53,575 research outputs found
Prediction of protein-protein interactions using one-class classification methods and integrating diverse data
This research addresses the problem of prediction of protein-protein interactions (PPI)
when integrating diverse kinds of biological information. This task has been commonly
viewed as a binary classification problem (whether any two proteins do or do not interact)
and several different machine learning techniques have been employed to solve this
task. However the nature of the data creates two major problems which can affect results.
These are firstly imbalanced class problems due to the number of positive examples (pairs
of proteins which really interact) being much smaller than the number of negative ones.
Secondly the selection of negative examples can be based on some unreliable assumptions
which could introduce some bias in the classification results.
Here we propose the use of one-class classification (OCC) methods to deal with the task of
prediction of PPI. OCC methods utilise examples of just one class to generate a predictive
model which consequently is independent of the kind of negative examples selected; additionally
these approaches are known to cope with imbalanced class problems. We have
designed and carried out a performance evaluation study of several OCC methods for this
task, and have found that the Parzen density estimation approach outperforms the rest. We
also undertook a comparative performance evaluation between the Parzen OCC method
and several conventional learning techniques, considering different scenarios, for example
varying the number of negative examples used for training purposes. We found that the
Parzen OCC method in general performs competitively with traditional approaches and in
many situations outperforms them. Finally we evaluated the ability of the Parzen OCC
approach to predict new potential PPI targets, and validated these results by searching for
biological evidence in the literature
Skewed Factor Models Using Selection Mechanisms
Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-t, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset
Inferential stability in systems biology
The modern biological sciences are fraught with statistical difficulties. Biomolecular
stochasticity, experimental noise, and the ālarge p, small nā problem all contribute to
the challenge of data analysis. Nevertheless, we routinely seek to draw robust, meaningful
conclusions from observations. In this thesis, we explore methods for assessing
the effects of data variability upon downstream inference, in an attempt to quantify and
promote the stability of the inferences we make.
We start with a review of existing methods for addressing this problem, focusing upon the
bootstrap and similar methods. The key requirement for all such approaches is a statistical
model that approximates the data generating process.
We move on to consider biomarker discovery problems. We present a novel algorithm for
proposing putative biomarkers on the strength of both their predictive ability and the stability
with which they are selected. In a simulation study, we find our approach to perform
favourably in comparison to strategies that select on the basis of predictive performance
alone.
We then consider the real problem of identifying protein peak biomarkers for HAM/TSP,
an inflammatory condition of the central nervous system caused by HTLV-1 infection.
We apply our algorithm to a set of SELDI mass spectral data, and identify a number of
putative biomarkers. Additional experimental work, together with known results from the
literature, provides corroborating evidence for the validity of these putative biomarkers.
Having focused on static observations, we then make the natural progression to time
course data sets. We propose a (Bayesian) bootstrap approach for such data, and then
apply our method in the context of gene network inference and the estimation of parameters
in ordinary differential equation models. We find that the inferred gene networks
are relatively unstable, and demonstrate the importance of finding distributions of ODE
parameter estimates, rather than single point estimates
Protein-Ligand Scoring with Convolutional Neural Networks
Computational approaches to drug discovery can reduce the time and cost
associated with experimental assays and enable the screening of novel
chemotypes. Structure-based drug design methods rely on scoring functions to
rank and predict binding affinities and poses. The ever-expanding amount of
protein-ligand binding and structural data enables the use of deep machine
learning techniques for protein-ligand scoring.
We describe convolutional neural network (CNN) scoring functions that take as
input a comprehensive 3D representation of a protein-ligand interaction. A CNN
scoring function automatically learns the key features of protein-ligand
interactions that correlate with binding. We train and optimize our CNN scoring
functions to discriminate between correct and incorrect binding poses and known
binders and non-binders. We find that our CNN scoring function outperforms the
AutoDock Vina scoring function when ranking poses both for pose prediction and
virtual screening
Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes
We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth āpermissiveā subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.Published versio
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