[HTML][HTML] Machine learning applications in cancer prognosis and prediction
Cancer has been characterized as a heterogeneous disease consisting of many different
subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in …
subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in …
Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment
process is long and very costly due to its high recurrence and mortality rates. Accurate early …
process is long and very costly due to its high recurrence and mortality rates. Accurate early …
A review of feature selection techniques in bioinformatics
Feature selection techniques have become an apparent need in many bioinformatics
applications. In addition to the large pool of techniques that have already been developed in …
applications. In addition to the large pool of techniques that have already been developed in …
Multimodal data fusion for cancer biomarker discovery with deep learning
Technological advances have made it possible to study a patient from multiple angles with
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …
A review of microarray datasets and applied feature selection methods
Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …
its high number of features and the small sample sizes. Feature selection has been soon …
Deep learning with multimodal representation for pancancer prognosis prediction
A Cheerla, O Gevaert - Bioinformatics, 2019 - academic.oup.com
Motivation Estimating the future course of patients with cancer lesions is invaluable to
physicians; however, current clinical methods fail to effectively use the vast amount of …
physicians; however, current clinical methods fail to effectively use the vast amount of …
Methods for the integration of multi-omics data: mathematical aspects
Background Methods for the integrative analysis of multi-omics data are required to draw a
more complete and accurate picture of the dynamics of molecular systems. The complexity …
more complete and accurate picture of the dynamics of molecular systems. The complexity …
A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data
Breast cancer is a highly aggressive type of cancer with very low median survival. Accurate
prognosis prediction of breast cancer can spare a significant number of patients from …
prognosis prediction of breast cancer can spare a significant number of patients from …
[PDF][PDF] Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation.
We present an algorithmic framework for learning local causal structure around target
variables of interest in the form of direct causes/effects and Markov blankets applicable to …
variables of interest in the form of direct causes/effects and Markov blankets applicable to …
Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach
OBJECTIVE: In the modern healthcare system, rapidly expanding costs/complexity, the
growing myriad of treatment options, and exploding information streams that often do not …
growing myriad of treatment options, and exploding information streams that often do not …