Interpretable deep learning for improving cancer patient survival based on personal transcriptomes

Sun, Bo; Chen, Liang

doi:10.1038/s41598-023-38429-7

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…[ 20 ] introduced an interpretable deep neural network that faithfully captures the underlying chemical mechanism of transcription factor binding to DNA. CancerIDP [ 21 ] is an interpretable neural network that predicts cancer patients’ survival based on drug prescriptions and personal transcriptomes. In these models, neurons are imbued with biological meaning (GO terms, pathways, etc.…”

Section: Introductionmentioning

confidence: 99%

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data

Yin,

Chen

2023

Briefings in Bioinformatics

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Identifying cell types is crucial for understanding the functional units of an organism. Machine learning has shown promising performance in identifying cell types, but many existing methods lack biological significance due to poor interpretability. However, it is of the utmost importance to understand what makes cells share the same function and form a specific cell type, motivating us to propose a biologically interpretable method. CellTICS prioritizes marker genes with cell-type-specific expression, using a hierarchy of biological pathways for neural network construction, and applying a multi-predictive-layer strategy to predict cell and sub-cell types. CellTICS usually outperforms existing methods in prediction accuracy. Moreover, CellTICS can reveal pathways that define a cell type or a cell type under specific physiological conditions, such as disease or aging. The nonlinear nature of neural networks enables us to identify many novel pathways. Interestingly, some of the pathways identified by CellTICS exhibit differential expression “variability” rather than differential expression across cell types, indicating that expression stochasticity within a pathway could be an important feature characteristic of a cell type. Overall, CellTICS provides a biologically interpretable method for identifying and characterizing cell types, shedding light on the underlying pathways that define cellular heterogeneity and its role in organismal function. CellTICS is available at https://github.com/qyyin0516/CellTICS.

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Section: Introductionmentioning

confidence: 99%

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data

Yin,

Chen

2023

Briefings in Bioinformatics

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From understanding diseases to drug design: can artificial intelligence bridge the gap?

Pushkaran,

Arabi

2024

Artif Intell Rev

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Artificial intelligence (AI) has emerged as a transformative technology with significant potential to revolutionize disease understanding and drug design in healthcare. AI serves as a remarkable accelerating tool that bridges the gap between understanding diseases and discovering drugs. Given its capacity in the analysis and interpretation of massive amounts of data, AI is tremendously boosting the power of predictions with impressive accuracies. This allowed AI to pave the way for advancing all key stages of drug development, with the advantage of expediting the drug discovery process and curbing its costs. This is a comprehensive review of the recent advances in AI and its applications in drug discovery and development, starting with disease identification and spanning through the various stages involved in the drug discovery pipeline, including target identification, screening, lead discovery, and clinical trials. In addition, this review discusses the challenges that arise during the implementation of AI at each stage of the discovery process and provides insights into the future prospects of this field.

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RCoxNet: deep learning framework for enhanced cancer survival prediction integrating random walk with restart with mutation and clinical data

Kumari,

Gujral,

Panda

et al. 2024

Preprint

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Cancer poses a significant global health challenge, characterized by a complex disease progression and disrupted growth regulation. A thorough understanding of cellular and molecular biological mechanisms is essential for developing novel treatments and improving the accuracy of patient survival predictions. While prior studies have leveraged gene expression and clinical data to forecast survival outcomes through current machine learning and deep learning approaches, gene mutation data despite being a widely recognized metric has rarely been incorporated due to its limited information, inadequate representation of gene relationships, and data sparsity, which negatively affects the robustness, effectiveness, and interpretability of current survival analysis approaches. To overcome the challenges of mutation data sparsity, we propose RCoxNet, a novel deep learning neural network framework that integrates the Random Walk with Restart (RWR) algorithm with a deep learning Cox Proportional Hazards model. By applying this framework to mutation data from cBioportal, our model achieved an average concordance index of 0.62+-0.05 across four cancer types, outperforming existing deep neural network models. Additionally, we identified clinical features critical for differentiating between predicted high- and low-risk patients, with the relevance of these features being partially supported by previous studies.

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Interpretable deep learning for improving cancer patient survival based on personal transcriptomes

Cited by 3 publications

References 37 publications

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data

From understanding diseases to drug design: can artificial intelligence bridge the gap?

RCoxNet: deep learning framework for enhanced cancer survival prediction integrating random walk with restart with mutation and clinical data

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