Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

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Scikit-learn: Machine Learning in Python

SUMMARY We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.

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Regression Shrinkage and Selection via the Lasso

Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

http://ahvazdownload.persiangig.com/document/article/39.pdf

A wavelet tour of signal processing

Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the

/pdf/regularization-and-variable-selection-via-the-elastic-net-4z8q6j5o0i.pdf

Regularization and variable selection via the elastic net

MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features

Wavelets have motivated development of a host of new ideas in nonparametric regression smoothing. Here we apply the too] of exact risk analysis, to understand the small sample behavior of wavelet estimators, and thus to check directly the conclusions suggested by asymptotics. Comparisons between some wavelet bases, and also between hard and soft thresholding, are given from several viewpoints. Our results provide insight as to why the viewpoints and conclusions of Donoho and Johnstone differ from those of Hall and Patil.

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Exact Risk Analysis of Wavelet Regression

Suppose that a sequence of unknown parameters is observed sub ject to independent Gaussian noise. The EbayesThresh package in the S language implements a class of Empirical Bayes thresholding methods that can take advantage of possible sparsity in the sequence, to improve the quality of estimation. 

The prior for each parameter in the sequence is a mixture of an atom of probability at zero and a heavy-tailed density. Within the package, this can be either a Laplace (double exponential) density or else a mixture of normal distributions with tail behavior similar to the Cauchy distribution. The mixing weight, or sparsity parameter, is chosen automatically by marginal maximum likelihood. If estimation is carried out using the posterior median, this is a random thresholding procedure; the estimation can also be carried out using other thresholding rules with the same threshold, and the package provides the posterior mean, and hard and soft thresholding, as additional options. 

This paper reviews the method, and gives details (far beyond those previously published) of the calculations needed for implementing the procedures. It explains and motivates both the general methodology, and the use of the EbayesThresh package, through simulated and real data examples. When estimating the wavelet transform of an unknown function, it is appropriate to apply the method level by level to the transform of the observed data. The package can carry out these calculations for wavelet transforms obtained using various packages in R and S-PLUS. Details, including a motivating example, are presented, and the application of the method to image estimation is also explored. 

The final topic considered is the estimation of a single sequence that may become progressively sparser along the sequence. An iterated least squares isotone regression method allows for the choice of a threshold that depends monotonically on the order in which the observations are made. An alternative possibility, also discussed in detail, is a particular parametric dependence of the sparsity parameter on the position in the sequence.

/pdf/ebayesthresh-r-programs-for-empirical-bayes-thresholding-159ihvc85c.pdf

EbayesThresh: R Programs for Empirical Bayes Thresholding

Background Coronary bypass surgery often leads to short-term cognitive dysfunction, whereas coronary angioplasty does not. Perioperative cognitive dysfunction usually resolves, although a subgroup of surgical patients may continue to exhibit long-term cognitive dysfunction. The purpose of this study was to compare cognitive function 5 years after randomization to a strategy of either initial coronary surgery or initial angioplasty. Methods and Results Five centers in the Bypass Angioplasty Revascularization Investigation participated in this ancillary study. Patients with multivessel coronary disease randomized to angioplasty or surgery were eligible at the time of their 5-year clinic visit. A battery of five measures previously shown to be sensitive to perioperative changes in cognitive function was administered, including the Logical and Figural Memory Scales from the Wechsler Memory Scale, the Digit Symbol and Digit Span subtests from the Wechsler Adult Intelligence Scale, and Part B of the Reitan Trail Making Test. The 125 study patients were generally similar to the 133 patients who were eligible but did not participate, although study participants were significantly younger (P=.003). The 64 patients randomly assigned to angioplasty had baseline characteristics similar to those of 61 patients randomly assigned to surgery. Cognitive function scores were not significantly different between angioplasty or surgery patients in an intention-to-treat analysis (P=.57). There also was no difference in cognitive function scores when the data were analyzed according to whether the patient had ever undergone bypass surgery (P=.59). Conclusions Long-term cognitive function is similar after coronary bypass surgery and coronary angioplasty in the majority of patients.

Cognitive Function Five Years after Randomization to Coronary Angioplasty or Coronary Artery Bypass Graft Surgery

Karlin has shown that the variation diminishing property possessed by totally positive distributions allows a unified and straightforward presentation of many basic properties of hypothesis tests. His work in fact reveals that total positivity is equivalent to a suitably formulated variation diminishing property. This largely expository article begins with the latter concept and consequently gives a more direct account of the theory. This approach avoids the extensive mathematical preliminaries previously required and isolates the more important statistical property.

Variation Diminishing Transformations: A Direct Approach to Total Positivity and its Statistical Applications

“Bulk” measurements of antiviral innate immune responses from pooled cells yield averaged signals and do not reveal underlying signaling heterogeneity in infected and bystander single cells. We examined such heterogeneity in the small intestine during rotavirus (RV) infection. Murine RV EW robustly activated type I IFNs and several antiviral genes (IFN-stimulated genes) in the intestine by bulk analysis, the source of induced IFNs primarily being hematopoietic cells. Flow cytometry and microfluidics-based single-cell multiplex RT-PCR allowed dissection of IFN responses in single RV-infected and bystander intestinal epithelial cells (IECs). EW replicates in IEC subsets differing in their basal type I IFN transcription and induces IRF3-dependent and IRF3-augmented transcription, but not NF-κB–dependent or type I IFN transcripts. Bystander cells did not display enhanced type I IFN transcription but had elevated levels of certain IFN-stimulated genes, presumably in response to exogenous IFNs secreted from immune cells. Comparison of IRF3 and NF-κB induction in STAT1−/− mice revealed that murine but not simian RRV mediated accumulation of IkB-α protein and decreased transcription of NF-κB–dependent genes. RRV replication was significantly rescued in IFN types I and II, as well as STAT1 (IFN types I, II, and III) deficient mice in contrast to EW, which was only modestly sensitive to IFNs I and II. Resolution of “averaged” innate immune responses in single IECs thus revealed unexpected heterogeneity in both the induction and subversion of early host antiviral immunity, which modulated host range.

/pdf/innate-immune-response-to-homologous-rotavirus-infection-in-310vj9io1v.pdf

Iain M. Johnstone

Papers

Exact Risk Analysis of Wavelet Regression

EbayesThresh: R Programs for Empirical Bayes Thresholding

Cognitive Function Five Years after Randomization to Coronary Angioplasty or Coronary Artery Bypass Graft Surgery

Variation Diminishing Transformations: A Direct Approach to Total Positivity and its Statistical Applications

Innate immune response to homologous rotavirus infection in the small intestinal villous epithelium at single-cell resolution