Classifying the multi-omics data of gastric cancer using a deep feature selection method

Hu, Yan‐Yu; Zhao, Liang; Li, Zhao; Dong, Xiangjun; Xu, Tiantian; Zhao, Yuhai

doi:10.1016/j.eswa.2022.116813

Cited by 29 publications

(11 citation statements)

References 27 publications

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“…The main challenge of this method is that the data is highly dimensional, noisy, heterogeneous, and has a small sample size, and there will be data loss during processing ( 91 , 96 , 188 , 189 ). Here are some methods to address these barriers: T-distributed stochastic neighborhood embedding, autoencoder, random forest deep feature selection, a stacked autoencoder, gradient descent method, multi-view factorization autoencoder, co-expression network analysis, and regulation techniques ( 88 , 91 , 114 , 190 – 193 ).…”

Section: Current Challenges and Future Prospectsmentioning

confidence: 99%

Artificial intelligence assists precision medicine in cancer treatment

Liao

et al. 2023

Front. Oncol.

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Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

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Section: Current Challenges and Future Prospectsmentioning

confidence: 99%

Artificial intelligence assists precision medicine in cancer treatment

Liao

et al. 2023

Front. Oncol.

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“…These methods primarily address the tasks of subtype clustering and prognostic analysis; that is, they do not require prior knowledge of sample phenotypes. With the availability of data sets containing detailed sample phenotype annotations on the rise, there is a growing interest in supervised approaches for integrating multiomics data, enabling accurate predictions on uncharacterized cases. , So far, supervised integration methods include: (1) early integration methods that concatenate matrices of different omics data types, such as RDFS, Stetson et al, and Fu et al, (2) intermediate integration methods that map diverse omics data into a shared space, such as MoGCN, and (3) late integration methods that combine predictions from different omics data types using ensemble learning, such as MOGONET and MOMA . Compared to other integration methods, early integration has become the most commonly used method , for the reasons that it preserves the attributes of biometric measurements and is easy to implement.…”

Section: Introductionmentioning

confidence: 99%

“…However, early integration encounters two primary challenges in its application: (1) The raw high-dimensional data generated by concatenating all omics data is intricate, noisy, and redundant, leading to challenging learning processes and suboptimal model performance. , Existing methods , often employ feature selection algorithms to reduce the complexity of the composite matrix, which results in information loss as certain useful information is filtered out during the selection process . (2) Another challenge lies in the fact that sequential high-dimensional multiomics vectors can hardly reflect the intrinsic correlations of omics-features from the representational level .…”

Section: Introductionmentioning

confidence: 99%

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

Zhang,

Mou,

et al. 2024

J. Chem. Inf. Model.

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In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.

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“…Using an autoencoder architecture, Chaudhary integrates multiomics data to predict hepatocellular carcinoma (HCC) survival. Hu developed a random forest deep feature selection (RDFS) and approach to increase gastric cancer prediction accuracy by combining the gene expression and copy number variation data [11]. Based on multiomics ensemble data, Xu employed a bidirectional deep neural network (BiDNN) model to predict the prognosis of gastric cancer [12].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways

Dai

et al. 2022

Journal of Oncology

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Background. Gastric cancer (GC) is one of the deadliest cancers in the world, with a 5-year overall survival rate of lower than 20% for patients with advanced GC. Genomic information is now frequently employed for precision cancer treatment due to the rapid advancements of high-throughput sequencing technologies. As a result, integrating multiomics data to construct predictive models for the GC patient prognosis is critical for tailored medical care. Results. In this study, we integrated multiomics data to design a biological pathway-based gastric cancer sparse deep neural network (GCS-Net) by modifying the P-NET model for long-term survival prediction of GC. The GCS-Net showed higher accuracy (accuracy = 0.844), area under the curve (AUC = 0.807), and F1 score (F1 = 0.913) than traditional machine learning models. Furthermore, the GCS-Net not only enables accurate patient survival prognosis but also provides model interpretability capabilities lacking in most traditional deep neural networks to describe the complex biological process of prognosis. The GCS-Net suggested the importance of genes (UBE2C, JAK2, RAD21, CEP250, NUP210, PTPN1, CDC27, NINL, NUP188, and PLK4) and biological pathways (Mitotic Anaphase, Resolution of Sister Chromatid Cohesion, and SUMO E3 ligases) to GC, which is consistent with the results revealed in biological- and medical-related studies of GC. Conclusion. The GCS-Net is an interpretable deep neural network built using biological pathway information whose structure represents a nonlinear hierarchical representation of genes and biological pathways. It can not only accurately predict the prognosis of GC patients but also suggest the importance of genes and biological pathways. The GCS-Net opens up new avenues for biological research and could be adapted for other cancer prediction and discovery activities as well.

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Classifying the multi-omics data of gastric cancer using a deep feature selection method

Cited by 29 publications

References 27 publications

Artificial intelligence assists precision medicine in cancer treatment

Artificial intelligence assists precision medicine in cancer treatment

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways

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