The Ontology for Biomedical Investigations (OBI) is build in a collaborative, international effort and will serve as a resource for annotating biomedical investigations, including the study design, protocols and instrumentation used, the data generated and the types of analysis performed on the data. This ontology arose from the Functional Genomics Investigation Ontology (FuGO) and will contain both terms that are common to all biomedical investigations, including functional genomics investigations and those that are more domain specific.
A CART (classification and regression trees) is a data transformation method for producing a classification or regression model with a tree-based structure.
The act of taking part of a homogeneous cell culture and creating one or more additional separate cultures of similar qualities. input: cell_culture, output cell_culture min cardinality 2. part of cell culturing
A processed material that serves as a liquid vehicle for freezing cells for long term quiescent stroage, which contains chemicls needed to sustain cell viability across freeze-thaw cycles.
A class discovery objective (sometimes called unsupervised classification) is a data transformation objective where the aim is to organize input data (typically vectors of attributes) into classes, where the number of classes and their specifications are not known a priori. Depending on usage, the class assignment can be definite or probabilistic.
A class prediction objective (sometimes called supervised classification) is a data transformation objective where the aim is to create a predictor from training data through a machine learning technique. The training data consist of pairs of objects (typically vectors of attributes) and class labels for these objects. The resulting predictor can be used to attach class labels to any valid novel input object. Depending on usage, the prediction can be definite or probabilistic. A classification is learned from the training data and can then be tested on test data.
A categorical value specification that is an assessment of the stage of a cancer according to the American Joint Committee on Cancer (AJCC) v7 staging systems.
A cross validation objective is a data transformation objective in which the aim is to partition a sample of data into subsets such that the analysis is initially performed on a single subset, while the other subset(s) are retained for subsequent use in confirming and validating the initial analysis.
A processed material comprised of a collection of cultured cells that has been continuously maintained together in culture and shares a common propagation history.
A decision tree induction objective is a data transformation objective in which a tree-like graph of edges and nodes is created and from which the selection of each branch requires that some type of logical decision is made.
A dimensionality reduction is data partitioning which transforms each input m-dimensional vector (x_1, x_2, …, x_m) into an output n-dimensional vector (y_1, y_2, …, y_n), where n is smaller than m.
A performance status value specification designed by the Eastern Cooperative Oncology Group to assess disease progression and its affect on the daily living abilities of the patient.
a process through which a new type of cell culture or cell line is created, either through the isolation and culture of one or more cells from a fresh source, or the deliberate experimental modification of an existing cell culture (e.g passaging a primary culture to become a secondary culture or line, or the immortalization or stable genetic modification of an existing culture or line).
a process whereby a new type of cell line is created, either through passaging of a primary cell culture to relative genetic stability and compositional homogeneity, or through some experimental modification of an existing cell line to produce a new line with novel characteristics (e.g. immortalization or some other stable genetic modification, or selection of some defined subset).
A hierarchical clustering is a data transformation which achieves a class discovery objective, which takes as input data item and builds a hierarchy of clusters. The traditional representation of this hierarchy is a tree (visualized by a dendrogram), with the individual input objects at one end (leaves) and a single cluster containing every object at the other (root).
A categorical value specification that is a histologic grade assigned to a tumor slide specimen according to the American Joint Committee on Cancer (AJCC) 7th Edition grading system.
A categorical value specification that is an assessment of the stage of a gynecologic cancer according to the International Federation of Gynecology and Obstetrics (FIGO) staging systems.
A categorical value specification that is a pathologic finding about one or more characteristics of ovarian cancer following the rules of the FIGO classification system.
A k-means clustering is a data transformation which achieves a class discovery or partitioning objective, which takes as input a collection of objects (represented as points in multidimensional space) and which partitions them into a specified number k of clusters. The algorithm attempts to find the centers of natural clusters in the data. The most common form of the algorithm starts by partitioning the input points into k initial sets, either at random or using some heuristic data. It then calculates the mean point, or centroid, of each set. It constructs a new partition by associating each point with the closest centroid. Then the centroids are recalculated for the new clusters, and the algorithm repeated by alternate applications of these two steps until convergence, which is obtained when the points no longer switch clusters (or alternatively centroids are no longer changed).
A k-nearest neighbors is a data transformation which achieves a class discovery or partitioning objective, in which an input data object with vector y is assigned to a class label based upon the k closest training data set points to y; where k is the largest value that class label is assigned.
is a data transformation : 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
Manufacturing is a process with the intent to produce a processed material which will have a function for future use. A person or organization (having manufacturer role) is a participant in this process
A material entity that is an individual living system, such as animal, plant, bacteria or virus, that is capable of replicating or reproducing, growth and maintenance in the right environment. An organism may be unicellular or made up, like humans, of many billions of cells divided into specialized tissues and organs.
An entity that can bear roles, has members, and has a set of organization rules. Members of organizations are either organizations themselves or individual people. Members can bear specific organization member roles that are determined in the organization rules. The organization rules also determine how decisions are made on behalf of the organization by the organization members.
A partitioning objective is a data transformation objective where the aim is to generate a collection of disjoint non-empty subsets whose union equals a non-empty input set.
A categorical value specification that is a pathologic finding about one or more characteristics of colon cancer following the rules of the TNM AJCC v7 classification system as they pertain to distant metastases. TNM pathologic distant metastasis findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery.
A categorical value specification that is a pathologic finding about one or more characteristics of renal cancer following the rules of the TNM AJCC v7 classification system as they pertain to distant metastases. TNM pathologic distant metastasis findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery.
A categorical value specification that is a pathologic finding about one or more characteristics of lung cancer following the rules of the TNM AJCC v7 classification system as they pertain to distant metastases. TNM pathologic distant metastasis findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery.
A categorical value specification that is a pathologic finding about one or more characteristics of ovarian cancer following the rules of the TNM AJCC v7 classification system as they pertain to distant metastases. TNM pathologic distant metastasis findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery.
A categorical value specification that is a pathologic finding about one or more characteristics of colorectal cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of regional lymph nodes.
A categorical value specification that is a pathologic finding about one or more characteristics of renal cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of regional lymph nodes.
A categorical value specification that is a pathologic finding about one or more characteristics of lung cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of regional lymph nodes.
A categorical value specification that is a pathologic finding about one or more characteristics of ovarian cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of regional lymph nodes.
A categorical value specification that is a pathologic finding about one or more characteristics of colorectal cancer following the rules of the TNM American Joint Committee on Cancer (AJCC) version 7 classification system as they pertain to staging of the primary tumor. TNM pathologic primary tumor findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery.
A categorical value specification that is a pathologic finding about one or more characteristics of renal cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of the primary tumor. TNM pathologic primary tumor findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery.
A categorical value specification that is a pathologic finding about one or more characteristics of lung cancer following the rules of the TNM American Joint Committee on Cancer (AJCC) version 7 classification system as they pertain to staging of the primary tumor. TNM pathologic primary tumor findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery.
A categorical value specification that is a pathologic finding about one or more characteristics of ovarian cancer following the rules of the TNM AJCC v7 classification system as they pertain to staging of the primary tumor. TNM pathologic primary tumor findings are based on clinical findings supplemented by histopathologic examination of one or more tissue specimens acquired during surgery.
Peak matching is a data transformation performed on a dataset of a graph of ordered data points (e.g. a spectrum) with the objective of pattern matching local maxima above a noise threshold
A material separation objective aiming to separate material into multiple portions, each of which contains a similar composition of the input material.
A principal components analysis dimensionality reduction is a dimensionality reduction achieved by applying principal components analysis and by keeping low-order principal components and excluding higher-order ones.
A biological or chemical entity that bears a reagent role in virtue of it being intended for application in a scientific technique to participate in (or have molecular parts that participate in) a chemical reaction that facilitates the generation of data about some distinct entity, or the generation of some distinct material specified output.
A role inhering in a biological or chemical entity that is intended to be applied in a scientific technique to participate (or have molecular components that participate) in a chemical reaction that facilitates the generation of data about some entity distinct from the bearer, or the generation of some specified material output distinct from the bearer.
a role which inheres in material entities and is realized in the processes of making, enforcing or being defined by legislation or orders issued by a governmental body.
A material separation objective aiming to separate a material entity that has parts of different types, and end with at least one output that is a material with parts of fewer types (modulo impurities).
is a role which inheres in a person or organization and is realized in in a planned process which provides access to training, materials or execution of protocols for an organization or person
A specimen that derives from an anatomical part or substance arising from an organism. Examples of tissue specimen include tissue, organ, physiological system, blood, or body location (arm).
a role borne by a material entity that is gained during a specimen collection process and that can be realized by use of the specimen in an investigation
A role borne by a material entity that is obtained during a specimen collection process and that can be realized by performing measurements or observations on the specimen.
A support vector machine is a data transformation with a class prediction objective based on the construction of a separating hyperplane that maximizes the margin between two data sets of vectors in n-dimensional space.