DeepAI AI Chat
Log In Sign Up

A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival

11/17/2016
by   Hamid Reza Hassanzadeh, et al.
Georgia Institute of Technology
0

Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalities of data derived from RNA deep-sequencing (RNA-seq) to predict survival of cancer patients. Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles. As such, analysis of transcriptomic data modalities calls for state-of-the-art big-data analytics techniques that can maximally use all the available data to discover the relevant information hidden within a significant amount of noise. In this paper, we propose a pipeline that predicts cancer patients' survival by exploiting the structure of the input (manifold learning) and by leveraging the unlabeled samples using Laplacian support vector machines, a graph-based semi supervised learning (GSSL) paradigm. We show that under certain circumstances, no single modality per se will result in the best accuracy and by fusing different models together via a stacked generalization strategy, we may boost the accuracy synergistically. We apply our approach to two cancer datasets and present promising results. We maintain that a similar pipeline can be used for predictive tasks where labeled samples are expensive to acquire.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/10/2020

Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks

Accurate survival prediction is crucial for development of precision can...
02/04/2017

Network-based methods for outcome prediction in the "sample space"

In this thesis we present the novel semi-supervised network-based algori...
04/24/2022

Colorectal cancer survival prediction using deep distribution based multiple-instance learning

Several deep learning algorithms have been developed to predict survival...
04/10/2017

Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer

The medical research facilitates to acquire a diverse type of data from ...
10/25/2022

Predicting Survival Outcomes in the Presence of Unlabeled Data

Many clinical studies require the follow-up of patients over time. This ...
04/28/2022

Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Genomics Data

Genomics data such as RNA gene expression, methylation and micro RNA exp...
06/22/2022

Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization

Cancer subtyping is crucial for understanding the nature of tumors and p...