The phrase "a collection of structural" is correct and usable in written English.
It can be used when referring to a group or assortment of structural elements, concepts, or components in various contexts, such as architecture, engineering, or data organization.
Example: "The architect presented a collection of structural designs that showcased innovative use of materials."
Alternatives: "a set of structural" or "an assortment of structural".
CNVs are a collection of structural variations within the genome that range from kilobases to megabases and are not detectable by conventional chromosomal banding [ 97].
Let these segments be qe, qe,…, qe p, p= n−ℓ+1 (qe stands for query sequence element), and denote the native structural fragments of these segments as ns, ns,…, ns p. We need to have a collection of structural fragments from which we can select the structural candidates for sequence segments.
The iDevice (instructional device) pane is a collection of structural elements that describe learning content.
For example, IRES RNAs are often part of a larger 5′ UTR and may comprise a collection of secondary structural elements, but they are named as discrete functional entities within the 5′ UTR and are identified by their source (e.g., 'HCV IRES RNA').
The path { q j } is a collection of sequential structural frames with the fixed initial q0 and final q P. Then the classical action can be written as (1) S = ∑ j = 0 P − 1 L j ({ q j } ) Δ, where the discretized Lagrangian of the jth temporal frame is defined as (2) L j = ∑ I = 1 N m I 2 Δ 2 (q I, j − q I, j + 1 ) 2 − V ({ q j } ).
Then, the time at step j can be expressed as t j = jΔ with j = 0,1, 2,…, P. The path { q j } is a collection of sequential structural frames with fixed initial q0 and final q P. The discretized action can be written as (1) S = ∑ j = 0 P − 1 L j q j Δ, where the discretized Lagrangian of jth temporal frame is defined as (2) L j q j = ∑ I = 1 N m I 2 Δ 2 q I, j − q I, j + 1 2 − V q j.
While benefiting from an extensive collection of structural data for nucleoside kinases, the creation of a predictive framework for enantioselectivity in combination with small libraries to probe the model and refine its accuracy through machine-learning algorithms has enable us to identify lead enzyme variants in a very time- and cost-effective manner.
Initially, a classifier is trained on more than four thousand molecules by data mining a selection of calculated descriptors; in a second step, the relative knowledge to complement its practice is extracted from a collection of well-known structural alerts.
The model is implemented in the CAESAR project (http://www.caesar-project.eu) [12] where initially a classifier is trained on more than four thousand molecules based on Bursi data set by using molecular descriptors, then in the next step the relative knowledge to complement its practice is extracted from a collection of well-known structural alerts.
FSL is a collection of functional and structural brain image analysis tools, written by members of the Image Analysis Group at the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB, Oxford Universityy.
These considerations are not unique to biologics, but when they are coupled with the diversity of development choices for design of drug substances and drug products, it is likely that a given set of related biosimilar products could possess a diverse and nontransitive collection of structural, pharmaceutical, and clinical characteristics.
