The phrase "algorithm parsing" is correct and usable in written English.
It can be used in contexts related to computer science, programming, or data processing, specifically when discussing the analysis or interpretation of algorithms.
Example: "The software's algorithm parsing capabilities allow it to efficiently analyze large datasets and extract meaningful insights."
Alternatives: "algorithm analysis" or "algorithm interpretation".
But finding the diagnosis within the patient's genome is very time consuming and, "for that we need computational tools". The algorithm is called ClinPhen — a combination of "clinical" and "phenotypes". After examining a patient's medical records, the algorithm parses the sentences into short phrases.
The algorithm parses through text, finding contiguous sequences of word doublets that are known to occur somewhere in the reference nomenclature.
Given a sequence of letters from an alphabet, the algorithm parses the string sequentially to produce a dictionary of new phrases, in order of occurrence in the sequence.
GTB comprises a language (LC) with various kinds of built-in grammar and automata related objects, and a set of black-box methods written in C++ that provide implementations of grammar transforms, automata construction algorithms, parsing and recognition algorithms, and a variety of visualisation aids.
How the algorithm parses a specific passage of text is illustrated in Fig. 3.
Berman's algorithm parses sentences into coded concepts from the UMLS and stop-words, which are high-frequency structural components of sentences, such as prepositions and common adjectives.
The following algorithm parses through text, extracting candidate term phrases: 1. Collect all the doublets that occur in the entire nomenclature (i.e., accumulate a list of the doublets from every term in the nomenclature).
In the novel algorithm PARSE, we overcome the limitations of PMcomp, maximizing the score of Equation (1) with the full flexibility of Sankoff's specification in terms of dependencies between alignment and predicted structures.
We describe an algorithm for parsing circuit descriptions to derive quantum equations of motion, illustrate our approach using simple examples based on linear and cavity-nonlinear optical components, and demonstrate a computational approach to hierarchical model reduction.
