Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe

Injection de bruit pour l'apprentissage automatique supervisé et application sur des données d'images et de génomique

Overfitting is a general and important issue in machine learning that has been addressed in several ways through the progress of the field. We first illustrate the importance of such an issue in a collaborative challenge that provided genotype and clinical data to assess response of Rheumatoid Arthritis patients to anti-TNF treatments. We then re-formalise Input Noise Injection (INI) as a set of increasingly popular regularisation methods. We provide a brief taxonomy of its use in supervised learning, its intuitive and theoretical benefits in preventing overfitting and how it can be incorporated in the learning problem. We focus in this context on the dropout trick, review related lines of work of its understanding and adaptations and provide a novel approximation that can be leveraged for general non-linear models, to understand how dropout works. We then present the DropLasso method, as both a generalisation of dropout by incorporating a sparsity penalty, and apply it in the case of single cell RNA-seq data where we show that it can improve accuracy of both Lasso and dropout while performing biologically meaningful feature selection. Finally we build another generalisation of Noise Injection where the noise variable follows a structure that can be either fixed, adapted or learnt during training. We present Adaptive Structured Noise Injection as a regularisation method for shallow and deep networks, where the noise structure applied on the input of a hidden layer follows the covariance of its activations. We provide a fast algorithm for this particular adaptive scheme, study the regularisation properties of our method on linear and multilayer networks using a quadratic approximation, and show improved results in generalisation performance and in representations disentanglement in real dataset experiments.Le surapprentissage est un problème général qui affecte les algorithmes d'apprentissage statistique de différentes manières et qui a été approché de différentes façons dans la littérature. Nous illustrons dans un premier temps un cas réel de ce problème dans le cadre d'un travail collaboratif visant à prédire la réponse de patients atteints d'arthrose rhumatoïde à des traitement anti-inflammatoires. Nous nous intéressons ensuite à la méthode d'Injection de bruit dans les données dans sa généralité en tant que méthode de régularisation. Nous donnons une vue d'ensemble de cette méthode, ses applications, intuitions, algorithmes et quelques éléments théoriques dans le contexte de l'apprentissage supervisé. Nous nous concentrons ensuite sur la méthode du dropout introduite dans le contexte d'apprentissage profond et construisons une nouvelle approximation permettant une nouvelle interprétation de cette méthode dans un cadre général. Nous complémentons cette étude par des expériences sur des simulations et des données réelles. Par la suite, nous présentons une généralisation de la méthode d'injection de bruit dans les données inspirée du bruit inhérent à certains types de données permettant en outre une sélection de variables. Nous présentons un nouvel algorithme stochastique pour cette méthode, étudions ses propriétés de régularisation et l'appliquons au context de séquençage ARN de cellules uniques. Enfin, nous présentons une autre généralisation de la méthode d'Injection de bruit où le bruit introduit suit une structure qui est déduite d'une façon adaptative des paramètres du modèle, en tant que la covariance des activations des unités auxquelles elle est appliquée. Nous étudions les propriétés théoriques de cette nouvelle méthode qu'on nomme ASNI pour des modèles linéaires et des réseaux de neurones multi-couches. Nous démontrons enfin que ASNI permet d'améliorer la performance de généralisation des modèles prédictifs tout en améliorant les représentations résultantes

DropLasso: A robust variant of Lasso for single cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many dropout events. Existing methods to learn molecular signatures from bulk transcriptomic data may therefore not be adapted to scRNA-seq data, in order to automatically classify individual cells into predefined classes. We propose a new method called DropLasso to learn a molecular signature from scRNA-seq data. DropLasso extends the dropout regularisation technique, popular in neural network training, to esti- mate sparse linear models. It is well adapted to data corrupted by dropout noise, such as scRNA-seq data, and we clarify how it relates to elastic net regularisation. We provide promising results on simulated and real scRNA-seq data, suggesting that DropLasso may be better adapted than standard regularisa- tions to infer molecular signatures from scRNA-seq data

Adaptive structured noise injection for shallow and deep neural networks

Dropout is a regularisation technique in neural network training where unit activations are randomly set to zero with a given probability independently. In this work, we propose a generalisation of dropout and other multiplicative noise injection schemes for shallow and deep neural networks, where the random noise applied to different units is not independent but follows a joint distribution that is either fixed or estimated during training. We provide theoretical insights on why such adaptive structured noise injection (ASNI) may be relevant, and empirically confirm that it helps boost the accuracy of simple feedforward and convolutional neural networks, disentangles the hidden layer representations, and leads to sparser representations. Our proposed method is a straightforward modification of the classical dropout and does not require additional computational overhead