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Human Genetics and Genomics Advances 2025年 100460页

作者： Samantha J. Klasfeld Katherine A. Knutson Melissa R. Miller Eric B. Fauman Joanne Berghout Rob Moccia Hye In Kim Internal Medicine Research Unit Pfizer Research and Development Cambridge MA 02139 USA Rare Disease Research Unit Pfizer Research and Development Cambridge MA 02139 USA Clinical Omics and Biomarker Statistics Pfizer Research and Development Cambridge MA 02139 USA Machine Learning and Computational Sciences Pfizer Research and Development Cambridge MA 02139 USA

Cardiomyopathy presents a significant medical burden due to frequent hospitalizations and invasive interventions. While cardiomyopathy is considered a rare monogenic disorder caused by rare pathogenic variants in a few genes, emerging evidence suggests that common genetic modifiers influence disease penetrance and clinical variability. Quantifying the interplay between common genetic modifiers and rare pathogenic variants is challenging due to the rarity of cardiomyopathy cases and pathogenic variant carriers. In this study, we utilized large-scale genetic and phenotypic data from the UK Biobank to refine the genetic architecture of hypertrophic and dilated cardiomyopathies. Using ClinVar annotations and variant effect prediction tools, we first identified known and predicted pathogenic variants and evaluated their association with disease risk, age of diagnosis, and quantitative cardiac phenotypes that reflect disease progression. We next examined the impact of polygenic risk scores on disease in the combined sets of known and predicted pathogenic variant carriers. Indeed, the polygenic risk scores were significantly associated with increased disease risk, with rare pathogenic variant carriers in the top 20% polygenic risk having 5.7- and 2.3-times higher risk than those in the bottom 20% for hypertrophic and dilated cardiomyopathy, respectively. We observed stronger associations in the carrier sets that included predicted pathogenic variant carriers, suggesting improved statistical power. In summary, our study adds to the evidence that common genetic modifiers influence the cardiomyopathy disease risk among rare pathogenic variant carriers and illustrates the benefits and limitations of incorporating variant effect predictions to examine the polygenic influence in rare disease variant carriers.

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A new feature selection method based on stability theory - Exploring parameters space to evaluate classification accuracy in neuroimaging data

A new feature selection method based on stability theory - E...

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International Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011

作者： Rondina, Jane M. Shawe-Taylor, John Mourão-Miranda, Janaina Centre for Neuroimaging Sciences Institute of Psychiatry King's College London United Kingdom Department of Computer Science Centre for Computational Statistics and Machine Learning University College London United Kingdom

Recently we proposed a feature selection method based on stability theory. In the present work we present an evaluation of its performance in different contexts through a grid search performed in a subset of its parameters space. The main contributions of this work are: we show that the method can improve the classification accuracy in relation to the wholebrain in different functional datasets;we evaluate the parameters influence in the results, getting some insight in reasonable ranges of values;and we show that combinations of parameters that yield the best accuracies are stable (i.e., they have low rates of false positive selections). © 2012 Springer-Verlag.

关键词： Neuroimaging

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3D Human Pose Estimation Based on Multi-Input Multi-Output Convolutional Neural Network and Event Cameras: A Proof of Concept on the DHP19 Dataset 25th

3D Human Pose Estimation Based on Multi-Input Multi-Output C...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Manilii, Alessandro Lucarelli, Leonardo Rosati, Riccardo Romeo, Luca Mancini, Adriano Frontoni, Emanuele Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60131 Italy Computational Statistics and Machine Learning and Cognition Motion and Neuroscience Istituto Italiano di Tecnologia Genova Italy

ISBN: (纸本)9783030687625

Nowadays Human Pose Estimation (HPE) represents one of the main research themes in the field of computer vision. Despite innovative methods and solutions introduced for frame processing algorithms, the use of standard frame-based cameras still has several drawbacks such as data redundancy and fixed frame-rate. The use of event-based cameras guarantees higher temporal resolution with lower memory and computational cost while preserving the significant information to be processed and thus it represents a new solution for real-time applications. In this paper, the DHP19 dataset was employed, the first and, to date, the only one with HPE data recorded from Dynamic Vision Sensor (DVS) event-based cameras. Starting from the baseline single-input single-output (SISO) Convolutional Neural Network (CNN) model proposed in the literature, a novel multi-input multi-output (MIMO) CNN-based architecture was proposed in order to model simultaneously two different single camera views. Experimental results show that the proposed MIMO approach outperforms the standard SISO model in terms of accuracy and training time. © 2021, Springer Nature Switzerland AG.

关键词： Cameras

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Data mining, data fusion and information management

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IEE Proceedings: Intelligent Transport Systems 2006年第3期153卷 221-229页

作者： Shawe-Taylor, J. De Bie, T. Cristianini, N. Centre for Computational Statistics and Machine Learning University College London Gower Street London WC1E 6BT United Kingdom Department of Engineering Mathematics University of Bristol University Walk Bristol BS8 1TR United Kingdom

The potential impact of advances in data mining, data fusion and information management on the efficient exploitation of the transport infrastructure are addressed. It is argued that the energy currently consumed in transport is poorly exploited for a number of reasons that the application of intelligent infrastructure analysis can significantly address: traffic congestion implies reduced fuel efficiency;the average number of people travelling per car is very low;and public/private transport combination options are difficult to plan. Relevant advances in intelligent information systems are reviewed, and a number of scenarios illustrating how these weaknesses might be addressed are presented. For each, the feasibility of the required technology and the timescales for deployment are discussed.

关键词： Data mining

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Bilevel learning of the group lasso structure 18

Bilevel learning of the group lasso structure

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Jordan Frecon Saverio Salzo Massimiliano Pontil Computational Statistics and Machine Learning Istituto Italiano di Tecnologia (Italy) Computational Statistics and Machine Learning Istituto Italiano di Tecnologia (Italy) and Department of Computer Science University College London (UK)

Regression with group-sparsity penalty plays a central role in high-dimensional prediction problems. However, most of existing methods require the group structure to be known a priori. In practice, this may be a too strong assumption, potentially hampering the effectiveness of the regularization method. To circumvent this issue, we present a method to estimate the group structure by means of a continuous bilevel optimization problem where the data is split into training and validation sets. Our approach relies on an approximation scheme where the lower level problem is replaced by a smooth dual forward-backward algorithm with Bregman distances. We provide guarantees regarding the convergence of the approximate procedure to the exact problem and demonstrate the well behaviour of the proposed method on synthetic experiments. Finally, a preliminary application to genes expression data is tackled with the purpose of unveiling functional groups.

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Observational-Interventional Priors for Dose-Response learning 16

Observational-Interventional Priors for Dose-Response Learni...

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Annual Conference on Neural Information Processing Systems

作者： Ricardo Silva Department of Statistical Science and Centre for Computational Statistics and Machine Learning University College London

ISBN: (纸本)9781510838819

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treatment is not controlled by a known mechanism, is sometimes available. Under some strong assumptions, observational data allows for the estimation of dose-response curves. Estimating such curves nonparametrically is hard: sample sizes for controlled interventions may be small, while in the observational case a large number of measured confounders may need to be marginalized. In this paper, we introduce a hierarchical Gaussian process prior that constructs a distribution over the dose-response curve by learning from observational data, and reshapes the distribution with a nonparametric affine transform learned from controlled interventions. This function composition from different sources is shown to speed-up learning, which we demonstrate with a thorough sensitivity analysis and an application to modeling the effect of therapy on cognitive skills of premature infants.

关键词： response curves Information source observational data Dose-response relationship learning Affine transformation Sensitivity analysis Curves Intervention Premature Infant Standing Orders

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A Bayesian approach to approximate joint diagonalization of square matrices

A Bayesian approach to approximate joint diagonalization of ...

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29th International Conference on machine learning, ICML 2012

作者： Zhong, Mingjun Girolami, Mark Department of Biomedical Engineering Dalian University of Technology Dalian 116023 China Department of Statistical Science Centre for Computational Statistics and Machine Learning University College London London WCIE 7HB United Kingdom

ISBN: (纸本)9781450312851

We present a Bayesian scheme for the approximate diagonalisation of several square matrices which are not necessarily symmetric. A Gibbs sampler is derived to simulate samples of the common eigenvectors and the eigenvalues for these matrices. Several synthetic examples are used to illustrate the performance of the proposed Gibbs sampler and we then provide comparisons to several other joint diagonalization algorithms, which shows that the Gibbs sampler achieves the state-of-the- art performance on the examples considered. As a byproduct, the output of the Gibbs sampler could be used to estimate the log marginal likelihood, however we employ the approximation based on the Bayesian information criterion (BIC) which in the synthetic examples considered correctly located the number of common eigenvectors. We then succesfully applied the sampler to the source separation problem as well as the common principal component analysis and the common spatial pattern analysis problems. Copyright 2012 by the author(s)/owner(s).

关键词： Eigenvalues and eigenfunctions

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The group Dantzig selector

The group Dantzig selector

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13th International Conference on Artificial Intelligence and statistics, AISTATS 2010

作者： Liu, Han Zhang, Jian Jiang, Xiaoye Liu, Jun Machine Learning Department Carnegie Mellon University United States Statistics Department Purdue University United States Computational Mathematics Stanford University United States Computer Science Department Arizona State University United States

We introduce a new method - the group Dantzig selector - for high dimensional sparse regression with group structure, which has a convincing theory about why utilizing the group structure can be beneficial. Under a group restricted isometry condition, we obtain a significantly improved nonasymptotic 2-norm bound over the basis pursuit or the Dantzig selector which ignores the group structure. To gain more insight, we also introduce a surprisingly simple and intuitive sparsity oracle condition to obtain a block 1-norm bound, which is easily accessible to a broad audience in machine learning community. Encouraging numerical results are also provided to support our theory.

关键词： learning systems

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Excess risk bounds for multitask learning with trace norm regularization

Excess risk bounds for multitask learning with trace norm re...

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26th Conference on learning Theory, COLT 2013

作者： Maurer, Andreas Pontil, Massimiliano Adalbertstr. 55 D-80799 München Germany Department of Computer Science Centre for Computational Statistics and Machine Learning University College London Malet Place London WC1E United Kingdom

Trace norm regularization is a popular method of multitask learning. We give excess risk bounds with explicit dependence on the number of tasks, the number of examples per task and properties of the data distribution. The bounds are independent of the dimension of the input space, which may be infinite as in the case of reproducing kernel Hilbert spaces. A byproduct of the proof are bounds on the expected norm of sums of random positive semidefinite matrices with subexponential moments. © 2013 A. Maurer & M. Pontil.

关键词： learning systems

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Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start

arXiv

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arXiv 2022年

作者： Grazzi, Riccardo Pontil, Massimiliano Salzo, Saverio Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy University College of London United Kingdom Universitá la Sapienza di Roma Italy and Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy

We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction map. This type of problems include instances of meta-learning, equilibrium models, hyperparameter optimization and data poisoning adversarial attacks. Several recent works have proposed algorithms which warm-start the lower-level problem, i.e. they use the previous lower-level approximate solution as a staring point for the lower-level solver. This warm-start procedure allows one to improve the sample complexity in both the stochastic and deterministic settings, achieving in some cases the order-wise optimal sample complexity. However, there are situations, e.g., meta learning and equilibrium models, in which the warm-start procedure is not well-suited or ineffective. In this work we show that without warm-start, it is still possible to achieve order-wise (near) optimal sample complexity. In particular, we propose a simple method which uses (stochastic) fixed point iterations at the lower-level and projected inexact gradient descent at the upper-level, that reaches an ϵ-stationary point using O(ϵ−2) and Õ(ϵ−1) samples for the stochastic and the deterministic setting, respectively. Finally, compared to methods using warm-start, our approach yields a simpler analysis that does not need to study the coupled interactions between the upper-level and lower-level iterates. Copyright © 2022, The Authors. All rights reserved.

关键词： Convex optimization

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