For the prediction transfer experiments, we again fit an elastic net classifier to predict cancer subtype and separated the samples into two groups: samples with age within one standard deviation (i.e. stream We then generated two embeddings for the internal and external datasets: (i) one for samples from the four datasets used for training, and (ii) another for the left out samples from the fifth dataset. As a motivating example, Figure 2a shows how confounder signals might dominate true signals in gene expression data. We pretrain our adversary model accordingly to predict the confounder as successfully as possible. In Figure 5ai, we colored all samples by their ER labels. For this dataset, we used two different confounder variables as two separate use cases: sex as a binary confounder, and age as a continuous-valued one. In this paper we use very deep autoencoders to map small color images to short binary codes. trying to eliminate confounder-sourced variations from the expression and outputting a corrected version of the expression matrix. This case simulates a substantial age distribution shift. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from … ���J��������\����p�����$/��JUvr�yK ��0�&��lߺ�8�SK(�һ�]8G_o��C\R����r�{�ÿ��Vu��1''j�϶��,�F� dj�YF�gq�bHUU��ҧ��^�7I��P0��$U���5(�a@�M�;�l {U�c34��x�L�k�tmmx�6��j�q�.�ڗ&��.NRVQ4T_V���o�si��������"8h����uwׁ���5L���pn�mg�Hq��TE� �QV�D�"��Ŕݏ�. Our code and data are available at Subplots are colored by (i) dataset, (ii) ER status and (iii) cancer grade. %���� AD-AE generates embeddings that are robust to confounders and generalizable to different domains. RAFT can improve the performance of computer vision systems in tracking a specific object of interest or tracking all objects of a particular type or category in the video. The research of M.W. IEEE Computer Society, NW Washington, DC, USA. Autoencoder is a kind of unsupervised learning method, data need not be annotated, so they are easier to collect. Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). By applying AD-AE to two distinct gene expression datasets, we show that our model can (i) generate embeddings that do not encode confounder information, (ii) conserve the biological signals present in the original space and (iii) generalize successfully across different confounder domains. These methods all handle non-linear batch effects. The PC plot in Figure 2c highlights the distinct separation between the external dataset and the two training datasets. Jonathan Masci, Ueli Meier, Dan Cireşan, Jürgen Schmidhuber. 5 0 obj endobj These sources of variations, called confounders, produce embeddings that fail to transfer to different domains, i.e. << /S /GoTo /D (section.0.4) >> It is not straightforward to use promising unsupervised models on gene expression data because expression measurements often contain out-of-interest sources of variation in addition to the signal we seek. All rights reserved. Paul G. Allen School of Computer Science & Engineering, University of Washington. With the autoencoder paradigm in mind, we began an inquiry into the question of what can shape a good, useful representation. The optimal number of latent nodes might differ based on the dataset and the specific tasks the embeddings will be used on; we tried to select a reasonable latent embedding size with respect to the number of samples and features we had such that we reduce the dimension of the input features by 10%. In this paper, we propose the “adversarial autoencoder” (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution. We used the same autoencoder architecture for the AD-AE as well. We also compared against other commonly used approaches to confounder removal. various application domains, autoencoder has been applied. Our first experiment aimed to demonstrate that AD-AE could successfully encode the biological signals we wanted while not detecting the selected confounders. This corresponds to updating the weights of the autoencoder to minimize  Equation 1 while maximizing  Equation 2 (minimizing the negative of the objective). Autoencoder is an artificial neural network used for unsupervised learning of efficient codings.The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.Recently, the autoencoder concept has become more widely used for learning generative models of data. $\endgroup$ – abunickabhi Sep 21 '18 at 10:45 Using unsupervised models to learn biologically meaningful representations would make it possible to map new samples to the learned space and adapt our model to any downstream task. (2019), or (ii) an adversarial approach for batch removal, such as training an autoencoder with two separate decoder networks that correspond to two different batches along with an adversarial discriminator to differentiate the batches (Shaham, 2018) or generative adversarial networks trained to match distributions of samples from different batches (Upadhyay and Jain, 2019) or to align different manifolds (Amodio and Krishnaswamy, 2018). (2020), which investigated the effect of the number of latent dimensions using multiple metrics on a variety of dimensionality reduction techniques. Abstract:This paper targets on designing a query-based dataset recommendation system, which accepts a query denoting a user's research interest as a set of research papers and returns a list of recommended datasets that are ranked by the potential usefulness for the user's research need. To simulate this problem, we use a separate set of samples from a different GEO study from the KMPlot data. Getting Data and Training Method I have retrieved car images from image net using Urllib and … Advances in profiling technologies are rapidly increasing the availability of expression datasets. View Auto-Encoder Research Papers on for free. We take the two GEO datasets with the highest number of samples and plot the first two principal components (PCs) (Wold et al., 1987) to examine the strongest sources of variation. To organize these results we make use of meta-priors believed useful for downstream tasks, such as disentanglement and hierarchical organization of features. Batch mean-centering: (Sims et al., 2008) subtracts the average expression of all samples from the same confounder class (e.g. This rich information source has been explored by many studies, ranging from those that predict complex traits (Geeleher et al., 2014; Golub et al., 1999; Shedden et al., 2008) to those that learn expression modules (Segal et al., 2005; Tang et al., 2001; Teschendorff et al., 2007). We could extend our model by incorporating multiple adversarial networks to account for various confounders. In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. (, Shedden K. (2017), which use adversarial training to eliminate confounders. In this article, we introduce the Adversarial Deconfounding AutoEncoder (AD-AE) approach to deconfounding gene expression latent spaces. Here we present a general mathematical framework for the study of both linear and non-linear autoencoders. Several recent studies accounted for non-linear batch effects and tried modeling them with neural networks. The … 12 Jan 2021 • JDAI-CV/faceX-Zoo • . The same direction of separation applies to the samples from the external dataset. For this dataset, we chose estrogen receptor (ER) and cancer grade as the biological variables of interest, since both are informative cancer traits. endobj We preprocessed both datasets by applying standard gene expression preprocessing steps: mapping probe ids to gene names, log transforming the values and making each gene zero-mean univariate. 5aii). On the other hand, the UMAP plot for AD-AE embedding shows that data points from different datasets are fused (Fig. Gene standardization: (Li and Wong, 2001) transforms each gene measurement to have zero mean and one standard deviation within a confounder class. Accordingly, we evaluate our model using two metrics: (i) how successfully the embedding can predict the confounder, where we expect a prediction performance close to random, and (ii) the quality of prediction of biologically relevant variables, where a better model is expected to lead to more accurate predictions. In this paper, we propose a method of dimension re-duction by manifold learning, which extends the tradition-al autoencoder to iteratively explore data relation and use the relation to pursue the manifold structure. ER is a binary label that denotes the existence of ERs in cancer cells, an important phenotype for determining treatment (Knight et al., 1977). The last layer had five hidden nodes corresponding to the number of confounder classes and softmax activation. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. Especially, when collected from a large cohort or multiple cohorts, expression profiles have, in addition to the true signal, variations in expression measures across samples as a result of (i) technical artifacts that are not relevant to biology, such as batch effects, (ii) out-of-interest biological variables, such as sex, age, medications and (iii) random noise. Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection Fanghua Ye, Chuan Chen, Zibin Zheng School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou, China,{chenchuan,zhzibin} ABSTRACT Community structure is … To achieve this goal, we propose a deep learning approach to learning deconfounded expression embeddings, which we call Adversarial Deconfounding AutoEncoder (AD-AE). Recommender system on the Movielens dataset using an Autoencoder and Tensorflow in Python. Our approach is significantly different since we focus on removing confounders from the latent space to learn deconfounded embeddings instead of trying to deconfound the reconstructed expression. Matching the aggregated posterior to the prior … (The Boolean Autoencoder) In this research paper the authors demonstrate that convolutional neural networks (CNN) trained for classification purposes can be used to extract … Whereas an undercomplete autoencoder will use the entire network for every observation, a sparse autoencoder will be forced to selectively activate regions of the network depending on the input data. 1 0 obj Moreover, different studies may collect information on different traits and even measure the same traits using different metrics (Haibe-Kains et al., 2013). with both labeled and unlabeled samples available. endobj AD-AE is a general model that can be used with any categorical or continuous valued confounder. We observed improvement in autoencoder performance when we applied clustering first and passed cluster centers to the model (e.g. This result shows that AD-AE much more successfully generalizes to other domains. endobj Outlier Detection with Autoencoder Ensembles Jinghui Chen Saket Sathe yCharu Aggarwal Deepak Turagay Abstract In this paper, we introduce autoencoder ensembles for unsupervised outlier detection. We trained AD-AE and the competitors using only four datasets, leaving the fifth dataset out. In this research, an effective deep learning method known as stacked autoencoders (SAEs) is proposed to solve gearbox fault diagnosis. << /S /GoTo /D (section.0.1) >> This simple example shows how confounder effects can prevent us from learning transferable latent models. We also subsampled from the subtype classes to carry the transfer experiments on the simulated balanced dataset and demonstrated that AD-AE could successfully transfer across domains in both cases of balanced and imbalanced class distributions (Supplementary Section S3 and Supplementary Fig. The gray dots denote samples with missing labels. Multiple studies aimed to generate fair representations that try to learn as much as possible from the data without learning the membership of a sample to sensitive categories (Louizos et al., 2015; Zemel et al., 2013). Figure 2b depicts the PC plot of the autoencoder embedding. These two networks compete against each other to learn the optimal embedding that encodes important signals without encoding the variation introduced by the selected confounder variable. Through this difficult time APS and the Physical Review editorial office are fully equipped and actively working to support researchers by continuing to carry out all editorial and peer-review functions and publish research in the journals as well as … Supplementary data are available at Bioinformatics online. (ii) The ability to extract patterns from the data without imposed directions or restrictions. The authors thankfully acknowledge all members of the AIMS lab for their helpful comments and useful discussions. For the sex confounder, the last layer had one hidden node with sigmoid activation, trained with binary cross entropy loss; for the age confounder, the last layer used linear activation, trained with mean squared loss. Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Sparse autoencoder 1 Introduction Supervised learning is one of the most powerful tools of AI, and has led to automatic zip code recognition, speech recognition, self-driving cars, and a continually improving understanding of the human genome. Therefore, AD-AE successfully learns manifolds that are valid across different domains, as we demonstrated for both ER and cancer grade predictions. We also propose a novel autoencoder based machine learning pipeline that can come up with … 16 0 obj Observe that ER- samples from the training set are concentrated on the upper left of the plot, while ER+ samples dominate the right. endobj For full access to this pdf, sign in to an existing account, or purchase an annual subscription. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. Note that we trained the model using samples in the four datasets only, and we then used the already trained model to encode the fifth dataset samples. Hindering the learning of meaningful representations is the fact that gene expression measurements often contain unwanted sources of variation, such as experimental artifacts and out-of-interest biological variables. et al. In this paper, we confront the above challenges by introducing Turbo Autoencoder (henceforth, TurboAE) – the first channel coding scheme with both encoder and decoder powered by neural networks that achieves reliability close to the state-of-the-art channel codes under AWGN channels for a moderate block length. We provide an in-depth review of recent advances in representation learning with a focus on autoencoder-based models. Though more general in scope, our article is relevant to batch effect correction techniques. Original language: English: Journal: International Journal of Artificial Intelligence and Machine … This tensor is fed to the encoder model as an input. orF content-based image retrieval, binary codes have many advan- tages compared with directly matching pixel intensities or matching real-valued codes. 13 0 obj The autoencoder tries to capture the strongest sources of variation to reconstruct the original input successfully. A simplified graphical model of measured expression shown as a mix of true signal, confounders of biological and non-biological origin and random noise. One advantage of Louppe’s model over the others is that it can work with any confounder variable, including continuous valued confounders. We can improve our model by adopting a regularized autoencoder such as denoising autoencoder (Vincent et al., 2008), or variational autoencoder (Kingma and Welling, 2013). The adversarial model was trained with categorical cross entropy loss. We trained the predictor model using only female samples and predicted for male samples. This might lead to discrepancies when transferring from one domain to another; however, AD-AE embeddings could be successfully transferred independent of the distribution of labels, a highly desirable property of a robust expression embedding. We then freeze the autoencoder model and train the adversary for an entire epoch to minimize  Equation 2. Step 1: The autoencoder model l is defined per Section 2.1. orF content-based image retrieval, binary codes have many advan-tages compared with directly matching pixel intensities or matching real-valued codes. Gene expression datasets contain valuable information central to unlocking biological mechanisms and understanding the biology of complex diseases. By interpreting a communications system as anautoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeksto jointly optimize transmitter and receiver components in a single process. 9 0 obj The AD-AE model consists of two neural networks: (i) an autoencoder to generate an embedding that can reconstruct original measurements, and (ii) an adversary trained to predict the confounder from that embedding. 4). However, Figure 6aii shows that when predicting for the left-out dataset, AD-AE clearly outperforms all other models. AD-AE trains two neural networks simultaneously, an autoencoder to generate an embedding that reconstructs the original data successfully and an adversary model that predicts the selected confounders from the generated embedding. We then apply an autoencoder (Hinton and Salakhutdinov, 2006) to this dataset, i.e. 6). endobj KMPlot expression validation reconstruction error of 0.624 for the all genes model compared to 0.522 for the 1000 cluster centers model). (2020) applied this approach to predict pneumonia from chest radiographs, showing that the model performs successfully without being confounded by selected variables. For the biological trait, we used cancer subtype label, a binary variable indicating whether a patient had LGG or GBM, the latter a particularly aggressive subtype of glioma. 8 0 obj Our selected model had one hidden layer in both encoder and decoder networks, with 500 hidden nodes and a dropout rate of 0.1. When we measure the Pearson’s correlation coefficient (Lin, 1989) between each node value and the binary dataset label, we observe that 78% of the embedding nodes are significantly correlated with the dataset label (P-value<0.01). Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models. LOCA is a special type of autoencoder, consisting of an encoder (E) parametrized by ρ and a decoder (D) parametrized by γ (see Section 5). It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. 25 0 obj They are very cheap to store, and they are very fast to compare using bit-wise operations. Ganin et al. 5bi). Without focusing on a specific phenotype prediction, these models enable us to learn patterns unconstrained by the limited phenotype labels we have. In this paper, we propose UCLData, which is a dataset containing detailed information of UEFA Champions League games played over the past six years. In this article, we tested our model on cancer expression datasets since cancer expression samples are available in large numbers. 4bii, iii), showing the effects of deconfounding. To leverage VAE in practical tasks which have high dimensions and huge dataset often face the problem of low variance evidence lower bounds construction... PDF Abstract … For clarity, the subplots for the training and external samples are provided below the joined plots. To whom correspondence should be addressed. An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. We implemented AD-AE using Keras with Tensorflow background. What are possible business applications? The architecture selected for brain cancer expression was very similar, with 500 k-means cluster centers, 50 latent nodes, one hidden layer with 500 nodes in both networks with no dropout, and ReLU activation at all layers except the last layers of the networks; the remaining parameters were the same as those for the breast cancer network. S4). (Introduction) While keeping these differences in mind, we can compare our approach to batch correction techniques to highlight the advantages of our adversarial confounder-removal framework. We trained our model and the baselines with the same procedure we applied to the breast cancer dataset and again fitted prediction models. Examples include mean-centering (Sims et al., 2008), gene-standardization (Li and Wong, 2001), ratio-based correction (Luo et al., 2010), distance-weighted discrimination (Benito et al., 2004) and probably the most popular of these techniques, the Empirical Bayes method (i.e. This work was supported by the National Institutes of Health [R35 GM 128638 and R01 NIA AG 061132] and National Science Foundation [CAREER DBI-1552309 and DBI-1759487] . Readers can go through the paper here. But why is it only almost as good? This is a great improvement in autoencoder architecture. OBJECT CLASSIFICATION USING STACKED AUTOENCODER AND CONVOLUTIONAL NEURAL NETWORK A Paper Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science By Vijaya Chander Rao Gottimukkula In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE Major Department: Computer Science November 2016 Fargo, North … Empirical Bayes method (ComBat): (Johnson et al., 2007) matches distributions of different batches by mean and deviation adjustment. Unlike prior work, AD-AE fits an adversary model on the embedding space to generate robust, confounder-free embeddings.