RANKPROP: a web server for protein remote homology detection (2009)
Melvin, Iain, Weston, Jason, Leslie, Christina, Noble, William Stafford
Summary: We present a large-scale implementation of the Rankprop protein homology ranking algorithm in the form of an openly accessible web server. We use the NRDB40 PSI-BLAST all-versus-all protein...
Iain Melvin, Jason Weston, William Stafford Noble, Christina Leslie
Editor: Predicting a protein’s structural class from its amino acid sequence is a fundamental problem in computational biology. Recent machine learning work in this domain has focused on developing...
BioMed Central Proceedings Protein Ranking by Semi-Supervised Network Propagation (2008)
Bmc Bioinformatics, Jason Weston, Rui Kuang, Christina Leslie, William Stafford Noble
Background: Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods, such as...
Combining classifiers for improved classification of proteins from sequence or structure (2008)
Melvin, Iain, Weston, Jason, Leslie, Christina S, Noble, William S
Abstract Background Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been...
Jason Weston, William Stafford Noble, Christina Leslie, Nello Cristianini
Predicting a protein’s structural class from its amino acid sequence is a fundamental problem in computational biology. Recent machine learning work in this domain has focused on developing new...
The Need for Open Source Software in Machine Learning (2008)
Sören Sonnenburg, Mikio L. Braun, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann Lecun, ...
Learning Gene Functional Classi � cations from Multiple Data Types (2008)
Paul Pavlidis, Jason Weston, Jinsong Cai, William Stafford Noble
In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene...
Christina Leslie, Jason Weston, Eleazar Eskin, William Stafford Noble
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure...
Gökhan H. Bakır, Max Planck, Léon Bottou, Jason Weston
We propose to selectively remove examples from the training set using probabilistic estimates related to editing algorithms (Devijver and Kittler, 1982). This heuristic procedure aims at creating a...
Thomas Navin Lal, Olivier Chapelle, Jason Weston, André Elisseeff
Although many embedded feature selection methods have been introduced during the last few years, a unifying theoretical framework has not been developed to date. We start this chapter by defining...
Solving MultiClass Support Vector Machines with LaRank (2008)
Antoine Bordes, Léon Bottou, Patrick Gallinari, Jason Weston
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optimization algorithms...
Léon Bottou, Olivier Chapelle, Denis Decoste, Jason Weston, London England
This is a draft containing only raykar chapter.tex and an abbreviated front matter. Please check that the formatting and small changes have been performed correctly. Please verify the affiliation....
Christina Leslie, Jason Weston, Eleazar Eskin, William Stafford Noble
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure...
Submitted to Machine Learning. Summary (2008)
Isabelle Guyon, Jason Weston, Stephen Barnhill
DNA micro-arrays now permit scientists to screen thousands of genes simultaneously and determine whether those genes are active, hyperactive or silent in normal or cancerous tissue. Because these new...
Olivier Chapelle, Vladimir Vapnik, Jason Weston
We introduce an algorithm for estimating the values of a function at a set of test points x
Adaptive Margin Machines for Classification Learning (2007)
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Quadratic Programming Machines (SVMs) [11], and Linear Programming Machines [1, 12] were based on...
Jason Weston, Bernhard Schölkopf, Alex Smola, Klaus-robert Müller
Abstract—We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of...
Jason Weston, Christina Leslie, William Staord Noble
Abstract. In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel...
Sebastian Mika, Jason Weston, Alex Smola
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a uni ed framework in terms of a nonlinear variant of the Rayleigh...
The Need for Open Source Software in Machine Learning (2007)
Sonnenburg, Sören, Braun, Mikio, Ong, Cheng Soon, Bengio, Samy, Bottou, Leon, Holmes, Geoffrey, ...
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a...
The Need for Open Source Software in Machine Learning (2007)
Sonnenburg, Sören, Braun, Mikio, Ong, Cheng Soon, Bengio, Samy, Bottou, Leon, Holmes, Geoff, ...
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a...
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition (2007)
Melvin, Iain, Ie, Eugene, Kuang, Rui, Weston, Jason, Noble, William, Leslie, Christina
Abstract Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new...
Solving MultiClass Classification with LaRank (2007)
Bordes, Antoine, Bottou, Leon, Gallinari, Patrick, Weston, Jason
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optimization algorithms...
Protein Ranking by Semi-Supervised Network Propagation (2006)
Weston, Jason, Kuang, Rui, Leslie, Christina, Noble, William
Abstract Background Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods,...
Trading convexity for scalability (2006)
Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis....
Large Scale Transductive SVMs (2006)
Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou, Thorsten Joachims
We show how the concave-convex procedure can be applied to transductive SVMs, which traditionally require solving a combinatorial search problem. This provides for the first time a highly scalable...
Inference with the universum (2006)
Jason Weston, Ronan Collobert, Léon Bottou, Vladimir Vapnik
In this paper we study a new framework introduced by Vapnik (1998) and Vapnik (2006) that is an alternative capacity concept to the large margin approach. In the particular case of binary...
Trading convexity for scalability (2006)
Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis....
Inference with the universum (2006)
Jason Weston, Ronan Collobert, Fabian Sinz, Léon Bottou, Vladimir Vapnik
In this paper we study a new framework introduced by Vapnik (1998) and Vapnik (2006) that is an alternative capacity concept to the large margin approach. In the particular case of binary...
Large scale transductive SVMs (2006)
Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou
We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, which traditionally requires solving a combinatorial search problem. This provides for the first time...
Trading convexity for scalability (2006)
Ronan Collobert, Jason Weston, Léon Bottou
This is an expanded version of the original document. The new appendix discusses previous works whose existence was not known to us. It also points out differences that we believe are significant...
Large scale transductive SVMs (2006)
Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou
We show how the Concave-Convex Procedure can be applied to Transductive SVMs, which traditionally requires solving a combinatorial search problem. This provides for the first time a highly scalable...
Inference with the universum (2006)
Jason Weston, Ronan Collobert, Fabian Sinz, Léon Bottou, Vladimir Vapnik
In this paper we study a new framework introduced by Vapnik (1998) that is an alternative capacity concept to the large margin approach. In the particular case of binary classification, we are given...
William S. Noble, Rui Kuang, Christina Leslie, Jason Weston, W. S. Noble
Identifying remote protein homologs by network propagation
Rui Kuang, Jason Weston, William Stafford Noble, Christina Leslie
Motivation: Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used...
Fast Kernel Classifiers With Online And Active Learning (2005)
Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou
Very high dimensional learning systems become theoretically possible when training examples are abundant. The computing cost then becomes the limiting factor. Any efficient learning algorithm should...
Multi-class protein fold recognition using adaptive codes (2005)
Eugene Ie, Jason Weston, William Stafford Noble, Christina Leslie
We develop a novel multi-class classification method based on output codes for the problem of classifying a sequence of amino acids into one of many known protein structural classes, called folds....
A general regression technique for learning transductions (2005)
Corinna Cortes, Mehryar Mohri, Jason Weston
The problem of learning a transduction, that is a string-to-string mapping, is a common problem arising in natural language processing and computational biology. Previous methods proposed for...
Multi-class protein fold recognition using adaptive codes (2005)
Eugene Ie, Jason Weston, William Stafford Noble, Christina Leslie
We develop a novel multi-class classification method based on output codes for the problem of classifying a sequence of amino acids into one of many known protein structural classes, called folds....
Semi-supervised protein classification using cluster kernels (2005)
Weston, Jason, Leslie, Christina, Ie, Eugene, Zhou, Dengyong, Elisseeff, Andre, Noble, William Stafford
Motivation: Building an accurate protein classification system depends critically upon choosing a good representation of the input sequences of amino acids. Recent work using string kernels for...
Semi-supervised protein classification using cluster kernels (2005)
Weston, Jason, Leslie, Christina, Ie, Eugene, Zhou, Dengyong, Elisseeff, Andre, Noble, William Stafford
Motivation: Building an accurate protein classification system depends critically upon choosing a good representation of the input sequences of amino acids. Recent work using string kernels for...
Motif-based protein ranking by network propagation (2005)
Kuang, Rui, Weston, Jason, Noble, William Stafford, Leslie, Christina
Motivation: Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used...
Motif-based protein ranking by network propagation (2005)
Kuang, Rui, Weston, Jason, Noble, William Stafford, Leslie, Christina
Motivation: Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used...
Protein ranking: from local to global structure in the protein similarity network (2004)
Weston, Jason, Elisseeff, Andre, Zhou, Dengyong, Leslie, Christina S., Noble, William Stafford
Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire...
Learning to Find Pre-Images (2004)
BakIr, Goekhan, Weston, Jason, Schölkopf, Bernhard
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of...
Learning with Local and Global Consistency (2004)
Zhou, Dengyong, Bousquet, Olivier, Lal, Thomas Navin, Weston, Jason, Schölkopf, Bernhard
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Ranking on Data Manifolds (2004)
Zhou, Dengyong, Weston, Jason, Gretton, Arthur, Bousquet, Olivier, Schölkopf, Bernhard
The Google search engine has enjoyed a huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a...
Semi-Supervised Protein Classification using Cluster Kernels (2004)
Weston, Jason, Leslie, Christina, Zhou, Dengyong, Elisseeff, Andre, Noble, William Stafford
A key issue in supervised protein classification is the representation of input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art...
Ranking on data manifolds (2004)
Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf
The Google search engine has enjoyed huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a...
Learning with local and global consistency (2004)
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Fast Binary and Multi-Output Reduced Set Selection (2004)
Jason Weston, Jason Weston, Gökhanh. Bakır
Abstract. We propose fast algorithms for reducing the number of kernel evaluations in the testing phase for methods such as Support Vector Machines (SVM) and Ridge Regression (RR). For non-sparse...
Jason Weston, Bernhard Schölkopf, Olivier Bousquet, Tobias Mann, William Stafford Noble, Jason Weston, ...
We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension,...
Learning with local and global consistency (2004)
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Ranking on data manifolds (2004)
Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf
The Google search engine has enjoyed huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a...
Ranking on data manifolds (2004)
Dengyong Zhou, Dengyong Zhou, Jason Weston, Jason Weston, Arthur Gretton, Olivier Bousquet, ...
The Google search engine has had a huge success with its PageRank web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the World Wide Web using random walk....
Prediction on spike data using kernel algorithms (2004)
Jan Eichhorn, Andreas Tolias, Er Zien, Malte Kuss, Carl Edward Rasmussen, Jason Weston, ...
We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the activity of a...
Learning to find pre-images (2004)
Gökhan H. Bakır, Jason Weston, Bernhard Schölkopf
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of...
Mismatch string kernels for discriminative protein classification (2004)
Christina Leslie, Eleazar Eskin, Adiel Cohen, Jason Weston, William Stafford Noble
Motivation Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine...
Learning with local and global consistency (2004)
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Learning to find pre-images (2004)
Gökhan H. Bakır, Jason Weston, Bernhard Schölkopf
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of...
Jason Weston, Bernhard Schölkopf, Olivier Bousquet, Tobias Mann, William Stafford Noble, Jason Weston, ...
We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension,...
Semi-supervised protein classification using cluster kernels (2004)
Jason Weston, Christina Leslie, Eugene Ie, Dengyong Zhou, Andre Elisseeff, William Stafford Noble
doi:10.1093/bioinformatics/bti497
Mismatch string kernels for discriminative protein classification (2004)
Leslie, Christina, Eskin, Eleazar, Cohen, Adiel, Weston, Jason, Noble, William Stafford
Motivation: Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine...
Mismatch string kernels for discriminative protein classification (2004)
Leslie, Christina S., Eskin, Eleazar, Cohen, Adiel, Weston, Jason, Noble, William Stafford
Motivation: Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine...
Mismatch string kernels for discriminative protein classification (2004)
Leslie, Christina, Eskin, Eleazar, Cohen, Adiel, Weston, Jason, Noble, William Stafford
Motivation: Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine...
Extension of the nu-SVM Range for Classification (2003)
Perez-Cruz, Fernando, Weston, Jason, Herrmann, Daniel, Schölkopf, Bernhard
In this paper, we revisit how maximum margin classifiers can be obtained for a separable training data set, to also enable us construct ``hard'' margin classifiers for non-separable data sets. This...
Feature selection and transduction for prediction of molecular bioactivity for drug design (2003)
Weston, Jason, Perez-Cruz, Fernando, Elisseeff, Andre, Bousquet, Olivier, Chapelle, Dr Olivier, Schölkopf, Bernhard
Motivation: In drug discovery a key task is to identify characteristics that separate active (binding) compounds from inactive (non-binding) ones. An automated prediction system can help reduce...
Use of the Zero-Norm with Linear Models and Kernel Methods (2003)
Jason Weston, Andre Elisseeff, Berhard Scholkopf, Mike Tipping, Pack Kaelbling
We explore the use of the so-called zero-norm of the parameters of linear models in learning.
Use of the zero-norm with linear models and kernel methods (2003)
Jason Weston, André Elisseeff, Bernhard Schölkopf, Pack Kaelbling
We explore the use of the so-called zero-norm of the parameters of linear models in learning. Minimization of such a quantity has many uses in a machine learning context: for variable or feature...
Christina S. Leslie, Eleazar Eskin, Adiel Cohen, Jason Weston, William Stafford Noble
Motivation: Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine...
Learning to Find Pre-Images (2003)
Gökhan H. Bakr, Jason Weston, Bernhard Schölkopf, Bernhard Sch Olkopf
We consider the problem of reconstructing patterns from a feature map.
Learning with Local and Global Consistency (2003)
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf, Bernhard Sch Olkopf
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Ranking on Data Manifolds (2003)
Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf, Bernhard Sch Olkopf
The Google search engine has enjoyed huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a...
Prediction on Spike Data Using Kernel Algorithms (2003)
Jan Eichhorn, Andreas Tolias, Alexander Zien, Er Zien, Malte Kuss, Carl Edward Rasmussen, ...
We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the activity of a...
Semi-supervised protein classification using cluster kernels (2003)
Jason Weston, Christina Leslie, Dengyong Zhou, Andre Elisseeff, William Stafford Noble
A key issue in supervised protein classification is the representation of input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art...
Learning gene functional classifications from multiple data types (2002)
Paul Pavlidis, Jinsong Cai, Jason Weston, William Stafford Noble
In our attempts to understand cellular function at the molecular level, we must be able to synthesize in-formation from disparate types of genomic data. We consider the problem of inferring gene...
A kernel method for multi-labelled classification (2001)
Andr E Elisseeff, Jason Weston
This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of...
Gene functional classification from heterogeneous data (2001)
Paul Pavlidis, Jinsong Cai, Jason Weston, William Noble Grundy
In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene...
A kernel method for multi-labelled classification (2001)
This report presents a SVM like learning system to handle multi-label problems. Such problems arise naturally in bio-informatics. Consider for instance the MIPS Yeast genome database in [12], it is...
Gene functional classification from heterogeneous data (2001)
Paul Pavlidis, Jinsong Cai, Jason Weston, William Noble Grundy
Submitted for publication.
Vicinal risk minimization (2001)
Olivier Chapelle, Jason Weston, L Eon Bottou, Vladimir Vapnik
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector Machines or...
A kernel method for multi-labelled classification (2001)
This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of...
A kernel method for multi-labelled classification (2001)
This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of...
Statistical learning and kernel methods (2000)
Bernhard Schölkopf, Isabelle Guyon, Jason Weston
Abstract. We briefly describe the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. In addition, we present an overview of applications of kernel methods...
Adaptive margin support vector machines (2000)
In this paper we propose a new learning algorithm for classification learning based on the Support Vector Machine (SVM) approach. Existing approaches for constructing SVMs [12] are based on...
Invariant Feature Extraction and Classification in Kernel Spaces (2000)
Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex Smola, Klaus-Robert Müller
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinear variant of the Rayleigh...
Adaptive Margin Support Vector Machines (2000)
this article also provide a website to obtain the data
Adaptive Margin Support Vector Machines for Classification (2000)
In this paper we propose a new learning algorithm for classification learning based on the Support Vector Machine (SVM) approach. Existing approaches for constructing SVMs [12] are based on...
Leave-One-Out Support Vector Machines (1999)
We present a new learning algorithm for pattern recognition inspired by a recent upper bound on leave--one--out error [ Jaakkola and Haussler, 1999 ] proved for Support Vector Machines (SVMs) [...
Fisher Discriminant Analysis With Kernels (1999)
Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Klaus-Robert Müller
A non-linear classification technique based on Fisher's discriminant is proposed. Main ingredient is the kernel trick which allows to efficiently compute the linear Fisher discriminant in...
Multi-class Support Vector Machines (1998)
this paper. Thanks also to M. Stitson for writing the code for one-against-one and one-against-all SV classification. We also thank Kai Vogtlaender for useful comments. In communication with V....
Support Vector Regression with ANOVA Decomposition Kernels (1997)
Mark O. Stitson, Alex Gammerman, Vladimir Vapnik, Volodya Vovk, Chris Watkins, Jason Weston, ...
Support Vector Machines using ANOVA Decomposition Kernels (SVAD) [Vapng] are a way of imposing a structure on multi-dimensional kernels which are generated as the tensor product of one-dimensional...
Protein ranking: From local to global structure in the protein similarity network
Weston, Jason, Elisseeff, Andre, Zhou, Dengyong, Leslie, Christina S., Noble, William Stafford
Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire...
Protein ranking: From local to global structure in the protein similarity network
Weston, Jason, Elisseeff, Andre, Zhou, Dengyong, Leslie, Christina S., Noble, William Stafford
Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire...
Protein Ranking by Semi-Supervised Network Propagation
Weston, Jason, Kuang, Rui, Leslie, Christina, Noble, William Stafford
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition
Melvin, Iain, Ie, Eugene, Kuang, Rui, Weston, Jason, Stafford, William Noble, Leslie, Christina
Dealing with large diagonals in kernel matrices
Jason Weston, Bernhard Schölkopf, Eleazar Eskin, Christina Leslie, William Noble
Kernel methods, Support Vector Machines, pattern recognition, bioinformatics, microarray data analysis, transduction, regularization,
Combining classifiers for improved classification of proteins from sequence or structure
Melvin, Iain, Weston, Jason, Leslie, Christina S, Noble, William S
Rankprop: a web server for protein remote homology detection
Melvin, Iain, Weston, Jason, Leslie, Christina, Noble, William Stafford
Summary: We present a large-scale implementation of the Rankprop protein homology ranking algorithm in the form of an openly accessible web server. We use the NRDB40 PSI-BLAST all-versus-all protein...
Mismatch String Kernels for SVM Protein Classification
Christina Leslie, Eleazar Eskin, Jason Weston, William Stafford Noble
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure...
Barnickel, Thorsten, Weston, Jason, Collobert, Ronan, Mewes, Hans-Werner, Stümpflen, Volker
To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can...