5,994 research outputs found
Analysis of Three-Dimensional Protein Images
A fundamental goal of research in molecular biology is to understand protein
structure. Protein crystallography is currently the most successful method for
determining the three-dimensional (3D) conformation of a protein, yet it
remains labor intensive and relies on an expert's ability to derive and
evaluate a protein scene model. In this paper, the problem of protein structure
determination is formulated as an exercise in scene analysis. A computational
methodology is presented in which a 3D image of a protein is segmented into a
graph of critical points. Bayesian and certainty factor approaches are
described and used to analyze critical point graphs and identify meaningful
substructures, such as alpha-helices and beta-sheets. Results of applying the
methodologies to protein images at low and medium resolution are reported. The
research is related to approaches to representation, segmentation and
classification in vision, as well as to top-down approaches to protein
structure prediction.Comment: See http://www.jair.org/ for any accompanying file
Nonlinear Models Using Dirichlet Process Mixtures
We introduce a new nonlinear model for classification, in which we model the
joint distribution of response variable, y, and covariates, x,
non-parametrically using Dirichlet process mixtures. We keep the relationship
between y and x linear within each component of the mixture. The overall
relationship becomes nonlinear if the mixture contains more than one component.
We use simulated data to compare the performance of this new approach to a
simple multinomial logit (MNL) model, an MNL model with quadratic terms, and a
decision tree model. We also evaluate our approach on a protein fold
classification problem, and find that our model provides substantial
improvement over previous methods, which were based on Neural Networks (NN) and
Support Vector Machines (SVM). Folding classes of protein have a hierarchical
structure. We extend our method to classification problems where a class
hierarchy is available. We find that using the prior information regarding the
hierarchical structure of protein folds can result in higher predictive
accuracy
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Predicting protein properties such as solvent accessibility and secondary
structure from its primary amino acid sequence is an important task in
bioinformatics. Recently, a few deep learning models have surpassed the
traditional window based multilayer perceptron. Taking inspiration from the
image classification domain we propose a deep convolutional neural network
architecture, MUST-CNN, to predict protein properties. This architecture uses a
novel multilayer shift-and-stitch (MUST) technique to generate fully dense
per-position predictions on protein sequences. Our model is significantly
simpler than the state-of-the-art, yet achieves better results. By combining
MUST and the efficient convolution operation, we can consider far more
parameters while retaining very fast prediction speeds. We beat the
state-of-the-art performance on two large protein property prediction datasets.Comment: 8 pages ; 3 figures ; deep learning based sequence-sequence
prediction. in AAAI 201
Ab Initio Identification of Novel Regulatory Elements in the Genome of Trypanosoma brucei by Bayesian Inference on Sequence Segmentation
Background: The rapid increase in the availability of genome information has created considerable demand for both comparative and ab initio predictive bioinformatic analyses. The biology laid bare in the genomes of many organisms is often novel, presenting new challenges for bioinformatic interrogation. A paradigm for this is the collected genomes of the kinetoplastid parasites, a group which includes Trypanosoma brucei the causative agent of human African trypanosomiasis. These genomes, though outwardly simple in organisation and gene content, have historically challenged many theories for gene expression regulation in eukaryotes. Methodology/Principle Findings: Here we utilise a Bayesian approach to identify local changes in nucleotide composition in the genome of T. brucei. We show that there are several elements which are found at the starts and ends of multicopy gene arrays and that there are compositional elements that are common to all intergenic regions. We also show that there is a composition-inversion element that occurs at the position of the trans-splice site. Conclusions/Significance: The nature of the elements discovered reinforces the hypothesis that context dependant RN
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An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
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