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Deep Mining from Omics Data

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2449))

Abstract

Since the advent of high-throughput omics technologies, various molecular data such as genes, transcripts, proteins, and metabolites have been made widely available to researchers. This has afforded clinicians, bioinformaticians, statisticians, and data scientists the opportunity to apply their innovations in feature mining and predictive modeling to a rich data resource to develop a wide range of generalizable prediction models. What has become apparent over the last 10 years is that researchers have adopted deep neural networks (or “deep nets”) as their preferred paradigm of choice for complex data modeling due to the superiority of performance over more traditional statistical machine learning approaches, such as support vector machines. A key stumbling block, however, is that deep nets inherently lack transparency and are considered to be a “black box” approach. This naturally makes it very difficult for clinicians and other stakeholders to trust their deep learning models even though the model predictions appear to be highly accurate. In this chapter, we therefore provide a detailed summary of the deep net architectures typically used in omics research, together with a comprehensive summary of the notable “deep feature mining” techniques researchers have applied to open up this black box and provide some insights into the salient input features and why these models behave as they do. We group these techniques into the following three categories: (a) hidden layer visualization and interpretation; (b) input feature importance and impact evaluation; and (c) output layer gradient analysis. While we find that omics researchers have made some considerable gains in opening up the black box through interpretation of the hidden layer weights and node activations to identify salient input features, we highlight other approaches for omics researchers, such as employing deconvolutional network-based approaches and development of bespoke attribute impact measures to enable researchers to better understand the relationships between the input data and hidden layer representations formed and thus the output behavior of their deep nets.

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Notes

  1. 1.

    https://www.cancer.gov/

  2. 2.

    https://www.genome.gov/

  3. 3.

    https://www.mskcc.org/

  4. 4.

    We direct the reader to the recent survey and guidelines produced by Zhang et al. [25] for a more detailed systematic review of deep learning technologies (including available software tools), their application to omics data, and good practice guidelines.

  5. 5.

    As shown in Fig. 1a, an additional “bias” weight, b, associated with a single unit set to a constant value of 1, is assigned to each processing element within the hidden and output layers of an MLP. The bias weight allows the activation function to “move” horizontally across the net input space (x-axis), whereas the standard weights w1…wn transform the shape and direction of the activation function to enable it to pivot around the horizontal point, therefore allowing for more adaptive and effective learning [35].

  6. 6.

    The terms weights, weighted values, connections, and connection strengths will be used synonymously throughout this section.

  7. 7.

    The purpose of the hidden layers within an Auto-encoder is not to reproduce a precise copy of the higher dimensional input vector on the output layer. Rather it is meant to be a rough approximation (within an allowable error tolerance) that is less sensitive to variations within the training data, thereby filtering out noise and forcing the network to concentrate on covariances within the input data by nonlinear projections onto a lower dimensional space—akin to a form of nonlinear principal component analysis.

  8. 8.

    For example, training an MLP with back-propagation for 100,000 epochs to learn the MNIST handwritten digits benchmark data sets [47], consisting of 60,000 images for training and 10,000 for testing, would not be feasible on such multi-core serial processors due to insufficient memory and/or lengthy training times (months) caused by intractably slow processing [48].

  9. 9.

    The nVidia GTX580 GPU provides up to 512 cores and between 197.6 GLOPS and 1.581 TFLOPS floating point compute power.

  10. 10.

    Residual functions make stronger reference to layer inputs by allowing the architecture to include “jump connections” where the input from one layer can be provided as input to a hidden feature layer n layers ahead, where n > 1. This is represented in an equation as F(x) = H(x) − x, where H(x) represents the nonlinear hidden mapping we want to learn with input x through an array of stacked nonlinear hidden layers. H(x) is then reformulated as F(x) + x, where F(x) and x represent the stacked nonlinear layers and identity function, respectively.

  11. 11.

    Which is simply f(x) = x·sigmoid(x).

  12. 12.

    Hubel and Wiesel’s discovered that the cat’s visual system had locally sensitive, orientation-selective neurons.

  13. 13.

    Which could be a standard MLP hidden layer or a convolutional or pooling layer in a CNN.

  14. 14.

    A deconvolutional net or “deconvnet” uses the same filtering and pooling layers of a standard CNN but in reverse, so instead of mapping pixels to feature maps, it maps features to input pixels.

  15. 15.

    PCA is commonly used for dimensionality reduction by establishing a new set of variables, from the original set, that better captures the variation in the sample and can be viewed as rotations of the original variable set. There are fewer new variables than the original and these new variables are referred to as principal components and are uncorrelated and ordered by the fraction of the total information retained. A limitation is that there is an underlying linearity assumption of the relationship between the data and set of variables.

  16. 16.

    A logistic regression model establishes a relationship between a binary outcome or “response” variable and a group of feature or “predictor” variables. It models the logit-transformed probability as a linear relationship with the predictor variables—for more information please see [104, 105].

  17. 17.

    Actual probabilities can be recovered by simply exponentiating the log odds ratios.

  18. 18.

    Wrapper-based approaches train a machine learning model (e.g., Support Vector Machine, Logistic Regression, or Decision Tree) on a range of independent subsets of features and compare their predictive accuracy on a hold-out test set. The model’s error rate associated with the test set is the score given for the subset of features used. Given the iterative training procedure, the approach is computationally very intensive. Filter methods, on the other hand, use common statistical algorithms such as Pearson product-moment correlation coefficient and mutual information, to score each feature/class combination. Filter methods are much faster than wrapper-based approaches and can be used as a pre-cursor to the wrapper method to filter out irrelevant features before the iterative training process. A common such approach is Recursive Feature Elimination [110] where feature subsets are trained with Support Vector Machines and features with low weights are removed and the process repeated. See [103] for more information.

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Authors and Affiliations

  1. School of Science and Technology, Department of Computer Science, Nottingham Trent University, Nottingham, UK

    Abeer Alzubaidi

  2. Perceptronix Ltd, Hilton, Derbyshire, UK

    Jonathan Tepper

Authors
  1. Abeer Alzubaidi
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  2. Jonathan Tepper
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Correspondence to Abeer Alzubaidi .

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Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Oliviero Carugo

  2. Genome Institute and Bioinformatics Institute, Singapore, Singapore

    Frank Eisenhaber

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Alzubaidi, A., Tepper, J. (2022). Deep Mining from Omics Data. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_15

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  • DOI: https://doi.org/10.1007/978-1-0716-2095-3_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2094-6

  • Online ISBN: 978-1-0716-2095-3

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