@article {712,
	title = {A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.},
	journal = {PLoS Comput Biol},
	volume = {17},
	year = {2021},
	month = {2021 02},
	pages = {e1008748},
	abstract = {<p>MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.</p>},
	keywords = {Algorithms, Cell Line, Tumor, Computational Biology, Databases, Factual, Gene Expression Profiling, Genomics, High-Throughput Nucleotide Sequencing, Humans, Models, Theoretical, mutation, RNA-seq, Signal Transduction, Software, Transcriptome, whole exome sequencing, Workflow},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1008748},
	author = {Garrido-Rodriguez, Mart{\'\i}n and L{\'o}pez-L{\'o}pez, Daniel and Ortuno, Francisco M and Pe{\~n}a-Chilet, Maria and Mu{\~n}oz, Eduardo and Calzado, Marco A and Dopazo, Joaquin}
}
@article {692,
	title = {Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.},
	journal = {IEEE J Biomed Health Inform},
	volume = {24},
	year = {2020},
	month = {2020 07},
	pages = {2119-2130},
	abstract = {<p>Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94\% and 80\%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.</p>},
	keywords = {Biomarkers, Tumor, Computational Biology, Diagnosis, Computer-Assisted, Gene Expression Profiling, Humans, Machine Learning, RNA-seq, Skin Neoplasms},
	issn = {2168-2208},
	doi = {10.1109/JBHI.2019.2953978},
	author = {Galvez, Juan M and Castillo-Secilla, Daniel and Herrera, Luis J and Valenzuela, Olga and Caba, Octavio and Prados, Jose C and Ortuno, Francisco M and Rojas, Ignacio}
}
