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103
port Vector Machines, Kernel Fisher Discriminant analysis
"... Abstract | This review provides an introduction to Sup- ..."
CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning
"... Mass spectrometric based methods for absolute quanti-fication of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or “Q-peptides, ” to act as surrogates. Key to the success of this and related methods for absolute protein quantifi-cation (such as AQ ..."
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as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q
A Bayesian Kernel for the Prediction of Neuron Properties from Binary Gene
, 2005
"... Predicting cellular properties from molecular or genetic data is a challenge for bioinformatics and machine learning. In brain slices of neuronal tissue, it has become possible to both measure electro-physiological properties of a given neuron and to extract a sample of its cytoplasm so that express ..."
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Cited by 1 (0 self)
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Predicting cellular properties from molecular or genetic data is a challenge for bioinformatics and machine learning. In brain slices of neuronal tissue, it has become possible to both measure electro-physiological properties of a given neuron and to extract a sample of its cytoplasm so
Using sequence-specific chemical and structural properties of DNA to predict transcription factor binding sites
- PLoS Comput. Biol
, 2010
"... An important step in understanding gene regulation is to identify the DNA binding sites recognized by each transcription factor (TF). Conventional approaches to prediction of TF binding sites involve the definition of consensus sequences or position-specific weight matrices and rely on statistical a ..."
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Cited by 8 (1 self)
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analysis of DNA sequences of known binding sites. Here, we present a method called SiteSleuth in which DNA structure prediction, computational chemistry, and machine learning are applied to develop models for TF binding sites. In this approach, binary classifiers are trained to discriminate between true
Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins
"... Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure ..."
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within helices or near the protein’s core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure
Multinomial inverse regression for text analysis
- J. Amer. Statist. Assoc
, 2013
"... ABSTRACT: Text data, including speeches, stories, and other document forms, are often connected to sentiment variables that are of interest for research in marketing, economics, and elsewhere. It is also very high dimensional and difficult to incorporate into statistical analyses. This article intro ..."
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Cited by 18 (3 self)
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on sentiment analysis from statistics, econometrics, and machine learning is surveyed and connected. Finally, the methods are applied in two detailed examples and we provide out-of-sample prediction studies to illustrate their effectiveness. Taddy is an Associate Professor of Econometrics and Statistics
Finding groups in gene expression data
- J Biomed Biotechnol
"... The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, h ..."
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Cited by 10 (0 self)
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of clustering method and number of clusters to be recast as a statistical model choice problem, and, for example, significance tests can be carried out. More recently, research in machine learning and data mining has produced new classes of clustering algorithms. These various areas are characterised
HST-582J/6.555J/16.456J Biomedical Signal and Image Processing Chapter 14 -BLIND SOURCE SEPARATION: Principal & Independent Component Analysis
"... Introduction In this chapter we will examine how we can generalize the idea of transforming a time series into an alternative representation, such as the Fourier (frequency) domain, to facilitate systematic methods of either removing (filtering) or adding (interpolating) data. In particular, we wil ..."
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(PCA) and Independent Component Analysis (ICA). Both techniques attempt to find an independent set of vectors onto which we can transform the data. The data that are projected (or mapped) onto each vector are the independent sources. The basic goal in PCA is to decorrelate the signal by projecting
1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE
"... The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comm ..."
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Heterogeneous multi-metric learning for multi-sensor fusion 16. SECURITY CLASSIFICATION OF: In this paper, we propose a multiple-metric learning algorithm to learn jointly a set of optimal homogenous/heterogeneous metrics in order to fuse the data collected from multiple sensors for classification. The learned
Results 1 - 10
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103
