Multi-view network embedding with node similarity ensemble

Yuan, Weiwei; He, Kangya; Shi, Chenyang; Guan, Donghai; Tian, Yuan; Al-Dhelaan, Abdullah; Al-Dhelaan, Mohammed

doi:10.1007/s11280-020-00799-7

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…The important contribution of this work is the introduction of external domain knowledge such as biological interaction networks to improve model generalizability and reduce the risk of overfitting. Another example of multi-omics integration through AE is Multiple Similarity Network Embedding (MSNE) [ 88 ]. MSNE integrates the multi-omics information by embedding the neighbor relations of samples defined by the random walk on multiple similarity networks.…”

Section: Ae Applications In Biological and Medical Contexts Beyond Rdmentioning

confidence: 99%

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Pratella

Saadi

Bannwarth

et al. 2021

IJMS

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Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.

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Section: Ae Applications In Biological and Medical Contexts Beyond Rdmentioning

confidence: 99%

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Pratella

Saadi

Bannwarth

et al. 2021

IJMS

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“…The rapid development of technologies such as network science and representation learning [5], [15]- [17] enables us to profile campus life accurately and effectively, which brings us an unprecedented opportunity for the detection of students' mental health [18]- [21]. Opportunities and challenges, however, coexist.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Educational Data Fusion for Students’ Mental Health Detection

et al. 2022

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Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially for university students who are not physically and mentally mature. Not all students with poor mental health are aware of their situation and actively seek help. Proactive detection of mental problems is a critical step in addressing this issue in this case. However, accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured multi-modal data generated by campus life. Against this background, we propose a detection framework for detecting students' mental health, named CASTLE (educational data fusion for mental health detection). Three parts are involved in this framework. First, we utilize representation learning to fuse social life, academic performance, and physical appearance. An algorithm, named MOON (multi-view social network embedding), is proposed to combine students' social life in a comprehensive way by fusing students' heterogeneous social relations effectively. Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue. Finally, a DNN (deep neural network) model is utilized for the final detection. The extensive results demonstrate the promising performance of the proposed methods in comparison to an extensive range of state-of-the-art baselines.

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