (2001, methodology): “The default procedure follows these steps: (1) Using the initial sequence(s) and the WU-BLASTP procedure (http://blast.”
• An Introduction to Sequence Similarity (“Homology”) Searching (2009, background): “However, the time required to fill in the DP matrix is proportional to the product of the length of all the sequences, which becomes impractical for more than a few sequences (Lipman et al., 1989).”
• A comparison of profile hidden Markov model procedures for remote homology detection. (2002, methodology): “It has been shown by Park et al. (1) that profile-based methods, which consider profiles of protein families, perform much better than pairwise methods, which consider individual protein sequences, and that of the profilebased methods hidden Markov models (HMMs) ( 2 ,3) perform best.”
• A Database of Bacterial Lipoproteins (DOLOP) with Functional Assignments to Predicted Lipoproteins (2006, methodology): “The domains in the sequences are detected and classified according to the SCOP (38) classi-fication of domain superfamilies using HMMs (12, 32).”
• Hybrid Modeling, HMM/NN Architectures, and Protein Applications (1996, methodology): “One of the standard HMM architectures for protein applications (Krogh et al. 1994a), is the left-right architecture depicted in Figure 1.”
• An HMM model for coiled-coil domains and a comparison with PSSM-based predictions (2002, methodology): “HMMs have become a standard technique in sequence analysis (Baldi et al., 1994; Durbin et al., 1998; Eddy, 1995; Hughey and Krogh, 1996; Krogh et al., 1994).”