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Application of Bioinformatics in Cancers
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
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
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
Opac: Controlla la disponibilità qui
Machine Learning in Sensors and Imaging
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
Opac: Controlla la disponibilità qui
Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
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
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
Opac: Controlla la disponibilità qui
Numerical Linear Algebra and the Applications
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
Opac: Controlla la disponibilità qui