2,160 research outputs found
Bounded Coordinate-Descent for Biological Sequence Classification in High Dimensional Predictor Space
We present a framework for discriminative sequence classification where the
learner works directly in the high dimensional predictor space of all
subsequences in the training set. This is possible by employing a new
coordinate-descent algorithm coupled with bounding the magnitude of the
gradient for selecting discriminative subsequences fast. We characterize the
loss functions for which our generic learning algorithm can be applied and
present concrete implementations for logistic regression (binomial
log-likelihood loss) and support vector machines (squared hinge loss).
Application of our algorithm to protein remote homology detection and remote
fold recognition results in performance comparable to that of state-of-the-art
methods (e.g., kernel support vector machines). Unlike state-of-the-art
classifiers, the resulting classification models are simply lists of weighted
discriminative subsequences and can thus be interpreted and related to the
biological problem
Remote Homology Detection of Protein Sequences
The classification of protein sequences using string kernels
provides valuable insights for protein function prediction. Almost
all string kernels are based on patterns that are not independent,
and therefore the associated scores are obtained using a set of
redundant features. In this talk we will discuss how a class of
patterns, called Irredundant, is specifically designed to address
this issue. Loosely speaking the set of Irredundant patterns is the
smallest class of independent patterns that can describe all
patterns in a string. We present a classification method based on
the statistics of these patterns, named Irredundant Class. Results
on benchmark data show that Irredundant Class outperforms most of
the string kernel methods previously proposed, and it achieves
results as good as the current state-of-the-art methods with a fewer
number of patterns. Unfortunately we show that the information
carried by the irredundant patterns can not be easily interpreted,
thus alternative notions are needed
A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models
<p>Abstract</p> <p>Background</p> <p>Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).</p> <p>Results</p> <p>We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function.</p> <p>Conclusions</p> <p>The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.</p
A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis
<p>Abstract</p> <p>Background</p> <p>Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.</p> <p>Results</p> <p>In this paper, a novel building block of proteins called Top-<it>n</it>-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-<it>n</it>-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-<it>n</it>-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-<it>n</it>-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-<it>n</it>-grams and LSA gives significantly better results compared to related methods.</p> <p>Conclusion</p> <p>The method based on Top-<it>n</it>-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-<it>n</it>-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.</p
Kernel methods in genomics and computational biology
Support vector machines and kernel methods are increasingly popular in
genomics and computational biology, due to their good performance in real-world
applications and strong modularity that makes them suitable to a wide range of
problems, from the classification of tumors to the automatic annotation of
proteins. Their ability to work in high dimension, to process non-vectorial
data, and the natural framework they provide to integrate heterogeneous data
are particularly relevant to various problems arising in computational biology.
In this chapter we survey some of the most prominent applications published so
far, highlighting the particular developments in kernel methods triggered by
problems in biology, and mention a few promising research directions likely to
expand in the future
Fast protein superfamily classification using principal component null space analysis.
The protein family classification problem, which consists of determining the family memberships of given unknown protein sequences, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular functions and medical diagnosis. Neural networks and Bayesian methods have performed well on the protein classification problem, achieving accuracy ranging from 90% to 98% while running relatively slowly in the learning stage. In this thesis, we present a principal component null space analysis (PCNSA) linear classifier to the problem and report excellent results compared to those of neural networks and support vector machines. The two main parameters of PCNSA are linked to the high dimensionality of the dataset used, and were optimized in an exhaustive manner to maximize accuracy. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .F74. Source: Masters Abstracts International, Volume: 44-03, page: 1400. Thesis (M.Sc.)--University of Windsor (Canada), 2005
Motif kernel generated by genetic programming improves remote homology and fold detection
BACKGROUND: Protein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kernel on shared occurrences of discrete sequence motifs. Still, many protein sequences fail to be classified correctly for a lack of a suitable set of motifs for these sequences. RESULTS: We introduce the GPkernel, which is a motif kernel based on discrete sequence motifs where the motifs are evolved using genetic programming. All proteins can be grouped according to evolutionary relations and structure, and the method uses this inherent structure to create groups of motifs that discriminate between different families of evolutionary origin. When tested on two SCOP benchmarks, the superfamily and fold recognition problems, the GPkernel gives significantly better results compared to related methods of remote homology detection. CONCLUSION: The GPkernel gives particularly good results on the more difficult fold recognition problem compared to the other methods. This is mainly because the method creates motif sets that describe similarities among subgroups of both the related and unrelated proteins. This rich set of motifs give a better description of the similarities and differences between different folds than do previous motif-based methods
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