Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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TL;DR: A generative model for NGS data derived from multiple subsections of a single tumor is proposed, and an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies is described, and it is demonstrated that this algorithm predicts clonal relationships that are both phylogenetically and spatially plausible.
Abstract: Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor's clonal composition; however, deconvolving NGS data to infer a tumor's clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.
122 citations
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TL;DR: The aim of this article is to describe some of the technological underpinnings of modern LVCSR systems, which are not robust to mismatched training and test conditions and cannot handle context as well as human listeners despite being trained on thousands of hours of speech and billions of words of text.
Abstract: Over the past decade or so, several advances have been made to the design of modern large vocabulary continuous speech recognition (LVCSR) systems to the point where their application has broadened from early speaker dependent dictation systems to speaker-independent automatic broadcast news transcription and indexing, lectures and meetings transcription, conversational telephone speech transcription, open-domain voice search, medical and legal speech recognition, and call center applications, to name a few. The commercial success of these systems is an impressive testimony to how far research in LVCSR has come, and the aim of this article is to describe some of the technological underpinnings of modern systems. It must be said, however, that, despite the commercial success and widespread adoption, the problem of large-vocabulary speech recognition is far from being solved: background noise, channel distortions, foreign accents, casual and disfluent speech, or unexpected topic change can cause automated systems to make egregious recognition errors. This is because current LVCSR systems are not robust to mismatched training and test conditions and cannot handle context as well as human listeners despite being trained on thousands of hours of speech and billions of words of text.
122 citations
TL;DR: A novel classification strategy based on the monogenic scale space for target recognition in Synthetic Aperture Radar (SAR) image is introduced and significant improvement for recognition accuracy can be achieved in comparison with the baseline algorithms.
Abstract: This paper introduces a novel classification strategy based on the monogenic scale space for target recognition in Synthetic Aperture Radar (SAR) image. The proposed method exploits monogenic signal theory, a multidimensional generalization of the analytic signal, to capture the characteristics of SAR image, e.g., broad spectral information and simultaneous spatial localization. The components derived from the monogenic signal at different scales are then applied into a recently developed framework, sparse representation-based classification (SRC). Moreover, to deal with the data set, whose target classes are not linearly separable, the classification via kernel combination is proposed, where the multiple components of the monogenic signal are jointly considered into a unifying framework for target recognition. The novelty of this paper comes from: 1) the development of monogenic feature via uniformly downsampling, normalization, and concatenation of the components at various scales; 2) the development of score-level fusion for SRCs; and 3) the development of composite kernel learning for classification. In particular, the comparative experimental studies under nonliteral operating conditions, e.g., structural modifications, random noise corruption, and variations in depression angle, are performed. The comparative experimental studies of various algorithms, including the linear support vector machine and the kernel version, the SRC and the variants, kernel SRC, kernel linear representation, and sparse representation of monogenic signal, are performed too. The feasibility of the proposed method has been successfully verified using Moving and Stationary Target Acquiration and Recognition database. The experimental results demonstrate that significant improvement for recognition accuracy can be achieved by the proposed method in comparison with the baseline algorithms.
122 citations
TL;DR: This review discusses the current state of biomarker discovery for the purposes of diagnostics and therapeutic monitoring and proposes a way to think about future biomarker development.
Abstract: This review discusses the current state of biomarker discovery for the purposes of diagnostics and therapeutic monitoring. We underscore relevant challenges that have defined the gap between biomarker discovery and meaningful clinical use. We highlight recent advancements in and propose a way to think about future biomarker development.
122 citations
TL;DR: A new image prior is introduced and used in image restoration based on products of spatially weighted total variations (TV) which provides this prior with the flexibility to better capture local image features than previous TV based priors.
Abstract: In this paper, a new image prior is introduced and used in image restoration. This prior is based on products of spatially weighted total variations (TV). These spatial weights provide this prior with the flexibility to better capture local image features than previous TV based priors. Bayesian inference is used for image restoration with this prior via the variational approximation. The proposed restoration algorithm is fully automatic in the sense that all necessary parameters are estimated from the data and is faster than previous similar algorithms. Numerical experiments are shown which demonstrate that image restoration based on this prior compares favorably with previous state-of-the-art restoration algorithms.
121 citations