Application of Bioinformatics in Cancers |
Autore | Brenner J. Chad |
Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
Descrizione fisica | 1 electronic resource (418 p.) |
Soggetto non controllato |
cancer treatment
extreme learning independent prognostic power AID/APOBEC HP gene inactivation biomarkers biomarker discovery chemotherapy artificial intelligence epigenetics comorbidity score denoising autoencoders protein single-biomarkers gene signature extraction high-throughput analysis concatenated deep feature feature selection differential gene expression analysis colorectal cancer ovarian cancer multiple-biomarkers gefitinib cancer biomarkers classification cancer biomarker mutation hierarchical clustering analysis HNSCC cell-free DNA network analysis drug resistance hTERT variable selection KRAS mutation single-cell sequencing network target skin cutaneous melanoma telomeres Neoantigen Prediction datasets clinical/environmental factors StAR PD-L1 miRNA circulating tumor DNA (ctDNA) false discovery rate predictive model Computational Immunology brain metastases observed survival interval next generation sequencing brain machine learning cancer prognosis copy number aberration mutable motif steroidogenic enzymes tumor mortality tumor microenvironment somatic mutation transcriptional signatures omics profiles mitochondrial metabolism Bufadienolide-like chemicals cancer-related pathways intratumor heterogeneity estrogen locoregionally advanced RNA feature extraction and interpretation treatment de-escalation activation induced deaminase knockoffs R package copy number variation gene loss biomarkers cancer CRISPR overall survival histopathological imaging self-organizing map Network Analysis oral cancer biostatistics firehose Bioinformatics tool alternative splicing biomarkers diseases genes histopathological imaging features imaging TCGA decision support systems The Cancer Genome Atlas molecular subtypes molecular mechanism omics curative surgery network pharmacology methylation bioinformatics neurological disorders precision medicine cancer modeling miRNAs breast cancer detection functional analysis biomarker signature anti-cancer hormone sensitive cancers deep learning DNA sequence profile pancreatic cancer telomerase Monte Carlo mixture of normal distributions survival analysis tumor infiltrating lymphocytes curation pathophysiology GEO DataSets head and neck cancer gene expression analysis erlotinib meta-analysis traditional Chinese medicine breast cancer TCGA mining breast cancer prognosis microarray DNA interaction health strengthening herb cancer genomic instability |
ISBN | 3-03921-789-5 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910367743403321 |
Brenner J. Chad | ||
MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass |
Autore | Aranha José |
Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
Descrizione fisica | 1 electronic resource (276 p.) |
Soggetto topico |
Research & information: general
Geography |
Soggetto non controllato |
AGB estimation and mapping
mangroves UAV LiDAR WorldView-2 terrestrial laser scanning above-ground biomass nondestructive method DBH bark roughness Landsat dataset forest AGC estimation random forest spatiotemporal evolution aboveground biomass variable selection forest type machine learning subtropical forests Landsat 8 OLI seasonal images stepwise regression map quality subtropical forest urban vegetation biomass estimation Sentinel-2A Xuzhou forest biomass estimation forest inventory data multisource remote sensing biomass density ecosystem services trade-off synergy multiple ES interactions valley basin norway spruce LiDAR allometric equation individual tree detection tree height diameter at breast height GEOMON ALOS-2 L band SAR Sentinel-1 C band SAR Sentinel-2 MSI ALOS DSM stand volume support vector machine for regression ordinary kriging forest succession leaf area index plant area index machine learning algorithms forest growing stock volume SPOT6 imagery Pinus massoniana plantations sentinel 2 landsat remote sensing GIS shrubs biomass bioenergy vegetation indices |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910557474803321 |
Aranha José | ||
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Bioinformatics and Machine Learning for Cancer Biology |
Autore | Wan Shibiao |
Pubbl/distr/stampa | Basel, : MDPI Books, 2022 |
Descrizione fisica | 1 electronic resource (196 p.) |
Soggetto topico |
Research & information: general
Biology, life sciences |
Soggetto non controllato |
tumor mutational burden
DNA damage repair genes immunotherapy biomarker biomedical informatics breast cancer estrogen receptor alpha persistent organic pollutants drug-drug interaction networks molecular docking NGS ctDNA VAF liquid biopsy filtering variant calling DEGs diagnosis ovarian cancer PUS7 RMGs CPA4 bladder urothelial carcinoma immune cells T cell exhaustion checkpoint architectural distortion image processing depth-wise convolutional neural network mammography bladder cancer Annexin family survival analysis prognostic signature therapeutic target R Shiny application RNA-seq proteomics multi-omics analysis T-cell acute lymphoblastic leukemia CCLE sitagliptin thyroid cancer (THCA) papillary thyroid cancer (PTCa) thyroidectomy metastasis drug resistance biomarker identification transcriptomics machine learning prediction variable selection major histocompatibility complex bidirectional long short-term memory neural network deep learning cancer incidence mortality modeling forecasting Google Trends Romania ARIMA TBATS NNAR |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910595077403321 |
Wan Shibiao | ||
Basel, : MDPI Books, 2022 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Machine Learning in Sensors and Imaging |
Autore | Nam Hyoungsik |
Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
Descrizione fisica | 1 electronic resource (302 p.) |
Soggetto topico |
Technology: general issues
History of engineering & technology |
Soggetto non controllato |
star image
image denoising reinforcement learning maximum likelihood estimation mixed Poisson–Gaussian likelihood machine learning-based classification non-uniform foundation stochastic analysis vehicle–pavement–foundation interaction forest growing stem volume coniferous plantations variable selection texture feature random forest red-edge band on-shelf availability semi-supervised learning deep learning image classification machine learning explainable artificial intelligence wildfire risk assessment Naïve bayes transmission-line corridors image encryption compressive sensing plaintext related chaotic system convolutional neural network color prior model object detection piston error detection segmented telescope BP artificial neural network modulation transfer function computer vision intelligent vehicles extrinsic camera calibration structure from motion convex optimization temperature estimation BLDC electric machine protection touchscreen capacitive display SNR stylus laser cutting quality monitoring artificial neural network burr formation cut interruption fiber laser semi-supervised fuzzy noisy real-world plankton marine activity recognition wearable sensors imbalanced activities sampling methods path planning Q-learning neural network YOLO algorithm robot arm target reaching obstacle avoidance |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910566484703321 |
Nam Hyoungsik | ||
Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing |
Autore | Lee Saro |
Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
Descrizione fisica | 1 electronic resource (438 p.) |
Soggetto non controllato |
artificial neural network
model switching sensitivity analysis neural networks logit boost Qaidam Basin land subsidence land use/land cover (LULC) naïve Bayes multilayer perceptron convolutional neural networks single-class data descriptors logistic regression feature selection mapping particulate matter 10 (PM10) Bayes net gray-level co-occurrence matrix multi-scale Logistic Model Trees classification Panax notoginseng large scene coarse particle grayscale aerial image Gaofen-2 environmental variables variable selection spatial predictive models weights of evidence landslide prediction random forest boosted regression tree convolutional network Vietnam model validation colorization data mining techniques spatial predictions SCAI unmanned aerial vehicle high-resolution texture spatial sparse recovery landslide susceptibility map machine learning reproducible research constrained spatial smoothing support vector machine random forest regression model assessment information gain ALS point cloud bagging ensemble one-class classifiers leaf area index (LAI) landslide susceptibility landsat image ionospheric delay constraints spatial spline regression remote sensing image segmentation panchromatic Sentinel-2 remote sensing optical remote sensing materia medica resource GIS precise weighting change detection TRMM traffic CO crop training sample size convergence time object detection gully erosion deep learning classification-based learning transfer learning landslide traffic CO prediction hybrid model winter wheat spatial distribution logistic alternating direction method of multipliers hybrid structure convolutional neural networks geoherb predictive accuracy real-time precise point positioning spectral bands |
ISBN | 3-03921-216-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910367564103321 |
Lee Saro | ||
MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Metabolomics Data Processing and Data Analysis—Current Best Practices |
Autore | Hanhineva Kati |
Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
Descrizione fisica | 1 electronic resource (276 p.) |
Soggetto topico | Research & information: general |
Soggetto non controllato |
metabolic networks
mass spectral libraries metabolite annotation metabolomics data mapping nontarget analysis liquid chromatography mass spectrometry compound identification tandem mass spectral library forensics wastewater gut microbiome meta-omics metagenomics metabolomics metabolic reconstructions genome-scale metabolic modeling constraint-based modeling flux balance host–microbiome metabolism global metabolomics LC-MS spectra processing pathway analysis enrichment analysis mass spectrometry liquid chromatography MS spectral prediction metabolite identification structure-based chemical classification rule-based fragmentation combinatorial fragmentation time series PLS NPLS variable selection bootstrapped-VIP data repository computational metabolomics reanalysis lipidomics data processing triplot multivariate risk modeling environmental factors disease risk chemical classification in silico workflows metabolome mining molecular families networking substructures mass spectrometry imaging metabolomics imaging biostatistics ion selection algorithms liquid chromatography high-resolution mass spectrometry data-independent acquisition all ion fragmentation targeted analysis untargeted analysis R programming full-scan MS/MS processing R-MetaboList 2 liquid chromatography–mass spectrometry (LC/MS) fragmentation (MS/MS) data-dependent acquisition (DDA) simulator in silico untargeted metabolomics liquid chromatography–mass spectrometry (LC-MS) experimental design sample preparation univariate and multivariate statistics metabolic pathway and network analysis LC–MS metabolic profiling computational statistical unsupervised learning supervised learning |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910557354403321 |
Hanhineva Kati | ||
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Numerical Linear Algebra and the Applications |
Autore | Jbilou Khalide |
Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
Descrizione fisica | 1 electronic resource (126 p.) |
Soggetto topico | Information technology industries |
Soggetto non controllato |
inverse scattering
reciprocity gap functional chiral media mixed boundary conditions non-linear matrix equations perturbation bounds Lyapunov majorants fixed-point principle nonsymmetric differential matrix Riccati equation cosine product Golub–Kahan algorithm Krylov subspaces PCA SVD tensors quadratic form estimates upper bounds networks perron vector power method lanczos method pseudospectra eigenvalues matrix polynomial perturbation Perron root large-scale matrices approximation algorithm high-dimensional minimum norm solution regularisation Tikhonov ℓp-ℓq variable selection |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910557359403321 |
Jbilou Khalide | ||
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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