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

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 this http URL.

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

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XGBoost: A Scalable Tree Boosting System

An important challenge in neuroimaging multi-subject studies is to take into account that different brains cannot be aligned perfectly. To this end, we extend the classical mass univariate model for group analysis to incorporate uncer- tainty on localization by introducing, for each subject, a spatial "jitter" variable to be marginalized out. We derive a Bayes factor to test for the mean popula- tion effect's sign in each voxel of a search volume, and discuss a Gibbs sampler to compute it. This Bayes factor, which generalizes the classical t-statistic, may be combined with a permutation test in order to control the frequentist false positive rate. Results on both simulated and experimental data suggest that this test may outperform conventional mass univariate tests in terms of detection power, while limiting the problem of overestimating the size of activity clusters.

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DEALING WITH SPATIAL NORMALIZATION ERRORS IN fMRI GROUP INFERENCE USING HIERARCHICAL MODELING

Structured sparsity penalization has recently improved statistical models applied to high-dimensional data in various domains As an extension to medical imaging, the present work incorporates priors on network hierarchies of brain regions into logistic-regression to distinguish neural activity effects These priors bridge two separately studied levels of brain architecture: functional segregation into regions and functional integration by networks Hierarchical region-network priors are shown to better classify and recover 18 psychological tasks than other sparse estimators Varying the relative importance of region and network structure within the hierarchical tree penalty captured complementary aspects of the neural activity patterns Local and global priors of neurobiological knowledge are thus demonstrated to offer advantages in generalization performance, sample complexity, and domain interpretability

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Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging.

Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Estimating the location and magnitude of the current sources that generated these electromagnetic fields is a challenging ill-posed regression problem known as \emph{source imaging}. When considering a group study, a common approach consists in carrying out the regression tasks independently for each subject. An alternative is to jointly localize sources for all subjects taken together, while enforcing some similarity between them. By pooling all measurements in a single multi-task regression, one makes the problem better posed, offering the ability to identify more sources and with greater precision. The Minimum Wasserstein Estimates (MWE) promotes focal activations that do not perfectly overlap for all subjects, thanks to a regularizer based on Optimal Transport (OT) metrics. MWE promotes spatial proximity on the cortical mantel while coping with the varying noise levels across subjects. On realistic simulations, MWE decreases the localization error by up to 4 mm per source compared to individual solutions. Experiments on the Cam-CAN dataset show a considerable improvement in spatial specificity in population imaging. Our analysis of a multimodal dataset shows how multi-subject source localization closes the gap between MEG and fMRI for brain mapping.

Multi-subject MEG/EEG source imaging with sparse multi-task regression

Functional Magnetic Resonance Images acquired during resting-state provide information about the functional organization of the brain through measuring correlations between brain areas. Independent components analysis is the reference approach to estimate spatial components from weakly structured data such as brain signal time courses; each of these components may be referred to as a brain network and the whole set of components can be conceptualized as a brain functional atlas. Recently, new methods using a sparsity prior have emerged to deal with low signal-to-noise ratio data. However, even when using sophisticated priors, the results may not be very sparse and most often do not separate the spatial components into brain regions. This work presents post-processing techniques that automatically sparsify brain maps and separate regions properly using geometric operations, and compares these techniques according to faithfulness to data and stability metrics. In particular, among threshold-based approaches, hysteresis thresholding and random walker segmentation, the latter improves significantly the stability of both dense and sparse models.

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Region segmentation for sparse decompositions: better brain parcellations from rest fMRI

Typical neuroimaging studies analyze associations between physiological or behavioral traits and brain structure or function. Some rely on predicting these scores from neuroimaging data. To explain association between brain features and multiple traits, reduced-rank regression (RRR) models are often used, such as canonical correlation analysis (CCA) and partial least squares (PLS). These methods estimate latent variables, or canonical modes, that maximize the covariations between neuroimaging features and behavioral scores. Here, we investigate theoretically and empirically the extent to which reduced-rank models predict out-of-sample clinical scores from functional connectivity. Experiments on a schizophrenia dataset show that i) significant correlations between canonical modes do not necessarily mean accurate generalization on unseen data, and ii) better accuracy is achieved when taking into account regularized covariance between scores.

/pdf/multi-output-predictions-from-neuroimaging-assessing-reduced-2dcd71jofb.pdf

Bertrand Thirion

Papers

DEALING WITH SPATIAL NORMALIZATION ERRORS IN fMRI GROUP INFERENCE USING HIERARCHICAL MODELING

Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging.

Multi-subject MEG/EEG source imaging with sparse multi-task regression

Region segmentation for sparse decompositions: better brain parcellations from rest fMRI

Multi-output predictions from neuroimaging: assessing reduced-rank linear models