5-Chemistry-Biochemistry-Drug-Activity-Methods-Factor Analysis

factor analysis

Processes have factors {factor analysis}. Physico-chemical or structural properties describe compounds and have components {descriptor, factor} {X-variable, factor} {X descriptor, factor}. Chemical activities relate to variables {response variable}.

canonical factor analysis

Methods {canonical factor analysis} can be for factor analysis.

centroid method

Methods {centroid method} can be for factor analysis.

combinatoric QSAR

QSAR {combinatoric QSAR} can find similarities using different descriptor combinations.

Comparative Molecular Moment Analysis

Moments of inertia, and dipole and quadrupole moments, can be descriptors to calculate molecular moments {Comparative Molecular Moment Analysis} (CoMMA). CoMMA depends on shapes and charges.

Correlation Analysis

Properties and structures have relations {Correlation Analysis}.

correspondence analysis

Factor-analysis methods {correspondence analysis} {correspondence factor analysis} (CFA) can use variable frequencies relative to activities, finds chi-square values, and finds principal components.

disjoint principal component

Principal components {disjoint principal component} (DPP) can be independent.

eigenvalue-one criterion

Thresholds {eigenvalue-one criterion} can be how many components have eigenvalues greater than one.

eigenvector projection

Unsupervised linear methods {eigenvector projection} can find factors.

Evolutionary Programming

Models {Evolutionary Programming} (EP) can add and subtract randomly selected variables, with crossing-over, and evaluate for "fitness" or best fit.

evolving factor analysis

Methods {evolving factor analysis} (EVA) can analyze ordered data.

explained variance percentage

Methods {percentage of explained variance} {explained variance percentage} can indicate number of components required to reach 90% of total variance.

extrathermodynamic approach

Parameters and descriptors can linearly relate to free energy {extrathermodynamic approach}.

free energy perturbation

Factor-analysis methods {free energy perturbation} (FEP) can use free-energy changes.

Free-Wilson approach

Binary descriptors can note molecule-substructure presence or absence {Free-Wilson approach}.

Genetic Function Algorithm

Linear property sets can have different values, change values by crossing-over between related such genes, and have random change {Genetic Function Algorithm} (GFA), to select best fit.

Hammett sigma value

Values {Hammett sigma value} can relate to electronic and electrostatic properties.

Hansch equation

Activity, partition coefficients for hydrophobicity, ionization degree, and molecular size relate {Hansch equation}.

latent variable

Variables {latent variable} can be linear-descriptor combination.

linear discriminant analysis

Supervised methods {linear discriminant analysis} (LDA), in which boundary surface minimizes region variance and maximizes variance between regions, can put compounds into groups by activity level.

linear free energy

log K = k1 * sigma + k2 {linear free energy equation, drug} (LFE).

linear learning machine

Supervised methods {linear learning machine} (LLM) can divide n-dimensional space into regions, using discriminant function.

maximum-likelihood method

Factor-analysis methods {maximum-likelihood method} can find factors.

multidimensional scaling

Metric or non-metric methods {multidimensional scaling} (MDS) can analyze similarity or dissimilarity matrices to find dimension number and place objects in proper relative positions.

multivariate adaptive regression spline

Non-parametric methods {multivariate adaptive regression spline} (MARS) can find factors.

Mutation and Selection Uncover Models

Models {Mutation and Selection Uncover Models} (MUSEUM) can add and subtract randomly selected variables, with no crossing-over, and evaluate for "fitness" or best fit.

non-linear iterative partial least-squares

Unsupervised linear methods {non-linear iterative partial least-squares} (NIPALS) can represent data as product of score matrix, for original observations, and loading-matrix transform, for original factors.

non-linear mapping

Topological mappings {non-linear mapping} (NLM) can be factor-analysis methods in which linear-variable combinations make two or three new variables.

predictive computational model

Information about compound physico-chemical properties can predict compound chemical or physiological behavior in vitro and in vivo {predictive computational model}.

principal component analysis

Variables {principal component} (PC) can be linear-descriptor combinations. Unsupervised linear method {principal component analysis, factor} (PCA) represents data as product of score matrix, for original observations, and loading-matrix transform, for original factors. PCA is factor-analysis method in which linear variable combinations make two or three new variables. PCA reduces unimportant variables.

principal component regression

Singular-value decomposition (SVD) can find best singular values for predicting {principal component regression} (PCR). SVD projects regression to latent structures.

principal factor analysis

Modified PCA {principal factor analysis} can find principal factors.

Procrustes analysis

Methods {Procrustes analysis} can identify descriptor sets for describing similarity.

QR algorithm

Methods {QR algorithm} can diagonalize matrices.

rank annihilation

Unsupervised linear methods {rank annihilation} can find factors.

Scree-plot

Residual variance approaches constancy {Scree-test, drug}, and plotted slope levels off {Scree-plot}, depending on component number.

singular value decomposition

In unsupervised linear methods {singular value decomposition, drug} (SVD), correlation matrix is product of score, eigenvalue, and loading matrices, with diagonalization using QR algorithm.

spectral mapping analysis

Factor-analysis methods {spectral mapping analysis} (SMA) can first take data logarithm to eliminate outliers and then subtract means from rows and columns, to leave only variation, showing which variables are important and how much.

structure space

Spaces {structure space} can have two or three principal components.

target-transformation

Methods {target-transformation factor analysis} can rotate features to match known pattern, such as hypothesis or signature.

Unsupervised Method

Factors and response variable have relations {Unsupervised Method}, without using factor information or predetermined models.

5-Chemistry-Biochemistry-Drug-Activity-Methods-Factor Analysis-Design

factorial design

Designs {factorial design} can try to ensure design-space sampling, if position varies.

fractional factorial

Designs {fractional factorial design} can try to ensure design-space sampling, if position varies.

response surface method

Three-level designs {response surface method} (RSM) can have three factors that quantify relationships among responses and factors. RSM includes MLR, OLS, PCR, and PLS linear designs; non-linear regression analysis (NLR); and non-parametric methods, such as ACE, NPLS, and MARS.

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Date Modified: 2022.0225