Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data.
- Published in:
- Briefings in Functional Genomics, 2023, v. 22, n. 5, p. 428, doi. 10.1093/bfgp/elad002
- By:
- Publication type:
- Article
Works by Tabares-Soto, Reinel
Results: 25
1
2
A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.
- Published in:
- BMC Medical Informatics & Decision Making, 2024, v. 24, n. 1, p. 1, doi. 10.1186/s12911-024-02460-z
- By:
- Publication type:
- Article
3
Dinámica no lineal del mercado del café en Colombia.
- Published in:
- Revista Mutis, 2018, v. 8, n. 2, p. 73, doi. 10.21789/22561498.1405
- By:
- Publication type:
- Article
4
Biases associated with database structure for COVID-19 detection in X-ray images.
- Published in:
- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-30174-1
- By:
- Publication type:
- Article
5
InpactorDB: A Classified Lineage-Level Plant LTR Retrotransposon Reference Library for Free-Alignment Methods Based on Machine Learning.
- Published in:
- Genes, 2021, v. 12, n. 2, p. 190, doi. 10.3390/genes12020190
- By:
- Publication type:
- Article
6
Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes.
- Published in:
- Briefings in Bioinformatics, 2023, v. 24, n. 1, p. 1, doi. 10.1093/bib/bbac511
- By:
- Publication type:
- Article
7
Applying the ethics of AI: a systematic review of tools for developing and assessing AI-based systems.
- Published in:
- Artificial Intelligence Review, 2024, v. 57, n. 5, p. 1, doi. 10.1007/s10462-024-10740-3
- By:
- Publication type:
- Article
8
Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning.
- Published in:
- Journal of Integrative Bioinformatics, 2022, v. 19, n. 3, p. 1, doi. 10.1515/jib-2021-0036
- By:
- Publication type:
- Article
9
A systematic review of vision transformers and convolutional neural networks for Alzheimer's disease classification using 3D MRI images.
- Published in:
- Neural Computing & Applications, 2024, v. 36, n. 35, p. 21985, doi. 10.1007/s00521-024-10420-x
- By:
- Publication type:
- Article
10
TIP_finder: An HPC Software to Detect Transposable Element Insertion Polymorphisms in Large Genomic Datasets.
- Published in:
- Biology (2079-7737), 2020, v. 9, n. 9, p. 281, doi. 10.3390/biology9090281
- By:
- Publication type:
- Article
11
Inpactor, Integrated and Parallel Analyzer and Classifier of LTR Retrotransposons and Its Application for Pineapple LTR Retrotransposons Diversity and Dynamics.
- Published in:
- Biology (2079-7737), 2018, v. 7, n. 2, p. 32, doi. 10.3390/biology7020032
- By:
- Publication type:
- Article
12
Predicting no-shows at outpatient appointments in internal medicine using machine learning models.
- Published in:
- PeerJ Computer Science, 2025, p. 1, doi. 10.7717/peerj-cs.2762
- By:
- Publication type:
- Article
13
Classification of Alzheimer's disease stages from magnetic resonance images using deep learning.
- Published in:
- PeerJ Computer Science, 2023, p. 1, doi. 10.7717/peerj-cs.1490
- By:
- Publication type:
- Article
14
K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes.
- Published in:
- PeerJ, 2021, p. 1, doi. 10.7717/peerj.11456
- By:
- Publication type:
- Article
15
A systematic review of the application of machine learning in the detection and classification of transposable elements.
- Published in:
- PeerJ, 2019, p. 1, doi. 10.7717/peerj.8311
- By:
- Publication type:
- Article
16
Worldwide co-occurrence analysis of 17 species of the genus Brachypodium using data mining.
- Published in:
- PeerJ, 2019, p. 1, doi. 10.7717/peerj.6193
- By:
- Publication type:
- Article
17
Genomic object detection: An improved approach for transposable elements detection and classification using convolutional neural networks.
- Published in:
- PLoS ONE, 2023, v. 18, n. 9, p. 1, doi. 10.1371/journal.pone.0291925
- By:
- Publication type:
- Article
18
Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements.
- Published in:
- Processes, 2020, v. 8, n. 6, p. 638, doi. 10.3390/pr8060638
- By:
- Publication type:
- Article
19
A Machine Learning-based Pipeline for the Classification of CTX-M in Metagenomics Samples.
- Published in:
- Processes, 2019, v. 7, n. 4, p. 235, doi. 10.3390/pr7040235
- By:
- Publication type:
- Article
20
Optimized Convolutional Neural Network Models for Skin Lesion Classification.
- Published in:
- Computers, Materials & Continua, 2022, v. 70, n. 2, p. 2131, doi. 10.32604/cmc.2022.019529
- By:
- Publication type:
- Article
21
An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network.
- Published in:
- Computers, Materials & Continua, 2021, v. 69, n. 2, p. 2475, doi. 10.32604/cmc.2021.018514
- By:
- Publication type:
- Article
22
G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era.
- Published in:
- Evolutionary Bioinformatics, 2023, p. 1, doi. 10.1177/11769343221150585
- By:
- Publication type:
- Article
23
G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era.
- Published in:
- Evolutionary Bioinformatics, 2023, v. 19, p. 1, doi. 10.1177/11769343221150585
- By:
- Publication type:
- Article
24
Composition and Diversity of LTR Retrotransposons in the Coffee Leaf Rust Genome (Hemileia vastatrix).
- Published in:
- Agronomy, 2022, v. 12, n. 7, p. 1665, doi. 10.3390/agronomy12071665
- By:
- Publication type:
- Article
25
High nucleotide similarity of three Copia lineage LTR retrotransposons among plant genomes.
- Published in:
- Genome, 2023, v. 66, n. 3, p. 51, doi. 10.1139/gen-2022-0026
- By:
- Publication type:
- Article