View-aware collaborative learning for survival prediction and subgroup identification
Advances of high throughput experimental methods have led to the availability of more
diverse omic datasets in clinical analysis applications. Different types of omic data reveal …
diverse omic datasets in clinical analysis applications. Different types of omic data reveal …
Deep correlational learning for survival prediction from multi-modality data
Technological advances have created a great opportunity to provide multi-view data for
patients. However, due to the large discrepancy between different heterogeneous views …
patients. However, due to the large discrepancy between different heterogeneous views …
Cluster-boosted multi-task learning framework for survival analysis
L Wang, M Chignell, H Jiang… - 2020 IEEE 20th …, 2020 - ieeexplore.ieee.org
Accurately predicting the time to an event of interest is an important problem in a wide range
of real-world applications. However, prediction is often difficult because many medical …
of real-world applications. However, prediction is often difficult because many medical …
Deep integrative analysis for survival prediction
Survival prediction is very important in medical treatment. However, recent leading research
is challenged by two factors: 1) the datasets usually come with multi-modality; and 2) sample …
is challenged by two factors: 1) the datasets usually come with multi-modality; and 2) sample …
CAMR: cross-aligned multimodal representation learning for cancer survival prediction
Motivation Accurately predicting cancer survival is crucial for helping clinicians to plan
appropriate treatments, which largely improves the life quality of cancer patients and spares …
appropriate treatments, which largely improves the life quality of cancer patients and spares …
MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling
Precision medicine fundamentally aims to establish causality between dysregulated
biochemical mechanisms and cancer subtypes. Omics-based cancer subtyping has …
biochemical mechanisms and cancer subtypes. Omics-based cancer subtyping has …
WMLRR: A weighted multi-view low rank representation to identify cancer subtypes from multiple types of omics data
The identification of cancer subtypes is of great importance for understanding the
heterogeneity of tumors and providing patients with more accurate diagnoses and …
heterogeneity of tumors and providing patients with more accurate diagnoses and …
[HTML][HTML] A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction
Background With the rapid development of sequencing technologies, collecting diverse
types of cancer omics data become more cost-effective. Many computational methods …
types of cancer omics data become more cost-effective. Many computational methods …
Deep Survival Analysis with Latent Clustering and Contrastive Learning
Survival analysis is employed to analyze the time before the event of interest occurs, which
is broadly applied in many fields. The existence of censored data with incomplete …
is broadly applied in many fields. The existence of censored data with incomplete …
Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data
Z Zhao, Q Feng, Y Zhang, Z Ning - Briefings in Bioinformatics, 2023 - academic.oup.com
Biomedical multi-modality data (also named multi-omics data) refer to data that span
different types and derive from multiple sources in clinical practices (eg gene sequences …
different types and derive from multiple sources in clinical practices (eg gene sequences …