New tools for the visualization of biological pathways

Ghosh, Tomojit; Ma, Xiaofeng; Kirby, Michael

doi:10.1016/j.ymeth.2017.09.006

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…Unlike autoencoder, which maps each point to itself, CE will map each point to its class centroid by minimizing the following cost function over the parameter set : The mapping f is composed of a dimension reducing mapping g (encoder) followed by a dimension increasing reconstruction mapping h (decoder). The output of the encoder is used as a supervised visualization tool 33 , and attaching another layer to map to the one-hot encoded labels and further training by fine-tuning provides a classifier. For further details, see 34 .…”

Section: Methodsmentioning

confidence: 99%

Using machine learning to determine the time of exposure to infection by a respiratory pathogen

Sharma

Aminian

Ghosh

et al. 2023

Sci Rep

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Given an infected host, estimating the time that has elapsed since initial exposure to the pathogen is an important problem in public health. In this paper we use longitudinal gene expression data from human challenge studies of viral respiratory illnesses for building predictive models to estimate the time elapsed since onset of respiratory infection. We apply sparsity driven machine learning to this time-stamped gene expression data to model the time of exposure by a pathogen and subsequent infection accompanied by the onset of the host immune response. These predictive models exploit the fact that the host gene expression profile evolves in time and its characteristic temporal signature can be effectively modeled using a small number of features. Predicting the time of exposure to infection to be in first 48 h after exposure produces BSR in the range of 80–90% on sequestered test data. A variety of machine learning experiments provide evidence that models developed on one virus can be used to predict exposure time for other viruses, e.g., H1N1, H3N2, and HRV. The interferon $$\alpha /\beta $$ α / β signaling pathway appears to play a central role in keeping time from onset of infection. Successful prediction of the time of exposure to a pathogen has potential ramifications for patient treatment and contact tracing.

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Section: Methodsmentioning

confidence: 99%

Using machine learning to determine the time of exposure to infection by a respiratory pathogen

Sharma

Aminian

Ghosh

et al. 2023

Sci Rep

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“…Centroid-encoder (CE) neural networks are the starting point of our approach [17,16,22]. We present a brief overview of CEs and demonstrate how they can be extended to perform non-linear feature selection.…”

Section: Sparse Centroid-encodermentioning

confidence: 99%

“…The mapping f is composed of a dimension reducing mapping g (encoder) followed by a dimension increasing reconstruction mapping h (decoder). The output of the encoder is used as a supervised visualization tool [17,16], and attaching another layer to map to the one-hot encoded labels performs robust classification [22].…”

Section: Centroid-encodermentioning

confidence: 99%

“…This paper proposes a new embedded variable selection approach called Sparse Centroid-Encoder (SCE) to extract features when class labels are available. Our method extends the Centroid-Encoder model [16,17], where we applied a l 1 penalty to a sparsity promoting layer between the input and the first hidden layer. We evaluate this Sparse Centroid-Encoder on diverse data sets and show that the selected features produce better generalization than other state-of-the-art techniques.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection

Ghosh¹,

Kirby²

2022

Preprint

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We develop a sparse optimization problem for the determination of the total set of features that discriminate two or more classes. This is a sparse implementation of the centroid-encoder for nonlinear data reduction and visualization called Sparse Centroid-Encoder (SCE). We also provide a feature selection framework that first ranks each feature by its occurrence, and the optimal number of features is chosen using a validation set. The algorithm is applied to a wide variety of data sets including, single-cell biological data, high dimensional infectious disease data, hyperspectral data, image data, and speech data. We compared our method to various state-of-the-art feature selection techniques, including two neural network-based models (DFS, and LassoNet), Sparse SVM, and Random Forest. We empirically showed that SCE features produced better classification accuracy on the unseen test data, often with fewer features.

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“…Perhaps the most interesting recent development for machine-learning-based interpretation of H-D data lies within the realm of neural-network based "deep learning" (LeCun et al, 2015;Zhou et al, 2017;Cao et al, 2018;Ghosh et al, 2018). Deep learning allows computational models, composed of multiple processing layers, to learn representations of data with multiple levels of abstraction.…”

Section: B Proteomic and Mass Spectrometric Analysismentioning

confidence: 99%

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

et al. 2019

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large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

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New tools for the visualization of biological pathways

Cited by 9 publications

References 21 publications

Using machine learning to determine the time of exposure to infection by a respiratory pathogen

Using machine learning to determine the time of exposure to infection by a respiratory pathogen

Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

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