DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Sharma, Alok; Lysenko, Artem; Boroevich, Keith A.; Tsunoda, Tatsuhiko

doi:10.1038/s41598-023-29644-3

Cited by 22 publications

(16 citation statements)

References 72 publications

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“…All algorithms were assessed with default settings (13). The results for DeepInsight-3D and MOLI were obtained directly from the study by Sharma et al (22).…”

Section: Methodsmentioning

confidence: 99%

“…The second is MOLI, which employs a method based on analyzing tabular data. The best performance AUCs for these deep-learning algorithms with the same data set were obtained from a recent publication (22). In addition, we evaluated two unsupervised machine learning algorithms based on the feature selection strategies, non-negative matrix factorization (NMF) and integrative NMF (intNMF).…”

Section: Benchmarking Implementationmentioning

confidence: 99%

“…Additionally, they are currently limited to analyzing data from only one type of omics data set. A recent method, DeepInsight-3D, represents an innovative step forward in the realm of high-throughput multi-omics data analysis(22). Building upon its predecessor, DeepInsight extends its capabilities to handle multi-omics data more effectively.…”

Section: Introductionmentioning

confidence: 99%

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OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

Tang,

Prodduturi,

Thompson

et al. 2024

Preprint

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Background: The integration and interpretation of multi-omics data are crucial in elucidating the pathophysiological mechanisms of complex diseases. While Deep Neural Networks (DNNs) and Graph Neural Networks (GNNs) have revolutionized the analysis of such data in cancer and neurodegenerative diseases, there remains a critical need for improved multi-omics analytic methodologies. Methods: We developed OmicsFootPrint, a novel framework for transforming multi-omics data into two-dimensional circular images for each sample, enabling intuitive representation and analysis. Utilizing DNNs for analysis, this framework incorporates the SHapley Additive exPlanations (SHAP) algorithm for model interpretation. We benchmarked OmicsFootPrint performance with data sets from The Cancer Genome Atlas (TCGA); we classified subtypes of lung cancer and breast cancer, specifically the PAM50 subtype and histological classifications. Additionally, we applied OmicsFootPrint to predict drug response data in cancer cell lines. Results: Evaluation of lung cancer subtypes using TCGA data sets, OmicsFootPrint achieved an average Area Under Curve (AUC) of over 0.90 for distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) subtypes. Notably, the EfficientNetV2 model reached an AUC of 0.98±0.02. For the TCGA BRCA (breast cancer) PAM50 subtype classification, encompassing Luminal A, Luminal B, Basal, and HER2 subtypes (836 samples in total), the framework reported an average AUC of 0.83±0.07. Furthermore, the application of OmicsFootPrint to the BRCA data set for distinguishing between invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) showed an average AUC of 0.87±0.04 with four-omics datatypes. Additionally, in drug response studies, OmicsFootPrint outperformed published algorithms, achieving the highest average AUC of 0.74 across all data sets. OmicsFootPrint also demonstrated robust performance with reduced training sample sizes. Conclusions: OmicsFootPrint represents a significant advancement, offering a comprehensive, efficient, and interpretable framework for multi-omics data analysis. Its innovative approach in transforming extensive multi-omics data sets into compact, circular images not only reduces memory requirements but also enhances the interpretability of complex biological data. This approach facilitates personalized treatment strategies and enriches our understanding of disease mechanisms, marking a significant step forward in the field of multi-omics research.

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“…All algorithms were assessed with default settings (13). The results for DeepInsight-3D and MOLI were obtained directly from the study by Sharma et al (22).…”

Section: Methodsmentioning

confidence: 99%

Section: Benchmarking Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

Tang,

Prodduturi,

Thompson

et al. 2024

Preprint

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show abstract

“… Sharma et al (2023) proposed a novel approach based on deep learning to utilize three distinct forms of multi-omics data for the purpose of predicting the efficacy of anticancer treatments in individual patients. The proposed DeepInsight 3D technique utilizes organized data to transform data into visual representations.…”

Section: Survey Methodologymentioning

confidence: 99%

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Hajim,

Zainudin,

Mohd Daud

et al. 2024

PeerJ Computer Science

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Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL’s techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models’ generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

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“…AI also excels at clustering data into groupings that may not be obvious to the human eye-an ability that can prove vital in clinical settings. Using such classification-based approaches, AI-omics can accurately categorize diseases based on their omics profiles [83] and even predict individual treatment responses prior to drug administration [84]. The elucidation of biomolecular pathways with AI-integrated omics approaches provides a better understanding of human health and disease.…”

Section: Omicsmentioning

confidence: 99%

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Flynn,

Chang

2024

Diagnostics

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The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

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DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Cited by 22 publications

References 72 publications

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

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