scholarly journals Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

2015 ◽  
Vol 7 (8) ◽  
pp. 904-920 ◽  
Author(s):  
Ioannis N. Melas ◽  
Theodore Sakellaropoulos ◽  
Francesco Iorio ◽  
Leonidas G. Alexopoulos ◽  
Wei-Yin Loh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Linear Programming ◽  
Lung Injury ◽  
Prior Knowledge ◽  
Gene Expression Data ◽  
Integer Linear Programming ◽  
Expression Data ◽  
Drug Induced ◽  
Data Application ◽  
Toxic Drugs

An Integer Linear Programming (ILP) formulation is introduced to model the modes of action of lung toxic drugs based on gene expression data and prior knowledge of protein connectivity.

scholarly journals Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions

2019 ◽  
Vol 18 (3) ◽  
Author(s):  
Luis M. de Campos ◽  
Andrés Cano ◽  
Javier G. Castellano ◽  
Serafín Moral
Keyword(s):  
Gene Expression ◽  
Bayesian Networks ◽  
Prior Knowledge ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cellular Systems ◽  
Expression Data ◽  
Starting Point ◽  
Gene Regulatory

Abstract Gene Regulatory Networks (GRNs) are known as the most adequate instrument to provide a clear insight and understanding of the cellular systems. One of the most successful techniques to reconstruct GRNs using gene expression data is Bayesian networks (BN) which have proven to be an ideal approach for heterogeneous data integration in the learning process. Nevertheless, the incorporation of prior knowledge has been achieved by using prior beliefs or by using networks as a starting point in the search process. In this work, the utilization of different kinds of structural restrictions within algorithms for learning BNs from gene expression data is considered. These restrictions will codify prior knowledge, in such a way that a BN should satisfy them. Therefore, one aim of this work is to make a detailed review on the use of prior knowledge and gene expression data to inferring GRNs from BNs, but the major purpose in this paper is to research whether the structural learning algorithms for BNs from expression data can achieve better outcomes exploiting this prior knowledge with the use of structural restrictions. In the experimental study, it is shown that this new way to incorporate prior knowledge leads us to achieve better reverse-engineered networks.

scholarly journals Who is this gene and what does it do? A toolkit for munging transcriptomics data in python

2018 ◽  
Author(s):  
Charles K. Fisher ◽  
Aaron M. Smith ◽  
Jonathan R. Walsh
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Transcriptional Regulation ◽  
Prior Knowledge ◽  
Gene Expression Data ◽  
Healthy Tissue ◽  
Expression Data ◽  
Batch Effects ◽  
Expression Levels ◽  
Transcriptomics Data

AbstractTranscriptional regulation is extremely complicated. Unfortunately, so is working with transcriptional data. Genes can be referred to using a multitude of different identifiers and are assigned to an ever increasing number of categories. Gene expression data may be available in a variety of units (e.g, counts, RPKMs, TPMs). Batch effects dominate signal, but metadata may not be available. Most of the tools are written in R. Here, we introduce a library, genemunge, that makes it easier to work with transcriptional data in python. This includes translating between various types of gene names, accessing Gene Ontology (GO) information, obtaining expression levels of genes in healthy tissue, correcting for batch effects, and using prior knowledge to select sets of genes for further analysis. Code for genemunge is freely available on Github.

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