scholarly journals DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

2021 ◽  
Author(s):  
Christos Fotis ◽  
George Alevizos ◽  
Nikolaos Meimetis ◽  
Christina Koleri ◽  
Thomas Gkekas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Mechanism Of Action ◽  
Biological System ◽  
Expression Profiles ◽  
Complex Structure ◽  
Gene Expression Profiles ◽  
Signaling Network ◽  
High Dimensional ◽  
Signaling Networks

The analysis and comparison of compounds' transcriptomic signatures can help elucidate a compound's Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compound's effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs. We created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds' gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA.

scholarly journals Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

2015 ◽  
Vol 11 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Aakash Chavan Ravindranath ◽  
Nolen Perualila-Tan ◽  
Adetayo Kasim ◽  
Georgios Drakakis ◽  
Sonia Liggi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ligand Binding ◽  
Gene Expression Data ◽  
Mechanism Of Action ◽  
In Silico ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Connectivity Map ◽  
Action Analysis

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding.

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations

2019 ◽  
Vol 40 (5) ◽  
pp. 624-632
Author(s):  
Ji-Wei Chang ◽  
Yuduan Ding ◽  
Muhammad Tahir ul Qamar ◽  
Yin Shen ◽  
Junxiang Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Protein Expression ◽  
Protein Interaction ◽  
Targeted Therapies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Related Proteins ◽  
Deep Learning Model

Abstract Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein–protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.

Reconstructing and analysing cellular states, space and time from gene expression profiles of many cells and single cells

2015 ◽  
Vol 11 (10) ◽  
pp. 2690-2698 ◽  
Author(s):  
Mirko Francesconi ◽  
Ben Lehner
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Expression Profiling ◽  
Biological Sample ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
High Dimensional ◽  
Dimensional Measurement ◽  
P Gene

Gene expression profiling is a fast, cheap and standardised analysis that provides a high dimensional measurement of the state of a biological sample, including of single cells. Computational methods to reconstruct the composition of samples and spatial and temporal information from expression profiles are described, as well as how they can be used to describe the effects of genetic variation.

scholarly journals Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shengqiao Gao ◽  
Lu Han ◽  
Dan Luo ◽  
Gang Liu ◽  
Zhiyong Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Large Scale ◽  
Prediction Models ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Genetic Profile ◽  
Drug Induced ◽  
Drug Mechanism

Abstract Background Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach. Results In this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR) was built to help modeling and predicting MOAs easily via deep learning. The users can use GPAR to customize their training sets to train self-defined MOA prediction models, to evaluate the model performances and to make further predictions automatically. Cross-validation tests show GPAR outperforms Gene set enrichment analysis in predicting MOAs. Conclusion GPAR can serve as a better approach in MOAs prediction, which may facilitate researchers to generate more reliable MOA hypothesis.

scholarly journals Composite measurements and molecular compressed sensing for highly efficient transcriptomics

2017 ◽  
Author(s):  
Brian Cleary ◽  
Le Cong ◽  
Eric S. Lander ◽  
Aviv Regev
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Training Data ◽  
Modular Structure ◽  
High Dimensional ◽  
Rna Profiling ◽  
Massive Scale ◽  
Random Composite ◽  
Modular Structures

AbstractRNA profiling is an excellent phenotype of cellular responses and tissue states, but can be costly to generate at the massive scale required for studies of regulatory circuits, genetic states or perturbation screens. Here, we draw on a series of advances over the last decade in the field of mathematics to establish a rigorous link between biological structure, data compressibility, and efficient data acquisition. We propose that very few random composite measurements – in which gene abundances are combined in a random linear combination – are needed to approximate the high-dimensional similarity between any pair of gene abundance profiles. We then show how finding latent, sparse representations of gene expression data would enable us to “decompress” a small number of random composite measurements and recover high-dimensional gene expression levels that were not measured (unobserved). We present a new algorithm for finding sparse, modular structure, which improves the ability to interpret samples in terms of small numbers of active modules, and show that the modular structure we find is sufficient to recover gene expression profiles from composite measurements (with ~100-fold fewer composite measurements than genes). Moreover, the knowledge that sparse, modular structures exist allows us to recover expression profiles from composite measurements, even without access to any training data. Finally, we present a proof-of-concept experiment for making composite measurements in the laboratory, involving the measurement of linear combinations of RNA abundances. Altogether, our results suggest new compressive modalities in experimental biology that can form a foundation for massive scaling in high-throughput measurements, while also offering new insights into the interpretation of high-dimensional data.

scholarly journals DeepSide: A Deep Learning Framework for Drug Side Effect Prediction

2019 ◽  
Author(s):  
Onur Can Uner ◽  
Ramazan Gokberk Cinbis ◽  
Oznur Tastan ◽  
A. Ercument Cicek
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Side Effect ◽  
State Of The Art ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Ground Truth ◽  
The State ◽  
Specific Information ◽  
Context Specific

AbstractDrug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features. The state-of-the-art approach that aims at using context specific information relies on only the high-quality experiments in LINCS L1000 and discards a large portion of the experiments. In this study, our goal is to boost the prediction performance by utilizing this data to its full extent. We experiment with 5 deep learning architectures. We find that a multi-modal architecture produces the best predictive performance among multi-layer perceptron-based architectures when drug chemical structure (CS), and the full set of drug perturbed gene expression profiles (GEX) are used as modalities. Overall, we observe that the CS is more informative than the GEX. A convolutional neural network-based model that uses only SMILES string representation of the drugs achieves the best results and provides 13.0% macro-AUC and 3.1% micro-AUC improvements over the state-of-the-art. We also show that the model is able to predict side effect-drug pairs that are reported in the literature but was missing in the ground truth side effect dataset. DeepSide is available at http://github.com/OnurUner/DeepSide.

scholarly journals Item response theory modeling for microarray gene expression data

2009 ◽  
Vol 6 (1) ◽  
Author(s):  
Andrej Kastrin
Keyword(s):  
Gene Expression ◽  
Item Response ◽  
Microarray Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
High Dimensional ◽  
Variable Model ◽  
Cluster Partition

The high dimensionality of global gene expression profiles, where number of variables (genes) is very large compared to the number of observations (samples), presents challenges that affect generalizability and applicability of microarray analysis. Latent variable modeling offers a promising approach to deal with high-dimensional microarray data. The latent variable model is based on a few latent variables that capture most of the gene expression information. Here, we describe how to accomplish a reduction in dimension by a latent variable methodology, which can greatly reduce the number of features used to characterize microarray data. We propose a general latent variable framework for prediction of predefined classes of samples using gene expression profiles from microarray experiments. The framework consists of (i) selection of smaller number of genes that are most differentially expressed between samples, (ii) dimension reduction using hierarchical clustering, where each cluster partition is identified as latent variable, (iii) discretization of gene expression matrix, (iv) fitting the Rasch item response model for genes in each cluster partition to estimate the expression of latent variable, and (v) construction of prediction model with latent variables as covariates to study the relationship between latent variables and phenotype. Two different microarray data sets are used to illustrate a general framework of the approach. We show that the predictive performance of our method is comparable to the current best approach based on an all-gene space. The method is general and can be applied to the other high-dimensional data problems.

scholarly journals Predicting compound activity from phenotypic profiles and chemical structures

2020 ◽  
Author(s):  
Tim Becker ◽  
Kevin Yang ◽  
Juan C Caicedo ◽  
Bridget K Wagner ◽  
Vlado C Dancik ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Drug Discovery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Relative Strength ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Chemical Structures ◽  
Wide Range

Recent advances in deep learning enable using chemical structures and phenotypic profiles to accurately predict assay results for compounds virtually, reducing the time and cost of screens in the drug discovery process. The relative strength of high-throughput data sources - chemical structures, images (Cell Painting), and gene expression profiles (L1000) - has been unknown. Here we compare their ability to predict the activity of compounds structurally different from those used in training, using a sparse dataset of 16,979 chemicals tested in 376 assays for a total of 542,648 readouts. Deep learning-based feature extraction from chemical structures provided a remarkable ability to predict assay activity for structures dissimilar to those used for training. Image-based profiling performed even better, but requires wet lab experimentation. It outperformed gene expression profiling, and at lower cost. Furthermore, the three profiling modalities are complementary, and together can predict a wide range of diverse bioactivity, including cell-based and biochemical assays. Our study shows that, for many assays, predicting compound activity from phenotypic profiles and chemical structures is an accurate and efficient way to identify potential treatments in the early stages of the drug discovery process.

scholarly journals Estimation of total mediation effect for high-dimensional omics mediators

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianzhong Yang ◽  
Jingbo Niu ◽  
Han Chen ◽  
Peng Wei
Keyword(s):  
Gene Expression ◽  
Mixed Model ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Estimation Procedure ◽  
Mediation Effect ◽  
High Dimensional ◽  
Model Framework ◽  
Intermediate Phenotypes ◽  
Age Related

Abstract Background Environmental exposures can regulate intermediate molecular phenotypes, such as gene expression, by different mechanisms and thereby lead to various health outcomes. It is of significant scientific interest to unravel the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposure and traits. Mediation analysis is an important tool for investigating such relationships. However, it has mainly focused on low-dimensional settings, and there is a lack of a good measure of the total mediation effect. Here, we extend an R-squared (R$$^2$$ 2 ) effect size measure, originally proposed in the single-mediator setting, to the moderate- and high-dimensional mediator settings in the mixed model framework. Results Based on extensive simulations, we compare our measure and estimation procedure with several frequently used mediation measures, including product, proportion, and ratio measures. Our R$$^2$$ 2 -based second-moment measure has small bias and variance under the correctly specified model. To mitigate potential bias induced by non-mediators, we examine two variable selection procedures, i.e., iterative sure independence screening and false discovery rate control, to exclude the non-mediators. We establish the consistency of the proposed estimation procedures and introduce a resampling-based confidence interval. By applying the proposed estimation procedure, we found that 38% of the age-related variations in systolic blood pressure can be explained by gene expression profiles in the Framingham Heart Study of 1711 individuals. An R package “RsqMed” is available on CRAN. Conclusion R-squared (R$$^2$$ 2 ) is an effective and efficient measure for total mediation effect especially under high-dimensional setting.

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