ADAGE analysis of publicly available gene expression data collections illuminates<i>Pseudomonas aeruginosa-host</i>interactions

Tan, Jie; Hammond, John H.; Hogan, Deborah A.; Greene, Casey S.

doi:10.1101/030650

Cited by 8 publications

(5 citation statements)

References 68 publications

(62 reference statements)

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“…Dimension reduction methods like principal component analysis (PCA), diffusion maps or t-distributed stochastic neighbor embedding (tSNE) are commonly used to visualize the manifold for gene expression data 19,20 . A number of recent studies describe applications of autoencoders in genomics [21][22][23][24][25] . During denoising, the autoencoder learns the manifold and removes the noise by moving corrupted data points onto the manifold 26 (Fig.…”

Section: Introductionmentioning

confidence: 99%

Single cell RNA-seq denoising using a deep count autoencoder

Eraslan

Simon

Mircea

et al. 2018

Preprint

263

453

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Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNAseq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a zero-inflated negative binomial noise model, and nonlinear gene-gene or gene-dispersion interactions are captured. Our method scales linearly with the number of cells and can therefore be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.

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Section: Introductionmentioning

confidence: 99%

Single cell RNA-seq denoising using a deep count autoencoder

Eraslan

Simon

Mircea

et al. 2018

Preprint

263

453

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“…Feature Selection: The “curse of dimensionality” is a typical problem when using multimodal data (Tan et al, 2015 ; Nguyen et al, 2020 ). For example, the gene expression data and CNA data in METABRIC dataset contain 24,369 and 22,545 genes, respectively.…”

Section: Methodsmentioning

confidence: 99%

A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

et al. 2021

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Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.

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“…Authors in [28] present a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for diagnosing breast cancer from multidimensional data i.e., gene expression profile data and Copy Number Alteration (CNA) profile data. The small sample size or high dimensionality data may cause bad results [29]. Initially, they select effective features from gene expression profile data that include approximately 24,000 genes and CNA profile data that include approximately 26,000 genes using mRMR method [30].…”

Section: Literature Reviewmentioning

confidence: 99%

IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

et al. 2023

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The development of high throughput sequencing technologies i.e. Next Generation Sequencing (NGS) is revolutionizing the exploration of cancer. Though sequence datasets are highly complex, mutation can occur randomly in DNA or RNA sequences that can make cells sicker or less fit. The unusual growth and behavior of genes in cells cause cancer. Cancer-driver gene cells grow when mutation occurs. Identification of cancer driver genes is a critical and challenging issue for researchers. In the proposed work, initially, robust features are extracted from the sequence dataset through Position Relative Incidence Matrix (PRIM) integrated with Accumulative Absolute Position Incidence Vector (AAPIV) generation. PRIM and AAPIV convert the single-dimensional sequence data into 2-dimensional numeric data. Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) are used to train the model. The proposed model is validated with different validation methods i.e., independent testing, k-fold crossvalidation, self-consistency, and jackknife testing. The proposed model predicts whether the given primary structure corresponds to cancer driver genes or not. Results analyses show 95%, 92%, and 69% accuracy on RF, Artificial Neural Networks (ANN), and SVM respectively. The comparative analysis with existing state-of-the-art models i.e., 20/20+ and Multimodal Deep Neural Network by integrating Multi-dimensional Data (NDNNMD) shows that the proposed model outperforms the existing techniques.

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ADAGE analysis of publicly available gene expression data collections illuminatesPseudomonas aeruginosa-hostinteractions

Cited by 8 publications

References 68 publications

Single cell RNA-seq denoising using a deep count autoencoder

Single cell RNA-seq denoising using a deep count autoencoder

A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

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