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Biomolecular Data Science-in Honor of Professor Philip E. Bourne
| Biomolecular Data Science-in Honor of Professor Philip E. Bourne |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2023 |
| Descrizione fisica | 1 online resource (232 p.) |
| Soggetto topico |
Biology, life sciences
Research & information: general |
| Soggetto non controllato |
amino acids
and graph neural network APOBEC basal cells binding energy bioinformatics biological macromolecules carbohydrates cell-type composition census tract ciliated cells CRISPR/Cas9 cryogenic electron microscopy cryogenic electron tomography data science deep learning DNA DNA sequencing drug discovery drug repositioning drugs of abuse electron crystallography electronic health records entropy FAIR function annotation functional families genome editing goblet cells graph neural network immune response to smoking kernel classifiers kernel method KinBase classification KinFams large language models ligand binding sites lung cancers machine learning macromolecular crystallography metagenomics micro-electron diffraction MSA mutational signatures n/a nuclear magnetic resonance spectroscopy nucleic acids off-targets Open Access pharmacovigilance Philip Bourne prioritization algorithm Protein Data Bank protein database protein kinases proteins quantum machine learning quantum metric learning reaction template read classification real-world evidence recurrent neural network retrosynthesis RNA search tool SHAP values small-molecule ligands smoking social determinants of health social media specificity annotation structural bioinformatics structure-based drug discovery variability Worldwide Protein Data Bank |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911053031203321 |
| MDPI - Multidisciplinary Digital Publishing Institute, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Learning to Understand Remote Sensing Images: Volume 1 / Qi Wang
| Learning to Understand Remote Sensing Images: Volume 1 / Qi Wang |
| Autore | Wang Qi |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (414 pages) |
| Soggetto topico | Computer science |
| Soggetto non controllato |
metadata
image classification sensitivity analysis ROI detection residual learning image alignment adaptive convolutional kernels Hough transform class imbalance land surface temperature inundation mapping multiscale representation object-based convolutional neural networks scene classification morphological profiles hyperedge weight estimation hyperparameter sparse representation semantic segmentation vehicle classification flood Landsat imagery target detection multi-sensor building damage detection optimized kernel minimum noise fraction (OKMNF) sea-land segmentation nonlinear classification land use SAR imagery anti-noise transfer network sub-pixel change detection Radon transform segmentation remote sensing image retrieval TensorFlow convolutional neural network particle swarm optimization optical sensors machine learning mixed pixel optical remotely sensed images object-based image analysis very high resolution images single stream optimization ship detection ice concentration online learning manifold ranking dictionary learning urban surface water extraction saliency detection spatial attraction model (SAM) quality assessment Fuzzy-GA decision making system land cover change multi-view canonical correlation analysis ensemble land cover semantic labeling sparse representation dimensionality expansion speckle filters hyperspectral imagery fully convolutional network infrared image Siamese neural network Random Forests (RF) feature matching color matching geostationary satellite remote sensing image change feature analysis road detection deep learning aerial images image segmentation aerial image multi-sensor image matching HJ-1A/B CCD endmember extraction high resolution multi-scale clustering heterogeneous domain adaptation hard classification regional land cover hypergraph learning automatic cluster number determination dilated convolution MSER semi-supervised learning gate Synthetic Aperture Radar (SAR) downscaling conditional random fields urban heat island hyperspectral image remote sensing image correction skip connection ISPRS spatial distribution geo-referencing Support Vector Machine (SVM) very high resolution (VHR) satellite image classification ensemble learning synthetic aperture radar conservation convolutional neural network (CNN) THEOS visible light and infrared integrated camera vehicle localization structured sparsity texture analysis DSFATN CNN image registration UAV unsupervised classification SVMs SAR image fuzzy neural network dimensionality reduction GeoEye-1 feature extraction sub-pixel energy distribution optimizing saliency analysis deep convolutional neural networks sparse and low-rank graph hyperspectral remote sensing tensor low-rank approximation optimal transport SELF spatiotemporal context learning Modest AdaBoost topic modelling multi-seasonal Segment-Tree Filtering locality information GF-4 PMS image fusion wavelet transform hashing machine learning techniques satellite images climate change road segmentation remote sensing tensor sparse decomposition Convolutional Neural Network (CNN) multi-task learning deep salient feature speckle canonical correlation weighted voting fully convolutional network (FCN) despeckling multispectral imagery ratio images linear spectral unmixing hyperspectral image classification multispectral images high resolution image multi-objective convolution neural network transfer learning 1-dimensional (1-D) threshold stability Landsat kernel method phase congruency subpixel mapping (SPM) tensor MODIS GSHHG database compressive sensing |
| ISBN |
9783038976851
3038976857 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367755603321 |
Wang Qi
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning to Understand Remote Sensing Images: Volume 2 / Qi Wang
| Learning to Understand Remote Sensing Images: Volume 2 / Qi Wang |
| Autore | Wang Qi |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (363 pages) |
| Soggetto non controllato |
metadata
image classification sensitivity analysis ROI detection residual learning image alignment adaptive convolutional kernels Hough transform class imbalance land surface temperature inundation mapping multiscale representation object-based convolutional neural networks scene classification morphological profiles hyperedge weight estimation hyperparameter sparse representation semantic segmentation vehicle classification flood Landsat imagery target detection multi-sensor building damage detection optimized kernel minimum noise fraction (OKMNF) sea-land segmentation nonlinear classification land use SAR imagery anti-noise transfer network sub-pixel change detection Radon transform segmentation remote sensing image retrieval TensorFlow convolutional neural network particle swarm optimization optical sensors machine learning mixed pixel optical remotely sensed images object-based image analysis very high resolution images single stream optimization ship detection ice concentration online learning manifold ranking dictionary learning urban surface water extraction saliency detection spatial attraction model (SAM) quality assessment Fuzzy-GA decision making system land cover change multi-view canonical correlation analysis ensemble land cover semantic labeling sparse representation dimensionality expansion speckle filters hyperspectral imagery fully convolutional network infrared image Siamese neural network Random Forests (RF) feature matching color matching geostationary satellite remote sensing image change feature analysis road detection deep learning aerial images image segmentation aerial image multi-sensor image matching HJ-1A/B CCD endmember extraction high resolution multi-scale clustering heterogeneous domain adaptation hard classification regional land cover hypergraph learning automatic cluster number determination dilated convolution MSER semi-supervised learning gate Synthetic Aperture Radar (SAR) downscaling conditional random fields urban heat island hyperspectral image remote sensing image correction skip connection ISPRS spatial distribution geo-referencing Support Vector Machine (SVM) very high resolution (VHR) satellite image classification ensemble learning synthetic aperture radar conservation convolutional neural network (CNN) THEOS visible light and infrared integrated camera vehicle localization structured sparsity texture analysis DSFATN CNN image registration UAV unsupervised classification SVMs SAR image fuzzy neural network dimensionality reduction GeoEye-1 feature extraction sub-pixel energy distribution optimizing saliency analysis deep convolutional neural networks sparse and low-rank graph hyperspectral remote sensing tensor low-rank approximation optimal transport SELF spatiotemporal context learning Modest AdaBoost topic modelling multi-seasonal Segment-Tree Filtering locality information GF-4 PMS image fusion wavelet transform hashing machine learning techniques satellite images climate change road segmentation remote sensing tensor sparse decomposition Convolutional Neural Network (CNN) multi-task learning deep salient feature speckle canonical correlation weighted voting fully convolutional network (FCN) despeckling multispectral imagery ratio images linear spectral unmixing hyperspectral image classification multispectral images high resolution image multi-objective convolution neural network transfer learning 1-dimensional (1-D) threshold stability Landsat kernel method phase congruency subpixel mapping (SPM) tensor MODIS GSHHG database compressive sensing |
| ISBN |
9783038976998
3038976997 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367755503321 |
|
Wang Qi
|
||
| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical Methods for the Analysis of Genomic Data
| Statistical Methods for the Analysis of Genomic Data |
| Autore | Jiang Hui |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (136 p.) |
| Soggetto topico |
Mathematics and Science
Research and information: general |
| Soggetto non controllato |
Bayes factor
Bayesian mixed-effect model boosting classification classification boundary clustering analysis convolutional neural networks CpG sites deep learning DNA methylation expectation-maximization algorithm false discovery rate control feed-forward neural networks gaussian finite mixture model GEE gene expression gene regulatory network gene set enrichment analysis integrative analysis kernel method lipid-environment interaction longitudinal lipidomics study machine learning multiple cancer types n/a network substructure nonparanormal graphical model omics data Ordinal responses penalized variable selection prognosis modeling RNA-seq uncertainty |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557545803321 |
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Jiang Hui
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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