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  1. Book ; Online: Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

    Gómez Vela, Francisco A. / García-Torres, Miguel / Divina, Federico / García-Torres, Miguel

    2021  

    Keywords Research & information: general ; Technology: general issues ; deep learning ; energy demand ; temporal convolutional network ; time series forecasting ; time series ; forecasting ; exponential smoothing ; electricity demand ; residential building ; energy efficiency ; clustering ; decision tree ; time-series forecasting ; evolutionary computation ; neuroevolution ; photovoltaic power plant ; short-term forecasting ; data processing ; data filtration ; k-nearest neighbors ; regression ; autoregression ; n/a
    Size 1 electronic resource (100 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Publishing place Basel, Switzerland
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021291556
    ISBN 9783036508634 ; 3036508635
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online: Computational Methods for the Analysis of Genomic Data and Biological Processes

    Gómez Vela, Francisco A. / Divina, Federico / García-Torres, Miguel / García-Torres, Miguel

    2021  

    Keywords Research & information: general ; Biology, life sciences ; HIGD2A ; cancer ; DNA methylation ; mRNA expression ; miRNA ; quercetin ; hypoxia ; eQTL ; CRISPR-Cas9 ; single-cell clone ; fine-mapping ; power ; RNA N6-methyladenosine site ; yeast genome ; methylation ; computational biology ; deep learning ; bioinformatics ; hepatocellular carcinoma ; transcriptomics ; proteomics ; bioinformatics analysis ; differentiation ; Gene Ontology ; Reactome Pathways ; gene-set enrichment ; meta-analysis ; transcription factor ; binding sites ; genomics ; chilling stress ; CBF ; DREB ; CAMTA1 ; pathway ; text mining ; infiltration tactics optimization algorithm ; classification ; clustering ; microarray ; ensembles ; machine learning ; infiltration ; computational intelligence ; gene co-expression network ; murine coronavirus ; viral infection ; immune response ; data mining ; systems biology ; obesity ; differential genes expression ; exercise ; high-fat diet ; pathways ; potential therapeutic targets ; DNA N6-methyladenine ; Chou's 5-steps rule ; Convolution Neural Network (CNN) ; Long Short-Term Memory (LSTM) ; machine-learning ; chromatin interactions ; prediction ; genome architecture ; n/a
    Size 1 electronic resource (222 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Publishing place Basel, Switzerland
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021044707
    ISBN 9783039437726 ; 3039437720
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Article ; Online: Computational Methods for the Analysis of Genomic Data and Biological Processes.

    Gómez-Vela, Francisco / Divina, Federico / García-Torres, Miguel

    Genes

    2020  Volume 11, Issue 10

    Abstract: Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [ ... ]. ...

    Abstract Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [...].
    MeSH term(s) Animals ; Biological Phenomena ; Computational Biology/methods ; Gene Expression Profiling ; Humans ; Oligonucleotide Array Sequence Analysis/methods ; Sequence Analysis, DNA/methods
    Keywords covid19
    Language English
    Publishing date 2020-10-20
    Publishing country Switzerland
    Document type Editorial ; Introductory Journal Article
    ZDB-ID 2527218-4
    ISSN 2073-4425 ; 2073-4425
    ISSN (online) 2073-4425
    ISSN 2073-4425
    DOI 10.3390/genes11101230
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Feature Selection

    Sosa-Cabrera, Gustavo / Gómez-Guerrero, Santiago / García-Torres, Miguel / Schaerer, Christian E.

    A perspective on inter-attribute cooperation

    2023  

    Abstract: High-dimensional datasets depict a challenge for learning tasks in data mining and machine learning. Feature selection is an effective technique in dealing with dimensionality reduction. It is often an essential data processing step prior to applying a ... ...

    Abstract High-dimensional datasets depict a challenge for learning tasks in data mining and machine learning. Feature selection is an effective technique in dealing with dimensionality reduction. It is often an essential data processing step prior to applying a learning algorithm. Over the decades, filter feature selection methods have evolved from simple univariate relevance ranking algorithms to more sophisticated relevance-redundancy trade-offs and to multivariate dependencies-based approaches in recent years. This tendency to capture multivariate dependence aims at obtaining unique information about the class from the intercooperation among features. This paper presents a comprehensive survey of the state-of-the-art work on filter feature selection methods assisted by feature intercooperation, and summarizes the contributions of different approaches found in the literature. Furthermore, current issues and challenges are introduced to identify promising future research and development.

    Comment: 17 pages, 2 figures
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Information Theory
    Subject code 006 ; 004
    Publishing date 2023-06-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  5. Article ; Online: Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty.

    Gómez-Guerrero, Santiago / Ortiz, Inocencio / Sosa-Cabrera, Gustavo / García-Torres, Miguel / Schaerer, Christian E

    Entropy (Basel, Switzerland)

    2021  Volume 24, Issue 1

    Abstract: Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed ...

    Abstract Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for
    Language English
    Publishing date 2021-12-30
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e24010064
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Computational Methods for the Analysis of Genomic Data and Biological Processes

    Gómez-Vela, Francisco / Divina, Federico / García-Torres, Miguel

    Genes. 2020 Oct. 20, v. 11, no. 10

    2020  

    Abstract: Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [ ... ] ...

    Abstract Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [...]
    Keywords computational methodology ; genomics ; microarray technology
    Language English
    Dates of publication 2020-1020
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2527218-4
    ISSN 2073-4425
    ISSN 2073-4425
    DOI 10.3390/genes11101230
    Database NAL-Catalogue (AGRICOLA)

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  7. Article: A Comparative Study of Supervised Machine Learning Algorithms for the Prediction of Long-Range Chromatin Interactions

    Vanhaeren, Thomas / Divina, Federico / García-Torres, Miguel / Gómez-Vela, Francisco / Vanhoof, Wim / Martínez-García, Pedro Manuel

    Genes. 2020 Aug. 24, v. 11, no. 9

    2020  

    Abstract: The role of three-dimensional genome organization as a critical regulator of gene expression has become increasingly clear over the last decade. Most of our understanding of this association comes from the study of long range chromatin interaction maps ... ...

    Abstract The role of three-dimensional genome organization as a critical regulator of gene expression has become increasingly clear over the last decade. Most of our understanding of this association comes from the study of long range chromatin interaction maps provided by Chromatin Conformation Capture-based techniques, which have greatly improved in recent years. Since these procedures are experimentally laborious and expensive, in silico prediction has emerged as an alternative strategy to generate virtual maps in cell types and conditions for which experimental data of chromatin interactions is not available. Several methods have been based on predictive models trained on one-dimensional (1D) sequencing features, yielding promising results. However, different approaches vary both in the way they model chromatin interactions and in the machine learning-based strategy they rely on, making it challenging to carry out performance comparison of existing methods. In this study, we use publicly available 1D sequencing signals to model cohesin-mediated chromatin interactions in two human cell lines and evaluate the prediction performance of six popular machine learning algorithms: decision trees, random forests, gradient boosting, support vector machines, multi-layer perceptron and deep learning. Our approach accurately predicts long-range interactions and reveals that gradient boosting significantly outperforms the other five methods, yielding accuracies of about 95%. We show that chromatin features in close genomic proximity to the anchors cover most of the predictive information, as has been previously reported. Moreover, we demonstrate that gradient boosting models trained with different subsets of chromatin features, unlike the other methods tested, are able to produce accurate predictions. In this regard, and besides architectural proteins, transcription factors are shown to be highly informative. Our study provides a framework for the systematic prediction of long-range chromatin interactions, identifies gradient boosting as the best suited algorithm for this task and highlights cell-type specific binding of transcription factors at the anchors as important determinants of chromatin wiring mediated by cohesin.
    Keywords chromatin ; computer simulation ; decision support systems ; gene expression ; genome ; genomics ; models ; prediction ; support vector machines ; transcription factors
    Language English
    Dates of publication 2020-0824
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2527218-4
    ISSN 2073-4425
    ISSN 2073-4425
    DOI 10.3390/genes11090985
    Database NAL-Catalogue (AGRICOLA)

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  8. Article ; Online: Soft Computing for Analysis of Biomedical Data.

    Divina, Federico / García-Torres, Miguel / Hu, Ting / Schaerer, Christian E

    Computational and mathematical methods in medicine

    2018  Volume 2018, Page(s) 3902484

    MeSH term(s) Computational Biology/methods ; Data Interpretation, Statistical ; Data Mining ; Electroencephalography/statistics & numerical data ; Gene Regulatory Networks ; Humans ; Machine Learning ; Magnetic Resonance Imaging/statistics & numerical data
    Language English
    Publishing date 2018-11-15
    Publishing country United States
    Document type Editorial ; Introductory Journal Article
    ZDB-ID 2252430-7
    ISSN 1748-6718 ; 1748-670X ; 1027-3662
    ISSN (online) 1748-6718
    ISSN 1748-670X ; 1027-3662
    DOI 10.1155/2018/3902484
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6373: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay.

    Mello-Román, Jorge D / Mello-Román, Julio C / Gómez-Guerrero, Santiago / García-Torres, Miguel

    Computational and mathematical methods in medicine

    2019  Volume 2019, Page(s) 7307803

    Abstract: Early diagnosis of dengue continues to be a concern for public health in countries with a high incidence of this disease. In this work, we compared two machine learning techniques: artificial neural networks (ANN) and support vector machines (SVM) as ... ...

    Abstract Early diagnosis of dengue continues to be a concern for public health in countries with a high incidence of this disease. In this work, we compared two machine learning techniques: artificial neural networks (ANN) and support vector machines (SVM) as assistance tools for medical diagnosis. The performance of classification models was evaluated in a real dataset of patients with a previous diagnosis of dengue extracted from the public health system of Paraguay during the period 2012-2016. The ANN multilayer perceptron achieved better results with an average of 96% accuracy, 96% sensitivity, and 97% specificity, with low variation in thirty different partitions of the dataset. In comparison, SVM polynomial obtained results above 90% for accuracy, sensitivity, and specificity.
    MeSH term(s) Adult ; Databases, Factual/statistics & numerical data ; Dengue/diagnosis ; Dengue/epidemiology ; Early Diagnosis ; Female ; Humans ; Male ; Mathematical Concepts ; Models, Biological ; Neural Networks, Computer ; Paraguay/epidemiology ; Support Vector Machine ; Young Adult
    Language English
    Publishing date 2019-07-29
    Publishing country United States
    Document type Comparative Study ; Journal Article
    ZDB-ID 2252430-7
    ISSN 1748-6718 ; 1748-670X ; 1027-3662
    ISSN (online) 1748-6718
    ISSN 1748-670X ; 1027-3662
    DOI 10.1155/2019/7307803
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6373: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  10. Article: Distribution level electric current consumption and meteorological data set of the east region of Paraguay

    Velázquez, Gustavo / Morales, Félix / García-Torres, Miguel / Gómez-Vela, Francisco / Divina, Federico / Vázquez Noguera, José Luis / Daumas-Ladouce, Federico / Sauer Ayala, Carlos / Pinto-Roa, Diego P. / Gardel-Sotomayor, Pedro E. / Mello Román, Julio César

    Data in Brief. 2022 Feb., v. 40

    2022  

    Abstract: This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, ...

    Abstract This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay. The final data consists of a total of 22.445 records of temperature, relative humidity, wind speed and atmospheric pressure. On the other hand, the electrical energy consumption data set contains a total of 1.848.947 records, all of them coming from the one hundred and fifteen feeders located throughout the Alto Paraná region of Paraguay. Electrical energy consumption data was provided by Administración Nacional de Electricidad (ANDE). The analysis of this data can yield insights regarding the energy consumption in the area.
    Keywords atmospheric pressure ; data collection ; electric current ; electric energy consumption ; electric power ; energy ; meteorological data ; relative humidity ; temperature ; wind speed ; Paraguay
    Language English
    Dates of publication 2022-02
    Publishing place Elsevier Inc.
    Document type Article
    ZDB-ID 2786545-9
    ISSN 2352-3409
    ISSN 2352-3409
    DOI 10.1016/j.dib.2021.107699
    Database NAL-Catalogue (AGRICOLA)

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