Ranking of cancer genes in Markov chain model through integration of heterogeneous sources of data

Abstract

Cancer is a disease driven largely by the accumulation of somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations had posed a challenge in modern cancer research. With the widespread use of microarray experiments and clinical studies, a large numbers of candidate cancer genes are produced and extracting informative genes out of them is essential. In our project we aim to find the informative genes for cancer by using mutation data from ovarian cancers. In our model we utilized the patient gene mutation profile, gene expression data and gene gene interactions network to build a graphical representation of genes and patients and construct Markov processes for mutation and patients separately. After this process, we can prioritize cancers genes automatically by looking into their scores at their stationary distributions. Comprehensive experiments show that the utilization of heterogeneous sources of information is very helpful in finding important cancer genes.

Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization

“…The research subjects of the former one mainly include load data, distributed power output, wind speed of wind turbine, and etc. These kinds of data integration technologies are relatively mature, and already form a relatively perfect research system, in which grey relational analysis, collaborative filtering, Markov chain, support vector machine and neural network are widely used as data analysis tools [9][10][11][12]. The research subjects of index data integration mainly include the structured and semi-structured data, in addition to time-series data.…”