Publicaciones

@article{Martorell-Marugán2024,

title = {Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics},

author = {Jordi Martorell-Marugán and Raúl López-Domínguez and Juan Antonio Villatoro-García and Daniel Toro-Domínguez and Marco Chierici and Giuseppe Jurman and Pedro Carmona-Sáez},

doi = {10.1093/bib/bbae673},

issn = {1477-4054},

year  = {2024},

date = {2024-11-22},

volume = {26},

number = {1},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications. However, standard classification schemas are often not suitable for scRNA-Seq due to their unique characteristics, and new algorithms are required to effectively analyze and classify samples at the single-cell level. Furthermore, existing methods for this purpose have limitations in their usability. Those reasons motivated us to develop singleDeep, an end-to-end pipeline that streamlines the analysis of scRNA-Seq data training deep neural networks, enabling robust prediction and characterization of sample phenotypes. We used singleDeep to make predictions on scRNA-Seq datasets from different conditions, including systemic lupus erythematosus, Alzheimer’s disease and coronavirus disease 2019. Our results demonstrate strong diagnostic performance, validated both internally and externally. Moreover, singleDeep outperformed traditional machine learning methods and alternative single-cell approaches. In addition to prediction accuracy, singleDeep provides valuable insights into cell types and gene importance estimation for phenotypic characterization. This functionality provided additional and valuable information in our use cases. For instance, we corroborated that some interferon signature genes are consistently relevant for autoimmunity across all immune cell types in lupus. On the other hand, we discovered that genes linked to dementia have relevant roles in specific brain cell populations, such as APOE in astrocytes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Díaz-Francés2024,

title = {Semi-Supervised Semantic Image Segmentation by Deep Diffusion Models and Generative Adversarial Networks},

author = {José Ángel Díaz-Francés and José David Fernández-Rodríguez and Karl Thurnhofer-Hemsi and Ezequiel López-Rubio},

doi = {10.1142/s0129065724500576},

issn = {1793-6462},

year  = {2024},

date = {2024-11-00},

journal = {Int. J. Neur. Syst.},

volume = {34},

number = {11},

publisher = {World Scientific Pub Co Pte Ltd},

abstract = { Typically, deep learning models for image segmentation tasks are trained using large datasets of images annotated at the pixel level, which can be expensive and highly time-consuming. A way to reduce the amount of annotated images required for training is to adopt a semi-supervised approach. In this regard, generative deep learning models, concretely Generative Adversarial Networks (GANs), have been adapted to semi-supervised training of segmentation tasks. This work proposes MaskGDM, a deep learning architecture combining some ideas from EditGAN, a GAN that jointly models images and their segmentations, together with a generative diffusion model. With careful integration, we find that using a generative diffusion model can improve EditGAN performance results in multiple segmentation datasets, both multi-class and with binary labels. According to the quantitative results obtained, the proposed model improves multi-class image segmentation when compared to the EditGAN and DatasetGAN models, respectively, by [Formula: see text] and [Formula: see text]. Moreover, using the ISIC dataset, our proposal improves the results from other models by up to [Formula: see text] for the binary image segmentation approach. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Essarroukh2024,

title = {Convective Heat Transfer in Uniformly Accelerated and Decelerated Turbulent Pipe Flows},

author = {Ismael Essarroukh and José M. López},

doi = {10.3390/math12223560},

issn = {2227-7390},

year  = {2024},

date = {2024-11-00},

journal = {Mathematics},

volume = {12},

number = {22},

publisher = {MDPI AG},

abstract = {This study presents a detailed investigation of the temporal evolution of the Nusselt number (Nu) in uniformly accelerated and decelerated turbulent pipe flows under a constant heat flux using direct numerical simulations. The influence of different acceleration and deceleration rates on heat transfer is systematically studied, addressing a gap in the previous research. The simulations confirm several key experimental findings, including the presence of three distinct phases in the Nusselt number temporal response—delay, recovery, and quasi-steady phases—as well as the characteristics of thermal structures in unsteady pipe flow. In accelerated flows, the delay in the turbulence response to changes in velocity results in reduced heat transfer, with average Nu values up to 48% lower than those for steady-flow conditions at the same mean Reynolds number. Conversely, decelerated flows exhibit enhanced heat transfer, with average Nu exceeding steady values by up to 42% due to the onset of secondary instabilities that amplify turbulence. To characterize the Nu response across the full range of acceleration and deceleration rates, a new model based on a hyperbolic tangent function is proposed, which provides a more accurate description of the heat transfer response than previous models. The results suggest the potential to design unsteady periodic cycles, combining slow acceleration and rapid deceleration, to enhance heat transfer compared to steady flows.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Atencia-Jiménez2024,

title = {Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images},

author = {Ignacio Atencia-Jiménez and Adayabalam S. Balajee and Miguel J. Ruiz-Gómez and Francisco Sendra-Portero and Alegría Montoro and Miguel A. Molina-Cabello},

doi = {10.3390/app142210440},

issn = {2076-3417},

year  = {2024},

date = {2024-11-00},

journal = {Applied Sciences},

volume = {14},

number = {22},

publisher = {MDPI AG},

abstract = {The Dicentric Chromosome Assay (DCA) is widely used in biological dosimetry, where the number of dicentric chromosomes induced by ionizing radiation (IR) exposure is quantified to estimate the absorbed radiation dose an individual has received. Dicentric chromosome scoring is a laborious and time-consuming process which is performed manually in most cytogenetic biodosimetry laboratories. Further, dicentric chromosome scoring constitutes a bottleneck when several hundreds of samples need to be analyzed for dose estimation in the aftermath of large-scale radiological/nuclear incident(s). Recently, much interest has focused on automating dicentric chromosome scoring using Artificial Intelligence (AI) tools to reduce analysis time and improve the accuracy of dicentric chromosome detection. Our study aims to detect dicentric chromosomes in metaphase plate images using an ensemble of artificial neural network detectors suitable for datasets that present a low number of samples (in this work, only 50 images). In our approach, the input image is first processed by several operators, each producing a transformed image. Then, each transformed image is transferred to a specific detector trained with a training set processed by the same operator that transformed the image. Following this, the detectors provide their predictions about the detected chromosomes. Finally, all predictions are combined using a consensus function. Regarding the operators used, images were binarized separately applying Otsu and Spline techniques, while morphological opening and closing filters with different sizes were used to eliminate noise, isolate specific components, and enhance the structures of interest (chromosomes) within the image. Consensus-based decisions are typically more precise than those made by individual networks, as the consensus method can rectify certain misclassifications, assuming that individual network results are correct. The results indicate that our methodology worked satisfactorily in detecting a majority of chromosomes, with remarkable classification performance even with the low number of training samples utilized. AI-based dicentric chromosome detection will be beneficial for a rapid triage by improving the detection of dicentric chromosomes and thereby the dose prediction accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Domínguez2024,

title = {The Cooperative Maximal Covering Location Problem with ordered partial attractions},

author = {Concepción Domínguez and Ricardo Gázquez and Juan Miguel Morales and Salvador Pineda},

doi = {10.1016/j.cor.2024.106782},

issn = {0305-0548},

year  = {2024},

date = {2024-10-00},

journal = {Computers & Operations Research},

volume = {170},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Torres-Salomón2024,

title = {Exploring the Prognostic Impact of Non-Obstructive Coronary Artery Lesions through Machine Learning},

author = {Pablo Torres-Salomón and Jorge Rodríguez-Capitán and Miguel A. Molina-Cabello and Karl Thurnhofer-Hemsi and Francesco Costa and Pedro L. Sánchez-Fernández and Mario Antonio Muñoz-Muñoz and Ada del Mar Carmona-Segovia and Miguel Romero-Cuevas and Francisco Javier Pavón-Morón and Manuel Jiménez-Navarro},

doi = {10.3390/app14199079},

issn = {2076-3417},

year  = {2024},

date = {2024-10-00},

journal = {Applied Sciences},

volume = {14},

number = {19},

publisher = {MDPI AG},

abstract = {The prognostic impact of non-obstructive coronary artery disease (CAD) remains controversial. Therefore, the objective of this study is to assess the long-term prognostic significance of non-obstructive CAD using machine learning models. We designed a multicenter retrospective, longitudinal, and observational study that included 3265 patients classified into three groups: 1426 patients with lesions < 20%, 643 patients with non-obstructive CAD (lesions 20–50%), and 1196 patients with obstructive CAD (lesions > 70%). A composite cardiovascular event (acute myocardial infarction, stroke, hospitalization due to heart failure, or cardiovascular-related death) was assessed after a mean follow-up of 43 months. To achieve this, various machine learning models were constructed. The model with the highest accuracy was selected to perform a Shapley Additive Explanations (SHAP) analysis, revealing the contribution of different variables in predicting an event. The SHAP analysis suggested that the percentage of coronary lesion was the most significant predictor of cardiovascular events. None of the models demonstrated adequate capability in predicting the event, showing only a good predictive ability for the absence of an endpoint. In conclusions, this study demonstrates how machine learning techniques may facilitate the development of high-performing models for predicting long-term cardiovascular events in patients undergoing coronary angiography.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pineda2024,

title = {Tight big-Ms for Optimal Transmission Switching},

author = {Salvador Pineda and Juan Miguel Morales and Álvaro Porras and Concepción Domínguez},

doi = {10.1016/j.epsr.2024.110620},

issn = {0378-7796},

year  = {2024},

date = {2024-09-00},

journal = {Electric Power Systems Research},

volume = {234},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paredes2024,

title = {Revenue stacking of BESSs in wholesale and aFRR markets with delivery guarantees},

author = {Ángel Paredes and José A. Aguado},

doi = {10.1016/j.epsr.2024.110633},

issn = {0378-7796},

year  = {2024},

date = {2024-09-00},

journal = {Electric Power Systems Research},

volume = {234},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Parra-Cadenas2024,

title = {Zinc amidinate-catalysed cyclization reaction of carbodiimides and alkynes. An insight into the mechanism},

author = {Blanca Parra-Cadenas and Carlos Ginés and Daniel García-Vivó and David Elorriaga and Fernando Carrillo-Hermosilla},

doi = {10.1039/d4dt01265h},

issn = {1477-9234},

year  = {2024},

date = {2024-06-17},

journal = {Dalton Trans.},

volume = {53},

number = {24},

pages = {10050--10054},

publisher = {Royal Society of Chemistry (RSC)},

abstract = {A novel method for synthesizing iminopyridines using zinc has been established. The proposed mechanism highlights the crucial role of zinc amidinato intermediates in this synthesis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ghasemi2024,

title = {Pyrene Functionalized Norbornadiene‐Quadricyclane Fluorescent Photoswitches: Characterization of their Spectral Properties and Application in Imaging of Amyloid Beta Plaques},

author = {Shima Ghasemi and Monika Shamsabadi and Axel Olesund and Francisco Najera and Andreas Erbs Hillers‐Bendtsen and Fredrik Edhborg and Adil S. Aslam and Wera Larsson and Zhihang Wang and Francoise M. Amombo Noa and Rebecca Jane Salthouse and Lars Öhrström and Helen Hölzel and E. Perez‐Inestrosa and Kurt V. Mikkelsen and Jörg Hanrieder and Bo Albinsson and Ambra Dreos and Kasper Moth‐Poulsen},

doi = {10.1002/chem.202400322},

issn = {1521-3765},

year  = {2024},

date = {2024-06-17},

journal = {Chemistry A European J},

volume = {30},

number = {34},

publisher = {Wiley},

abstract = {AbstractThis study presents the synthesis and characterization of two fluorescent norbornadiene (NBD) photoswitches, each incorporating two conjugated pyrene units. Expanding on the limited repertoire of reported photoswitchable fluorescent NBDs, we explore their properties with a focus on applications in bioimaging of amyloid beta (Aβ) plaques. While the fluorescence emission of the NBD decreases upon photoisomerization, aligning with what has been previously reported, for the first time we observed luminescence after irradiation of the quadricyclane (QC) isomer. We deduce how the observed emission is induced by photoisomerization to the excited state of the parent isomer (NBD) which is then the emitting species. Thorough characterizations including NMR, UV‐Vis, fluorescence, X‐ray structural analysis and density functional theory (DFT) calculations provide a comprehensive understanding of these systems. Notably, one NBD‐QC system exhibits exceptional durability. Additionally, these molecules serve as effective fluorescent stains targeting Aβ plaques in situ, with observed NBD/QC switching within the plaques. Molecular docking simulations explore NBD interactions with amyloid, unveiling novel binding modes. These insights mark a crucial advancement in the comprehension and design of future photochromic NBDs for bioimaging applications and beyond, emphasizing their potential in studying and addressing protein aggregates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pérez-Ramos2024,

title = {Coordination of azol(in)ium dithiocarboxylate ligands to Au(iii): unexpected formation of a novel family of cyclometallated Au(iii) complexes, DFT calculations and catalytic studies},

author = {Paula Pérez-Ramos and María A. Mateo and David Elorriaga and Daniel García-Vivó and Raquel G. Soengas and Humberto Rodríguez-Solla},

doi = {10.1039/d4dt01184h},

issn = {1477-9234},

year  = {2024},

date = {2024-06-04},

journal = {Dalton Trans.},

volume = {53},

number = {22},

pages = {9433--9440},

publisher = {Royal Society of Chemistry (RSC)},

abstract = {We describe the preparation and characterization of novel Au(iii) cyclometallated κ1S-azoli(ni)um-2-dithiocarboxylatecomplexes and their rearrangement to Au(iii) biscyclometallated κ2-S,S′-azoli(ni)um-2-(methoxy)methanedithiolate complexes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Casimiro-Soriguer2024,

title = {Transcriptome Analysis of Stigmas of Vicia faba L. Flowers},

author = {Inés Casimiro-Soriguer and David Aguilar-Benitez and Natalia Gutierrez and Ana M. Torres},

doi = {10.3390/plants13111443},

issn = {2223-7747},

year  = {2024},

date = {2024-06-00},

journal = {Plants},

volume = {13},

number = {11},

publisher = {MDPI AG},

abstract = {Pollination in angiosperms depends on complex communication between pollen grains and stigmas, classified as wet or dry, depending on the presence or absence of secretions at the stigma surface, respectively. In species with wet stigma, the cuticle is disrupted and the presence of exudates is indicative of their receptivity. Most stigma studies are focused on a few species and families, many of them with self-incompatibility systems. However, there is scarce knowledge about the stigma composition in Fabaceae, the third angiosperm family, whose stigmas have been classified as semidry. Here we report the first transcriptome profiling and DEGs of Vicia faba L. styles and stigmas from autofertile (flowers able to self-fertilize in the absence of manipulation, whose exudate is released spontaneously) and autosterile (flowers that need to be manipulated to break the cuticle and release the exudates to be receptive) inbred lines. From the 76,269 contigs obtained from the de novo assembly, only 45.1% of the sequences were annotated with at least one GO term. A total of 115,920, 75,489, and 70,801 annotations were assigned to Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories, respectively, and 5918 differentially expressed genes (DEGs) were identified between the autofertile and the autosterile lines. Among the most enriched metabolic pathways in the DEGs subset were those related with amino acid biosynthesis, terpenoid metabolism, or signal transduction. Some DEGs have been related with previous QTLs identified for autofertility traits, and their putative functions are discussed. The results derived from this work provide an important transcriptomic reference for style-stigma processes to aid our understanding of the molecular mechanisms involved in faba bean fertilization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ruiz-Casado2024,

title = {Enhancing Histopathological Image Classification Performance through Synthetic Data Generation with Generative Adversarial Networks},

author = {Jose L. Ruiz-Casado and Miguel A. Molina-Cabello and Rafael M. Luque-Baena},

doi = {10.3390/s24123777},

issn = {1424-8220},

year  = {2024},

date = {2024-06-00},

journal = {Sensors},

volume = {24},

number = {12},

publisher = {MDPI AG},

abstract = {Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibile methods used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, a significant issue is the unbalanced nature of available datasets, with some classes having more images than others, which may impact the performance of the models due to poorer generalizability. A possible strategy to avoid this problem is downsampling the class with the most images to create a balanced dataset. Nevertheless, this approach is not recommended for small datasets as it can lead to poor model performance. Instead, techniques such as data augmentation are traditionally used to address this issue. These techniques apply simple transformations such as translation or rotation to the images to increase variability in the dataset. Another possibility is using generative adversarial networks (GANs), which can generate images from a relatively small training set. This work aims to enhance model performance in classifying histopathological images by applying data augmentation using GANs instead of traditional techniques.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Espinosa_2024,

title = {Advancements in long-read genome sequencing technologies and algorithms},

author = {Elena Espinosa and Rocio Bautista and Rafael Larrosa and Oscar Plata},

url = {http://dx.doi.org/10.1016/j.ygeno.2024.110842},

doi = {10.1016/j.ygeno.2024.110842},

issn = {0888-7543},

year  = {2024},

date = {2024-05-01},

journal = {Genomics},

volume = {116},

number = {3},

pages = {110842},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Naranjo2024,

title = {Hydroxytyrosol, a Promising Supplement in the Management of Human Stroke: An Exploratory Study},

author = {Ángela Naranjo and M. Josefa Álvarez-Soria and Pilar Aranda-Villalobos and Ana M. Martínez-Rodríguez and Esther Martínez-Lara and Eva Siles},

doi = {10.3390/ijms25094799},

issn = {1422-0067},

year  = {2024},

date = {2024-05-00},

journal = {IJMS},

volume = {25},

number = {9},

publisher = {MDPI AG},

abstract = {Hydroxytyrosol (HT) is a bioactive olive oil phenol with beneficial effects in a number of pathological situations. We have previously demonstrated that an HT-enriched diet could serve as a beneficial therapeutic approach to attenuate ischemic-stroke-associated damage in mice. Our exploratory pilot study examined this effect in humans. Particularly, a nutritional supplement containing 15 mg of HT/day was administered to patients 24 h after the onset of stroke, for 45 days. Biochemical and oxidative-stress-related parameters, blood pressure levels, serum proteome, and neurological and functional outcomes were evaluated at 45 and 90 days and compared to a control group. The main findings were that the daily administration of HT after stroke could: (i) favor the decrease in the percentage of glycated hemoglobin and diastolic blood pressure, (ii) control the increase in nitric oxide and exert a plausible protective effect in oxidative stress, (iii) modulate the evolution of the serum proteome and, particularly, the expression of apolipoproteins, and (iv) be beneficial for certain neurological and functional outcomes. Although a larger trial is necessary, this study suggests that HT could be a beneficial nutritional complement in the management of human stroke.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alvarez2024c,

title = {Nitrosyl derivatives of the halide-bridged complexes [M2Cp2(μ-X)(μ-P Bu2)(CO)2] (M = Mo, W; X = Cl, I).},

author = {M. Angeles Alvarez and M. Esther García and Daniel García-Vivó and Ana M. Guerra and Miguel A. Ruiz},

doi = {10.1016/j.jorganchem.2024.123147},

issn = {0022-328X},

year  = {2024},

date = {2024-05-00},

journal = {Journal of Organometallic Chemistry},

volume = {1012},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alvarez2024d,

title = {C≡N and N≡O Bond Cleavages of Acetonitrile and Nitrosyl Ligands at a Dimolybdenum Center to Render Ethylidyne and Acetamidinate Ligands},

author = {M. Angeles Alvarez and M. Esther García and Daniel García-Vivó and Ana M. Guerra and Miguel A. Ruiz},

doi = {10.1021/acs.inorgchem.3c03697},

issn = {1520-510X},

year  = {2024},

date = {2024-02-19},

journal = {Inorg. Chem.},

volume = {63},

number = {7},

pages = {3207--3211},

publisher = {American Chemical Society (ACS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rodríguez-Rodríguez2024,

title = {The Impact of Noise and Brightness on Object Detection Methods},

author = {José A. Rodríguez-Rodríguez and Ezequiel López-Rubio and Juan A. Ángel-Ruiz and Miguel A. Molina-Cabello},

doi = {10.3390/s24030821},

issn = {1424-8220},

year  = {2024},

date = {2024-02-00},

journal = {Sensors},

volume = {24},

number = {3},

publisher = {MDPI AG},

abstract = {The application of deep learning to image and video processing has become increasingly popular nowadays. Employing well-known pre-trained neural networks for detecting and classifying objects in images is beneficial in a wide range of application fields. However, diverse impediments may degrade the performance achieved by those neural networks. Particularly, Gaussian noise and brightness, among others, may be presented on images as sensor noise due to the limitations of image acquisition devices. In this work, we study the effect of the most representative noise types and brightness alterations on images in the performance of several state-of-the-art object detectors, such as YOLO or Faster-RCNN. Different experiments have been carried out and the results demonstrate how these adversities deteriorate their performance. Moreover, it is found that the size of objects to be detected is a factor that, together with noise and brightness factors, has a considerable impact on their performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cordoba-Caballero2024,

title = {Exploring miRNA–target gene pair detection in disease with coRmiT},

author = {Jose Cordoba-Caballero and James R Perkins and Federico García-Criado and Diana Gallego and Alicia Navarro-Sánchez and Mireia Moreno-Estellés and Concepción Garcés and Fernando Bonet and Carlos Romá-Mateo and Rocio Toro and Belén Perez and Pascual Sanz and Matthias Kohl and Elena Rojano and Pedro Seoane and Juan A G Ranea},

doi = {10.1093/bib/bbae060},

issn = {1477-4054},

year  = {2024},

date = {2024-01-22},

volume = {25},

number = {2},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               A wide range of approaches can be used to detect micro RNA (miRNA)–target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}