Hierarchical Bayesian Modeling of Pharmacophores in Bioinformatics

Mardia, Kanti V.; Nyirongo, Vysaul; Fallaize, Christopher J.; Barber, Stuart; Jackson, Richard M.

doi:10.1111/j.1541-0420.2010.01460.x

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Green and Mardia (2006) and Mardia et al. (2011) have examined the sensitivity issues of the ALIBI method at great length.…”

Section: Structure Comparison and Alignmentmentioning

confidence: 99%

“…Various possible avenues and extensions are also described there, including extension of non‐isotropic labelled shape analysis such as that of Theobald and Wuttke (2006). Expert knowledge could help in selecting a Bayesian solution as Bayesian methods allow in a natural way any prior physicochemical information such as amino acid types (Green and Mardia, 2006) or atom types (Mardia et al. , 2011).…”

Section: Structure Comparison and Alignmentmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Approaches to Three Key Challenges in Protein Structural Bioinformatics

Mardia

2013

Journal of the Royal Statistical Society Series C: Applied Statistics

Self Cite

View full text Add to dashboard Cite

Proteins are the workhorses of all living systems, and protein bioinformatics deals with analysis of protein sequences (one dimensional) and structures (three dimensional). The paper reviews statistical advances in three major active areas of protein structural bioinformatics: structure comparison, Ramachandran plots and structure prediction.These topics play a key role in understanding one of the greatest unsolved problems in biology, how proteins fold from one dimension to three dimensions, and have relevance to protein functionality, drug discovery and evolutionary biology. For each area, we give the biological background and review one of the main bioinformatics solutions to a specific problem in that area. We then present statistical tools recently developed to investigate these problems, consisting of Bayesian alignment, directional distributions and hidden Markov models. We illustrate each problem with a new case-study and describe what statistics can offer to these problems. We highlight challenges facing these areas and conclude with an overall discussion.

show abstract

“…Green and Mardia (2006) and Mardia et al. (2011) have examined the sensitivity issues of the ALIBI method at great length.…”

Section: Structure Comparison and Alignmentmentioning

confidence: 99%

Section: Structure Comparison and Alignmentmentioning

confidence: 99%

Statistical Approaches to Three Key Challenges in Protein Structural Bioinformatics

Mardia

2013

Journal of the Royal Statistical Society Series C: Applied Statistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ruffieux and Green (2009) generalized the method of Green and Mardia (2006) to develop a fully Bayesian model for the alignment of multiple configurations under rigid body transformations; a natural extension might therefore be to incorporate our methodology developed in this paper within their model. Mardia et al (2011) addressed the problem of multiple alignment under rigid body transformations by embedding a pairwise alignment method within a multi-stage algorithm, and their methodology could easily be adapted to incorporate the extension to the full similarity shape case introduced here.…”

Section: Illustrative Examplesmentioning

confidence: 99%

Bayesian alignment of similarity shapes

Mardia

Fallaize²,

Barber³

et al. 2013

Ann. Appl. Stat.

Self Cite

View full text Add to dashboard Cite

We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area has concentrated on rigid body transformations, where scale information is preserved, motivated by problems involving molecular data; this is known as form analysis. We concentrate on a Bayesian formulation for statistical shape analysis. We generalize the model introduced by Green and Mardia for the pairwise alignment of two unlabeled configurations to full similarity transformations by introducing a scaling factor to the model. The generalization is not straight-forward, since the model needs to be reformulated to give good performance when scaling is included. We illustrate our method on the alignment of rat growth profiles and a novel application to the alignment of protein domains. Here, scaling is applied to secondary structure elements when comparing protein folds; additionally, we find that one global scaling factor is not in general sufficient to model these data and, hence, we develop a model in which multiple scale factors can be included to handle different scalings of shape components.

show abstract

A Conversation with Kanti Mardia: Part II

Mukhopadhyay

2015

Geometry Driven Statistics

View full text Add to dashboard Cite

Hierarchical Bayesian Modeling of Pharmacophores in Bioinformatics

Cited by 5 publications

References 12 publications

Statistical Approaches to Three Key Challenges in Protein Structural Bioinformatics

Statistical Approaches to Three Key Challenges in Protein Structural Bioinformatics

Bayesian alignment of similarity shapes

A Conversation with Kanti Mardia: Part II

Contact Info

Product

Resources

About