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73
Development and Evaluation of Methods for Predicting Protein Levels from Tandem Mass Spectrometry Data
, 2005
"... This work addresses a central problem of Proteomics: estimating the amounts of each of the thousands of proteins in a cell culture or tissue sample. Although laboratory methods involving isotopes have been developed for this problem, we seek a simpler approach, one that uses morestraightforward lab ..."
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This work addresses a central problem of Proteomics: estimating the amounts of each of the thousands of proteins in a cell culture or tissue sample. Although laboratory methods involving isotopes have been developed for this problem, we seek a simpler approach, one that uses morestraightforward laboratory procedures. Specifically, our aim is to use datamining techniques to infer protein levels from the relatively cheap and abundant data available from highthroughput tandem mass spectrometry (MS/MS). In this thesis, we develop and evaluate several techniques for tackling this problem. Specifically, we develop and evaluate different statistical models of MS/MS data. In addition, to evaluate their biological relevance, we test each method on three realworld datasets generated by MS/MS experiments performed on various tissue samples taken from Mouse.
BioMed Central
, 2006
"... A novel approach to phylogenetic tree construction using stochastic optimization and clustering ..."
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A novel approach to phylogenetic tree construction using stochastic optimization and clustering
Towards Formal Structural Representation of Spoken Language: An Evolving Transformation System (ETS) Approach
, 2005
"... Speech recognition has been a very active area of research over the past twenty years. Despite an evident progress, it is generally agreed by the practitioners of the field that performance of the current speech recognition systems is rather suboptimal and new approaches are needed. The motivation ..."
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Speech recognition has been a very active area of research over the past twenty years. Despite an evident progress, it is generally agreed by the practitioners of the field that performance of the current speech recognition systems is rather suboptimal and new approaches are needed. The motivation behind the undertaken research is an observation that the notion of representation of objects and concepts that once was considered to be central in the early days of pattern recognition, has been largely marginalised by the advent of statistical approaches. As a consequence of a predominantly statistical approach to speech recognition problem, due to the numeric, feature vectorbased, nature of representation, the classes inductively discovered from real data using decisiontheoretic techniques have little meaning outside the statistical framework. This is because decision surfaces or probability distributions are difficult to analyse linguistically. Because of the later limitation it is doubtful that the gap between speech recognition and linguistic research can be bridged by the numeric representations. This thesis investigates an alternative, structural, approach to spoken language representation and categorisa
A cellular automata approach to detecting interactions among singlenucleotide polymorphisms in complex multifactorial diseases
 Pacific Symposium on Biocomputing 7
, 2002
"... The identification and characterization of susceptibility genes for common complex multifactorial human diseases remains a statistical and computational challenge. Parametric statistical methods such as logistic regression are limited in their ability to identify genes whose effects are dependent so ..."
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The identification and characterization of susceptibility genes for common complex multifactorial human diseases remains a statistical and computational challenge. Parametric statistical methods such as logistic regression are limited in their ability to identify genes whose effects are dependent solely or partially on interactions with other genes and environmental exposures. We introduce cellular automata (CA) as a novel computational approach for identifying combinations of singlenucleotide polymorphisms (SNPs) associated with clinical endpoints. This alternative approach is nonparametric (i.e. no hypothesis about the value of a statistical parameter is made), is modelfree (i.e. assumes no particular inheritance model), and is directly applicable to casecontrol and discordant sibpair study designs. We demonstrate using simulated data that the approach has good power for identifying highorder nonlinear interactions (i.e. epistasis) among four SNPs in the absence of independent main effects. 1
A Comparison of Statistical Learning Approaches for Engine Torque Estimation
 Control Engineering Practice
, 2007
"... Engine torque estimation has important applications in the automotive industry: for example, automatically setting gears, optimizing engine performance, reducing emissions and designing drivelines. A methodology is described for the online calculation of torque values from the gear, the accelerator ..."
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Engine torque estimation has important applications in the automotive industry: for example, automatically setting gears, optimizing engine performance, reducing emissions and designing drivelines. A methodology is described for the online calculation of torque values from the gear, the accelerator pedal position and the engine rotational speed. It is based on the availability of inputtorque experimental signals that are preprocessed (resampled, filtered and segmented) and then learned by a statistical machinelearning method. Four methods, spanning the main learning principles, are reviewed in a unified framework and compared using the torque estimation problem: linear least squares, linear and nonlinear neural networks and support vector machines. It is found that a nonlinear model structure is necessary for accurate torque estimation. The most efficient torque model built is a nonlinear neural network that achieves about 2 % test normalized mean square error in nominal conditions.
Detecting particles in cryoem micrographs using learned features
 Journal of Structural Biology
, 2004
"... A new learningbased approach is presented for particle detection in cryoelectron micrographs using the Adaboost learning algorithm. The approach builds directly on the successful detectors developed for the domain of face detection. It is a discriminative algorithm which learns important features ..."
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A new learningbased approach is presented for particle detection in cryoelectron micrographs using the Adaboost learning algorithm. The approach builds directly on the successful detectors developed for the domain of face detection. It is a discriminative algorithm which learns important features of the particle’s appearance using a set of training examples of the particles and a set of images that do not contain particles. The algorithm is fast (10 seconds on a 1.3 GHz Pentium M processor), is generic, and is not limited to any particular shape or size of the particle to be detected. The method has been evaluated on a publicly available dataset of 82 cryoEM images of keyhole lympet hemocyanin (KLH). From 998 automatically extracted particle images, the 3D structure of KLH has been reconstructed at a resolution of 23.2 ˚A which is the same resolution as obtained using particles manually selected by a trained user.
Cross Channel Correlations in Tetrode Recordings: Implications for SpikeSorting.
, 1999
"... We are exploring new methods of spike detection to improve spikesorting in tetrode recordings. Based on our observation that the four channels of the tetrode carry highly correlated signals, we propose the use of a hyperellipsoidal thresholding surface in the 4dimensional space of the signal value ..."
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We are exploring new methods of spike detection to improve spikesorting in tetrode recordings. Based on our observation that the four channels of the tetrode carry highly correlated signals, we propose the use of a hyperellipsoidal thresholding surface in the 4dimensional space of the signal values to detect spikes. This surface is determined by the crosschannel covariance matrix and provides a better approximation of the equiprobable surface of the noise amplitude distribution compared to the traditionally used hypercubical thresholding surface. This spike detection procedure greatly improves Rebrik, Wright, & Miller. Cross channel correlations in tetrode recordings. Page 2 of 8 the separation of signal clusters from the noise cluster around the origin. We have extended these approaches to automatic spikesorting in both amplitude and full waveform spaces. Keywords: Tetrode; Spikesorting; Multielectrode recordings 1. Introduction Tetrodes allow recording from many nearby cells ...
Signal Processing Methods for Heart Rate Variability
, 1998
"... and except where otherwise stated, describes my own research. ..."
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Cited by 3 (2 self)
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and except where otherwise stated, describes my own research.
Efficient Learning and Feature Selection in HighDimensional Regression
, 2010
"... We present a novel algorithm for efficient learning and feature selection in highdimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version of the wellknown statistical regression technique of back ..."
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We present a novel algorithm for efficient learning and feature selection in highdimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version of the wellknown statistical regression technique of backfitting. Using the expectationmaximization algorithm, along with variational approximation methods to overcome intractability, we extend our algorithm to include automatic relevance detection of the input features. This variational Bayesian least squares (VBLS) approach retains its simplicity as a linear model, but offers a novel statistically robust blackbox approach to generalized linear regression with highdimensional inputs. It can be easily extended to nonlinear regression and classification problems. In particular, we derive the framework of sparse Bayesian learning, the relevance vector machine, with VBLS at its core, offering significant computational and robustness advantages for this class of methods. The iterative nature of VBLS makes it most suitable for realtime incremental learning, which is crucial especially in the application domain of robotics, brainmachine interfaces, and neural prosthetics, where realtime learning of models for control is needed. We evaluate our algorithm on synthetic and neurophysiological data sets, as well as on standard regression and classification benchmark data sets, comparing it with other competitive statistical approaches and demonstrating its suitability as a dropin replacement for other generalized linear regression techniques.