NIPS workshop on New Problems and Methods in Computational Biology (2007)
Chechik, Gal, Leslie, Christina, Noble, William, Rätsch, Gunnar, Morris, Quaid, Tsuda, Koji
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...
Motif Discovery through Predictive Modeling of Gene Regulation (2007)
Middendorf, Manuel, Kundaje, Anshul, Shah, Mihir, Freund, Yoav, Wiggins, Chris H., Leslie, Christina
We present MEDUSA, an integrative method for learning motif models of transcription factor binding sites by incorporating promoter sequence and gene expression data. We use a modern large-margin...
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,...
A classification-based framework for predicting and analyzing gene regulatory response (2006)
Kundaje, Anshul, Middendorf, Manuel, Shah, Mihir, Wiggins, Chris H, Freund, Yoav, Leslie, Christina
Abstract Background We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass....
Predicting Genetic Regulatory Response Using Classification (2004)
Middendorf, Manuel, Kundaje, Anshul, Wiggins, Chris, Freund, Yoav, Leslie, Christina
We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae,...
Predicting Genetic Regulatory Response using Classification: Yeast Stress Response (2004)
Middendorf, Manuel, Kundaje, Anshul, Wiggins, Chris, Freund, Yoav, Leslie, Christina
We present a novel classification-based algorithm called GeneClass for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as...
Semi-Supervised Protein Classification Using Cluster Kernels (2004)
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...
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...
Learning Regulatory Networks from Sparsely Sampled (2003)
Anshul Kundaje, Omar Antar, Tony Jebara, Christina Leslie
We present a probabilistic modeling approach to learning gene transcriptional regulation networks from time series gene expression data that is appropriate for the sparsely and irregularly sampled...
Mismatch String Kernels for SVM Protein (2003)
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...
A Kernel Approach for Learning From Almost Orthogonal Patterns (2002)
Jason Weston, Eleazar Eskin, Christina Leslie, William Staord Noble
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 methods...
Vita: leaf [33].
Thesis submitted to the School of Humanities (Women's Studies), Faculty of Arts. Bibliography: leaves 80-90.
Regulation of the bacterial utilization of choline sulfate. (1973)
Thesis (M.S.)--University of Georgia, 1973.
Protein Ranking by Semi-Supervised Network Propagation
Weston, Jason, Kuang, Rui, Leslie, Christina, Noble, William Stafford
A classification-based framework for predicting and analyzing gene regulatory response
Kundaje, Anshul, Middendorf, Manuel, Shah, Mihir, Wiggins, Chris H, Freund, Yoav, Leslie, Christina
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
NIPS workshop on New Problems and Methods in Computational Biology
Chechik, Gal, Leslie, Christina, Noble, William Stafford, Rätsch, Gunnar, Morris, Quaid, Tsuda, Koji
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,
A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast
Kundaje, Anshul, Xin, Xiantong, Lan, Changgui, Lianoglou, Steve, Zhou, Mei, Zhang, Li, ...
Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression...