Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

By Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme J. Mitchison. 1998

Subfields: Probabilistic & Bayesian Methods, Computational Biology & Bioinformatics

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Citations in the corpus

On Structured Prediction Theory with Calibrated Convex Surrogate Losses (2017, neutral): “Tasks of this type appear in many practical domains such as computer vision (Nowozin & Lampert, 2011), natural language processing (Smith, 2011) and bioinformatics (Durbin et al., 1998).”
Generating functional protein variants with variational autoencoders (2020, methodology): “Patterns of amino acid conservation at individual positions reflect the presence of functionally important sites and are used by profile HMMs to identify family members [48], while correlations in amino acid usage Fig 2.”
Evaluating Protein Transfer Learning with TAPE (2019, methodology): “The key technique for modeling sequence relationships in computational biology is alignment [13, 16, 21, 22].”
Accelerated Profile HMM Searches (2011, methodology): “Another approach is rescaling [5,38].”
Advances in Large Margin Classifiers (2000, background): “Durbin et al. [1998] provide an excellent tutorial introduction and review of HMMs, PHMMs, and their use in biological sequence analysis.”
Rational Kernels: Theory and Algorithms (2004, unknown): “The framework includes in particular pair-HMM string kernels (Durbin et al., 1998; Watkins, 1999), Haussler’s convolution kernels for strings, the path kernels of Takimoto and Warmuth (2003), and other classes of string kernels introduced for computational biology.”
Setting Cost in Optimal Matching to Uncover Contemporaneous Socio-Temporal Patterns (2010, background): “…is a computational reproduction of biological experimentations but it is precisely the opposite, a way to solve the question of the identification of the structure and/or functions of DNA or proteins without what is considered as the most reliable way to do so, experimentation (Durbin et al. 1998).”
Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction (2006, methodology): “In such cases, this problem was addressed by turning off the more sophisticated maximum a posteriori method for match state generation [ 53 ] and using the simple 50% column representation option to determine model architecture in hmmbuild.”
Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities (2005, unknown): “We are not aware of any MSA tools and phylogeny programs that are able to cope with short partial gene fragments, any two of which may fail to have significant overlap.”
A comprehensive high‐throughput analysis of substorms observed by IMAGE magnetometer network: Years 1993–2003 examined (2009, methodology): “patterns in biology [Durbin et al., 1998] and auroral forms from all-sky camera images in space physics [Syrjäsuo and Donovan, 2004].”
BACTIBASE second release: a database and tool platform for bacteriocin characterization (2010, background): “[16] in order to allow users to identify the bacterial family that produces the bacteriocins most similar to their sequences.”
A new generation of homology search tools based on probabilistic inference. (2009, background): “to express a probabilistic model of local sequence alignment [6, 9].”
Adaptive computation and machine learning (1998, background): “Such durations can also be modeled or adjusted using empirical distributions extracted from the available data, or by fitting theoretical distributions to the training data (see also [154]).”
Bayesian reasoning and machine learning (2012, unknown): “Other applications involve gene finding and protein family modelling[163, 84].”
A Dynamic Programming Algorithm for the Fused Lasso and L0-Segmentation (2013, methodology): “This viewpoint is not necessary to derive our algorithm, but we ﬁnd it useful since dynamic programming algorithms for HMMs are well known—excellent descriptions of the Viterbi algorithm are available in the literature by Rabiner (1989) and Durbin et al. (1999).”