Biological Random Walks: integrating heterogeneous data in disease gene prioritization

02/14/2020
by   Michele Gentili, et al.
7

This work proposes a unified framework to leverage biological information in network propagation-based gene prioritization algorithms. Preliminary results on breast cancer data show significant improvements over state-of-the-art baselines, such as the prioritization of genes that are not identified as potential candidates by interactome-based algorithms, but that appear to be involved in/or potentially related to breast cancer, according to a functional analysis based on recent literature.

READ FULL TEXT
research
11/10/2021

Biomarker Gene Identification for Breast Cancer Classification

BACKGROUND: Breast cancer has emerged as one of the most prevalent cance...
research
10/10/2017

An Extension of Deep Pathway Analysis: A Pathway Route Analysis Framework Incorporating Multi-dimensional Cancer Genomics Data

Recent breakthroughs in cancer research have come via the up-and-coming ...
research
02/24/2018

Correlating Cellular Features with Gene Expression using CCA

To understand the biology of cancer, joint analysis of multiple data mod...
research
08/02/2023

Evaluation of network-guided random forest for disease gene discovery

Gene network information is believed to be beneficial for disease module...
research
03/08/2023

Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level

Successful machine learning methods require a trade-off between memoriza...
research
05/08/2020

The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data Integration

Recent advances in biological research have seen the emergence of high-t...
research
05/04/2018

Mixture Envelope Model for Heterogeneous Genomics Data Analysis

Envelope model also known as multivariate regression model was proposed ...

Please sign up or login with your details

Forgot password? Click here to reset