DT terms with no definition

[obi-bot]

!obi:OBI_0200033 "leave one out cross validation method" !obi:OBI_0200183 "scaling objective" !obi:OBI_0200066 "Holm false discovery rate correction method" !obi:OBI_0302868 "mean-centered data" !obi:OBI_0000713 "GenePattern software" !obi:OBI_0200032 "K-fold cross validation method" !obi:OBI_0200180 "spread calculation objective" !obi:OBI_0000675 "statistical hypothesis test objective" !obi:OBI_0200197 "spectrum analysis objective"

Reported by: mcourtot

Original Ticket: obi/obi-terms/397

[obi-bot]

Ok will get on to these. I thought file was frozen now though - when can I realistically add these in?

Original comment by: jamesmalone

[obi-bot]

all the following defined by TB, need an update

Survival analysis: survival analysis is a data transformation that involves the modeling of time to event data; the purpose of survival analysis is to model the underlying distribution of event times and to assess the dependence of the event time on other explanatory variables

Kaplan meier: a nonparametric (actuarial) data transformation technique for estimating time-related events (the survivorship function). It is a univariate analysis that estimates the probability of the proportion of subjects in remission at a particular time, starting from the initiation of active date (time zero), and takes into account those lost to follow-up or not yet in remission at end of study (censored), Objective: survival analysis

Longitudinal analysis: a correlational research study that involves repeated observations of the same items over long periods of time; alternative terms: correlation study Proportional hazards model: Proportional hazards model is a data transformation model to estimate the effects of different covariates influencing the times-to-failure of a system. Objective: survival analysis; alternative Terms: Cox Models; Cox Proportional Hazards Models

continuum mass spectrum: A continuum mass spectrum is a data transformation that contains the full profile of the detected signals for a given ion. Objective: mass spectrum analysis

characteristic path length calculation: Quantifying subgraph navigability based on shortest-path length averaged over all pairs of subgraph vertices Objective: mass spectrum analysis

centroid mass spectrum: A centroid mass spectrum is a data transformation in which many points are used to delineate a mass spectral peak, is converted into mass-centroided data by a data compression algorithm. The centroided mass peak is located at the weighted center of mass of the profile peak. The normalized area of the peak provides the mass intensity data.. Objective: mass spectrum analysis; Alternative Names: Centroiding

Matrix-Assisted Laser Desorption-Ionization Fast Atom Bombardment: fast atom bombardment is A data transformation technique used for the analysis of a wide range of biomolecules that records positive and negative fast atom bombardment spectra on a mass spectrometer fitted with an atom gun with xenon as the customary beam. The mass spectra obtained contain molecular weight recognition as well as sequence information. Objective: Mass Spectrum Analysis; Definition: Medical Subject Headings D013058

Secondary Ion: Secondary Ion is a data transformation technique for mass spectrometry that used a beam of primary ions with an energy of 5-20 kiloelectronvolts (keV) to bombard a small spot on the surface of the sample under ultra-high vacuum conditions. Positive and negative secondary ions sputtered from the surface are analyzed in a mass spectrometer in regards to their mass-to-charge ratio. Digital imaging can be generated from the secondary ion beams and their intensity can be measured. Ionic images can be correlated with images from light or other microscopy providing useful tools in the study of molecular and drug actions. Objective: Mass Spectrum Analysis; Definition: Medical Subject Headings D013058

Electrospray Ionization: Electrospray ionization is a mass spectrometry technique used for analysis of nonvolatile compounds such as proteins and macromolecules. The technique involves preparing electrically charged droplets from analyte molecules dissolved in solvent. The electrically charged droplets enter a vacuum chamber where the solvent is evaporated. Evaporation of solvent reduces the droplet size, thereby increasing the coulombic repulsion within the droplet. As the charged droplets get smaller, the excess charge within them causes them to disintegrate and release analyte molecules. The volatilized analyte molecules are then analyzed by mass spectrometry.(definition from Medical Subject Headings: D021241) Objective: Mass Spectrometry Analysis Tandem Mass Spectrometry: Tandem Mass Spectrometry is a data transformation that uses two or more analyzers separated by a region in which ions can be induced to fragment by transfer of energy (frequently by collision with other molecules). Objective: Mass Spectrometry Analysis Gas Chromatography-Mass Spectrometry: Gas Chromatography-Mass Spectrometry is a data transformation combining mass spectrometry and gas chromatography for the qualitative as well as quantitative determinations of compounds. Objective: Mass Spectrometry analysis

False Discovery Rate: The false discovery rate is a data transformation used in multiple hypothesis testing to correct for multiple comparisons. It controls the expected proportion of incorrectly rejected null hypotheses (type I errors) in a list of rejected hypotheses. It is a less conservative comparison procedure with greater power than familywise error rate (FWER) control, at a cost of increasing the likelihood of obtaining type I errors. Objective: multiple testing correction http://www.wikidoc.org/index.php/False\_discovery\_rate Child: Holm false discovery rate correction method: The Holm-method is a data transformation that performs more than one hypothesis test simultaneously, a closed-test procedure, that controls the familywise error rate for all the k hypotheses at level α in the strong sense. Objective: multiple testing correction http://en.wikipedia.org/wiki/Holm%E2%80%93Bonferroni\_method

objective: pattern matching: pattern matching is the act of checking for the presence of the constituents of a given pattern. In contrast to pattern recognition, the pattern is rigidly specified. http://en.wikipedia.org/wiki/Pattern\_matching

peak matching is a data transformation performed on a dataset of a graph of ordered datapoints (e.g. a spectrum) with the objective of pattern matching local maxima above a noise threshold

Bonferroni correction: the Bonferroni correction is a data transformation used to address the problem of multiple comparisons, based on the idea that if an experimenter is testing n dependent or independent hypotheses on a set of data, then one way of maintaining the familywise error rate is to test each individual hypothesis at a statistical significance level of 1/n times what it would be if only one hypothesis were tested.

Objective: multiple testing correction http://en.wikipedia.org/wiki/Bonferroni\_correction

objective: correlation (often measured as a correlation coefficient, ρ) indicates the strength and direction of a relationship between two random variables.

chi-square test: the chi-square test is a statistical hypothesis test in which the sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true, or any in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by making the sample size large enough. Objective: hypothesis testing

children: Pearson's chi-square test: Pearson’s chi-square test evaluates a null hypothesis that the frequency distribution of certain events observed in a sample is consistent with a particular theoretical distribution., Alternative terms: chi-square goodness-of-fit test; chi-square test for independence

Yates' chi-square test: Yate’s chi square tests for independence in a contingency table with the assumption that the discrete probability of observed frequencies can be approximated by the chi-squared distribution, which is continuous. Alternative terms: Yates' correction for continuity.

Mantel-Haenszel chi-square test. The Mantel-Haenszel chi-square statistic tests the alternative hypothesis that there is a linear association between the row variable and the column variable. Both variables must lie on an ordinal scale. Definition: http://v8doc.sas.com/sashtml/stat/chap28/sect19.htm

Fisher's Exact Test: Fisher's exact test is a statistical significance test used in the analysis of contingency tables where sample sizes are small where the significance of the deviation from a null hypothesis can be calculated exactly, rather than relying on an approximation that becomes exact in the limit as the sample size grows to infinity, as with many statistical tests. Objective: hypothesis testing

Likelihood-ratio tests: Likelihood-ratio tests whether there is evidence of the need to move from a simple model to a more complicated one (where the simple model is nested within the complicated one); tests of the goodness-of-fit between two models.

Objective: cross validation. is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds. http://en.wikipedia.org/wiki/Cross-validation\_\(statistics)

K-fold cross-validation: K-fold cross-validation randomly partitions the original sample into K subsamples. Of the K subsamples, a single subsample is retained as the validation data for testing the model, and the remaining K − 1 subsamples are used as training data. The cross-validation process is then repeated K times (the folds), with each of the K subsamples used exactly once as the validation data. The K results from the folds then can be averaged (or otherwise combined) to produce a single estimation. The advantage of this method over repeated random sub-sampling is that all observations are used for both training and validation, and each observation is used for validation exactly once. 10-fold cross-validation is commonly used . Objective: cross-validation

Leave-one-out cross-validation: leave-one-out cross-validation (LOOCV) involves using a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. Objective: cross-validation

Original comment by: helenp

[obi-bot]

• priority: 8 --> 7

Original comment by: bpeters42

[obi-bot]

• priority: 7 --> 8

Original comment by: bpeters42

[obi-bot]

• priority: 8 --> 7

Original comment by: bpeters42

[obi-bot]

Downgraded to 7 as they are in the file now.

Original comment by: bpeters42

[obi-bot]

• status: open --> closed

Original comment by: helenp