Computational biology and bioinformatics


Ontology type: npg:Subject  | skos:Concept     

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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/ontologies/subjects/computational-biology-and-bioinformatics'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/ontologies/subjects/computational-biology-and-bioinformatics'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/ontologies/subjects/computational-biology-and-bioinformatics'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/ontologies/subjects/computational-biology-and-bioinformatics'


 

This table displays all metadata directly associated to this object as RDF triples.

569 TRIPLES      10 PREDICATES      55 URIs      7 LITERALS

Subject Predicate Object
1 sg:ontologies/subjects/computational-biology-and-bioinformatics sgo:license sg:explorer/license/
2 sgo:sdDataset onto_subjects
3 rdf:type npg:Subject
4 skos:Concept
5 rdfs:label Computational biology and bioinformatics
6 skos:altLabel Bio-Informatics
7 Bioinformatics
8 Computational Molecular Biology
9 skos:broader sg:ontologies/subjects/biological-sciences
10 skos:definition Computational biology and bioinformatics is an interdisciplinary field that develops and applies computational methods to analyse large collections of biological data, such as genetic sequences, cell populations or protein samples, to make new predictions or discover new biology. The computational methods used include analytical methods, mathematical modelling and simulation.
11 skos:inScheme sg:ontologies/subjects/
12 skos:narrower sg:ontologies/subjects/biochemical-reaction-networks
13 sg:ontologies/subjects/cellular-signalling-networks
14 sg:ontologies/subjects/classification-and-taxonomy
15 sg:ontologies/subjects/communication-and-replication
16 sg:ontologies/subjects/computational-models
17 sg:ontologies/subjects/computational-neuroscience
18 sg:ontologies/subjects/computational-platforms-and-environments
19 sg:ontologies/subjects/data-acquisition
20 sg:ontologies/subjects/data-integration
21 sg:ontologies/subjects/data-mining
22 sg:ontologies/subjects/data-processing
23 sg:ontologies/subjects/data-publication-and-archiving
24 sg:ontologies/subjects/databases
25 sg:ontologies/subjects/functional-clustering
26 sg:ontologies/subjects/gene-ontology
27 sg:ontologies/subjects/gene-regulatory-networks
28 sg:ontologies/subjects/genome-informatics
29 sg:ontologies/subjects/hardware-and-infrastructure
30 sg:ontologies/subjects/high-throughput-screening
31 sg:ontologies/subjects/image-processing
32 sg:ontologies/subjects/literature-mining
33 sg:ontologies/subjects/machine-learning
34 sg:ontologies/subjects/microarrays
35 sg:ontologies/subjects/network-topology
36 sg:ontologies/subjects/phylogeny
37 sg:ontologies/subjects/power-law
38 sg:ontologies/subjects/predictive-medicine
39 sg:ontologies/subjects/probabilistic-data-networks
40 sg:ontologies/subjects/programming-language-and-code
41 sg:ontologies/subjects/protein-analysis
42 sg:ontologies/subjects/protein-design
43 sg:ontologies/subjects/protein-folding
44 sg:ontologies/subjects/protein-function-predictions
45 sg:ontologies/subjects/protein-structure-predictions
46 sg:ontologies/subjects/proteome-informatics
47 sg:ontologies/subjects/quality-control
48 sg:ontologies/subjects/scale-invariance
49 sg:ontologies/subjects/sequence-annotation
50 sg:ontologies/subjects/software
51 sg:ontologies/subjects/standards
52 sg:ontologies/subjects/statistical-methods
53 sg:ontologies/subjects/virtual-drug-screening
54 skos:prefLabel Computational biology and bioinformatics
55 sg:ontologies/subjects/ dcterms:description The Nature Subjects Taxonomy is a polyhierarchical categorization of scholarly subject areas which are used for the indexing of content by Springer Nature.
56 dcterms:title Nature Subjects Taxonomy
57 sgo:sdDataset onto_subjects
58 rdf:type skos:ConceptScheme
59 skos:hasTopConcept sg:ontologies/subjects/DEPRECATED
60 sg:ontologies/subjects/biological-sciences
61 sg:ontologies/subjects/business-and-commerce
62 sg:ontologies/subjects/earth-and-environmental-sciences
63 sg:ontologies/subjects/health-sciences
64 sg:ontologies/subjects/humanities
65 sg:ontologies/subjects/physical-sciences
66 sg:ontologies/subjects/scientific-community-and-society
67 sg:ontologies/subjects/social-science
68 sg:ontologies/subjects/biochemical-reaction-networks sgo:sdDataset onto_subjects
69 rdf:type npg:Subject
70 skos:Concept
71 rdfs:label Biochemical reaction networks
72 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
73 skos:definition A biochemical reaction is the transformation of one molecule to a different molecule inside a cell. Biochemical reactions are mediated by enzymes, which are biological catalysts that can alter the rate and specificity of chemical reactions inside cells.
74 skos:inScheme sg:ontologies/subjects/
75 skos:prefLabel Biochemical reaction networks
76 sg:ontologies/subjects/biological-sciences sgo:sdDataset onto_subjects
77 rdf:type npg:Subject
78 skos:Concept
79 rdfs:label Biological sciences
80 skos:altLabel Biologic Science
81 Biologic Sciences
82 Biological Science
83 Biological Science Discipline
84 Biological Science Disciplines
85 Life Science
86 Life Sciences
87 skos:definition Biological sciences encompasses all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants.
88 skos:inScheme sg:ontologies/subjects/
89 skos:narrower sg:ontologies/subjects/biochemistry
90 sg:ontologies/subjects/biological-techniques
91 sg:ontologies/subjects/biophysics
92 sg:ontologies/subjects/biotechnology
93 sg:ontologies/subjects/cancer
94 sg:ontologies/subjects/cell-biology
95 sg:ontologies/subjects/chemical-biology
96 sg:ontologies/subjects/computational-biology-and-bioinformatics
97 sg:ontologies/subjects/developmental-biology
98 sg:ontologies/subjects/drug-discovery
99 sg:ontologies/subjects/ecology
100 sg:ontologies/subjects/evolution
101 sg:ontologies/subjects/genetics
102 sg:ontologies/subjects/immunology
103 sg:ontologies/subjects/microbiology
104 sg:ontologies/subjects/molecular-biology
105 sg:ontologies/subjects/neuroscience
106 sg:ontologies/subjects/physiology
107 sg:ontologies/subjects/plant-sciences
108 sg:ontologies/subjects/psychology
109 sg:ontologies/subjects/stem-cells
110 sg:ontologies/subjects/structural-biology
111 sg:ontologies/subjects/systems-biology
112 sg:ontologies/subjects/zoology
113 skos:prefLabel Biological sciences
114 skos:topConceptOf sg:ontologies/subjects/
115 sg:ontologies/subjects/cellular-signalling-networks sgo:sdDataset onto_subjects
116 rdf:type npg:Subject
117 skos:Concept
118 rdfs:label Cellular signalling networks
119 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
120 skos:definition Cellular signalling networks are formed when different cell signalling pathways interact and are detected by a combination of experimental and computational methods. Understanding how a biological system responds to a signal can be deduced by analysing how multiple cell signalling pathways affect each other and, in turn, cellular processes within the network.
121 skos:inScheme sg:ontologies/subjects/
122 skos:prefLabel Cellular signalling networks
123 sg:ontologies/subjects/classification-and-taxonomy sgo:sdDataset onto_subjects
124 rdf:type npg:Subject
125 skos:Concept
126 rdfs:label Classification and taxonomy
127 skos:altLabel Classification
128 Classifications
129 Systematics
130 Taxonomies
131 Taxonomy
132 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
133 skos:definition The discipline of classification and taxonomy involves the grouping of organisms into categories based on different properties including size, shape and gene sequences. Classification can help to identify evolutionary relationships among organisms.
134 skos:inScheme sg:ontologies/subjects/
135 skos:prefLabel Classification and taxonomy
136 sg:ontologies/subjects/communication-and-replication sgo:sdDataset onto_subjects
137 rdf:type npg:Subject
138 skos:Concept
139 rdfs:label Communication and replication
140 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
141 skos:definition Communication is the transfer of data between data sources and data sinks, and replication is the copying of data from one server to another. Communication and replication are important in database maintenance and preservation of data.
142 skos:inScheme sg:ontologies/subjects/
143 skos:prefLabel Communication and replication
144 sg:ontologies/subjects/computational-models sgo:sdDataset onto_subjects
145 rdf:type npg:Subject
146 skos:Concept
147 rdfs:label Computational models
148 skos:altLabel Mathematical modelling
149 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
150 skos:definition Computational models are mathematical models that are simulated using computation to study complex systems. In biology, one example is the use of a computational model to study an outbreak of an infectious disease such as influenza. The parameters of the mathematical model are adjusted using computer simulation to study different possible outcomes.
151 skos:inScheme sg:ontologies/subjects/
152 skos:prefLabel Computational models
153 sg:ontologies/subjects/computational-neuroscience sgo:sdDataset onto_subjects
154 rdf:type npg:Subject
155 skos:Concept
156 rdfs:label Computational neuroscience
157 skos:altLabel Neuroinformatics
158 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
159 sg:ontologies/subjects/neuroscience
160 skos:definition Computational neuroscience is the field of study in which mathematical tools and theories are used to investigate brain function. It can also incorporate diverse approaches from electrical engineering, computer science and physics in order to understand how the nervous system processes information.
161 skos:inScheme sg:ontologies/subjects/
162 skos:narrower sg:ontologies/subjects/biophysical-models
163 sg:ontologies/subjects/dynamical-systems
164 sg:ontologies/subjects/learning-algorithms
165 sg:ontologies/subjects/network-models
166 sg:ontologies/subjects/neural-decoding
167 sg:ontologies/subjects/neural-encoding
168 skos:prefLabel Computational neuroscience
169 sg:ontologies/subjects/computational-platforms-and-environments sgo:sdDataset onto_subjects
170 rdf:type npg:Subject
171 skos:Concept
172 rdfs:label Computational platforms and environments
173 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
174 skos:definition Computational platforms and environments are platforms of integrated software that have user-friendly interfaces and that can be used by researchers to properly analyse data, such as large datasets, and answer research questions.
175 skos:inScheme sg:ontologies/subjects/
176 skos:prefLabel Computational platforms and environments
177 sg:ontologies/subjects/data-acquisition sgo:sdDataset onto_subjects
178 rdf:type npg:Subject
179 skos:Concept
180 rdfs:label Data acquisition
181 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
182 skos:definition Data acquisition is the process of converting measurements, such as temperature, pressure, relative humidity, light, resistance, current, power, speed and vibration, into digital numeric values that can be manipulated by a computer. During data acquisition physical measurements are converted into a voltage that is in turn transformed into a binary number by a digital-to-analog converter.
183 skos:inScheme sg:ontologies/subjects/
184 skos:prefLabel Data acquisition
185 sg:ontologies/subjects/data-integration sgo:sdDataset onto_subjects
186 rdf:type npg:Subject
187 skos:Concept
188 rdfs:label Data integration
189 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
190 skos:definition Data integration is the process of combining data generated using a variety of different research methods in order to enable detection of underlying themes and, in computational biology and bioinformatics, biological principles. Data integration is important in biology owing to the large and different 'omics' datasets now available.
191 skos:inScheme sg:ontologies/subjects/
192 skos:prefLabel Data integration
193 sg:ontologies/subjects/data-mining sgo:sdDataset onto_subjects
194 rdf:type npg:Subject
195 skos:Concept
196 rdfs:label Data mining
197 skos:altLabel Text Mining
198 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
199 skos:definition Data mining is the process of extracting potentially useful information from data sets. It uses a suite of methods to organise, examine and combine large data sets, including machine learning, visualisation methods and statistical analyses. Data mining is used in computational biology and bioinformatics to detect trends or patterns without knowledge of the meaning of the data.
200 skos:inScheme sg:ontologies/subjects/
201 skos:prefLabel Data mining
202 sg:ontologies/subjects/data-processing sgo:sdDataset onto_subjects
203 rdf:type npg:Subject
204 skos:Concept
205 rdfs:label Data processing
206 skos:altLabel Automatic Data Processing
207 Automatic Information Processing
208 Bar Code
209 Bar Codes
210 Computer Data Processing
211 Data File
212 Data Files
213 Electronic Data Processing
214 Information Processing
215 Machine Readable Data Files
216 Machine-Readable Data File
217 Machine-Readable Data Files
218 Optical Reader
219 Optical Readers
220 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
221 skos:definition Data processing is a set of methods that are used to input, retrieve, verify, store, organize, analyse or interpret a set of data. Data processing enables information to be automatically extracted from data, and could be used in computational biology and bioinformatics to organise a large set of 'omics data.
222 skos:inScheme sg:ontologies/subjects/
223 skos:prefLabel Data processing
224 sg:ontologies/subjects/data-publication-and-archiving sgo:sdDataset onto_subjects
225 rdf:type npg:Subject
226 skos:Concept
227 rdfs:label Data publication and archiving
228 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
229 skos:definition Data publication is the process of making information, particularly data generated from research, available to all. Data archiving is the long term storage of such data and methods. In science, publishing and archiving data is important to preserve scientific information for future research.
230 skos:inScheme sg:ontologies/subjects/
231 skos:prefLabel Data publication and archiving
232 sg:ontologies/subjects/databases sgo:sdDataset onto_subjects
233 rdf:type npg:Subject
234 skos:Concept
235 rdfs:label Databases
236 skos:altLabel Databanks as Topic
237 Databanks as Topics
238 Databases as Topic
239 Databases as Topics
240 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
241 sg:ontologies/subjects/publication-characteristics
242 sg:ontologies/subjects/research-data
243 skos:definition A database is one or more sets of data, for example numbers, characters and images, bundled together with software that enables data to be added, removed or retrieved. Databases can be used to store research data, for example in protein databases and genetic databases, and they organise data into standard formats so that information can readily be obtained.
244 skos:inScheme sg:ontologies/subjects/
245 skos:narrower sg:ontologies/subjects/genetic-databases
246 sg:ontologies/subjects/protein-databases
247 skos:prefLabel Databases
248 sg:ontologies/subjects/functional-clustering sgo:sdDataset onto_subjects
249 rdf:type npg:Subject
250 skos:Concept
251 rdfs:label Functional clustering
252 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
253 skos:definition Functional clustering is a computational technique that groups samples, for instance proteins, into clusters with similar functions. Clustering is the first step in forming a network of functions, and can help to identify new connections between, for example, different proteins in a cell.
254 skos:inScheme sg:ontologies/subjects/
255 skos:prefLabel Functional clustering
256 sg:ontologies/subjects/gene-ontology sgo:sdDataset onto_subjects
257 rdf:type npg:Subject
258 skos:Concept
259 rdfs:label Gene ontology
260 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
261 skos:definition The Gene Ontology (GO) project is a bioinformatics initiative that provides an ontology (shared vocabulary) of defined terms to represent specific gene product properties. The use of controlled terms from the GO means that computers can be used to analyse relationships between gene products, which in turn can reveal previously unknown functions.
262 skos:inScheme sg:ontologies/subjects/
263 skos:prefLabel Gene ontology
264 sg:ontologies/subjects/gene-regulatory-networks sgo:sdDataset onto_subjects
265 rdf:type npg:Subject
266 skos:Concept
267 rdfs:label Gene regulatory networks
268 skos:altLabel Gene Circuit
269 Gene Circuits
270 Gene Module
271 Gene Modules
272 Gene Network
273 Gene Networks
274 Gene Regulatory Network
275 Transcriptional Network
276 Transcriptional Networks
277 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
278 skos:definition A gene regulatory network is a set of genes, or parts of genes, that interact with each other to control a specific cell function. Gene regulatory networks are important in development, differentiation and responding to environmental cues.
279 skos:inScheme sg:ontologies/subjects/
280 skos:prefLabel Gene regulatory networks
281 sg:ontologies/subjects/genome-informatics sgo:sdDataset onto_subjects
282 rdf:type npg:Subject
283 skos:Concept
284 rdfs:label Genome informatics
285 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
286 skos:definition Genome informatics is the field in which computer and statistical techniques are applied to derive biological information from genome sequences. Genome informatics includes methods to analyse DNA sequence information and to predict protein sequence and structure.
287 skos:inScheme sg:ontologies/subjects/
288 skos:narrower sg:ontologies/subjects/genome-assembly-algorithms
289 skos:prefLabel Genome informatics
290 sg:ontologies/subjects/hardware-and-infrastructure sgo:sdDataset onto_subjects
291 rdf:type npg:Subject
292 skos:Concept
293 rdfs:label Hardware and infrastructure
294 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
295 skos:definition Hardware comprises the physical parts of a computer that can be touched, and infrastructure comprises the physical hardware used to connect computers to other computers and users.
296 skos:inScheme sg:ontologies/subjects/
297 skos:prefLabel Hardware and infrastructure
298 sg:ontologies/subjects/high-throughput-screening sgo:sdDataset onto_subjects
299 rdf:type npg:Subject
300 skos:Concept
301 rdfs:label High-throughput screening
302 skos:altLabel High Throughput Biological Assays
303 High Throughput Chemical Assays
304 High Throughput Screening Assays
305 High Throughput Screening Methods
306 High-Throughput Biological Assay
307 High-Throughput Biological Assays
308 High-Throughput Chemical Assay
309 High-Throughput Chemical Assays
310 High-Throughput Screening Assay
311 High-Throughput Screening Assays
312 High-Throughput Screening Method
313 High-Throughput Screening Methods
314 skos:broader sg:ontologies/subjects/biological-techniques
315 sg:ontologies/subjects/computational-biology-and-bioinformatics
316 sg:ontologies/subjects/cytological-techniques
317 sg:ontologies/subjects/drug-screening
318 skos:definition High-throughput screening methods provide efficient measurement of the effects of agents or conditions in biological or chemical assays. These methods often require robotics, imaging and computation to increase the scale and speed of assays.
319 skos:inScheme sg:ontologies/subjects/
320 skos:prefLabel High-throughput screening
321 sg:ontologies/subjects/image-processing sgo:sdDataset onto_subjects
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323 skos:Concept
324 rdfs:label Image processing
325 skos:altLabel Computer Assisted Image Analysis
326 Computer Assisted Image Processing
327 Computer-Assisted Image Analyses
328 Computer-Assisted Image Analysis
329 Computer-Assisted Image Processing
330 Image Reconstruction
331 Image Reconstructions
332 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
333 skos:definition Image processing is manipulation of an image that has been digitised and uploaded into a computer. Software programs modify the image to make it more useful, and can for example be used to enable image recognition.
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340 skos:altLabel Text mining
341 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
342 skos:definition Literature mining is a specialised data mining method that is used to extract information (facts or data) from text, such as the scientific literature. Literature mining can generate new hypotheses by systematically scrutinising huge numbers of abstracts, or full text versions, of scientific publications.
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344 skos:prefLabel Literature mining
345 sg:ontologies/subjects/machine-learning sgo:sdDataset onto_subjects
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348 rdfs:label Machine learning
349 skos:altLabel AI (Artificial Intelligence)
350 AIs (Artificial Intelligence)
351 Artificial Intelligence
352 Computer Reasoning
353 Computer Vision System
354 Computer Vision Systems
355 Knowledge Acquisition (Computer)
356 Knowledge Acquisitions (Computer)
357 Knowledge Representation (Computer)
358 Knowledge Representations (Computer)
359 Machine Intelligence
360 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
361 skos:definition Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.
362 skos:inScheme sg:ontologies/subjects/
363 skos:prefLabel Machine learning
364 sg:ontologies/subjects/microarrays sgo:sdDataset onto_subjects
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366 skos:Concept
367 rdfs:label Microarrays
368 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
369 skos:definition A microarray is a set of samples, for example DNA, RNA or proteins, arranged on a solid substrate or chip, such as a glass slide or silicon film, that is used in high throughput experiments. Microarrays enable multiple simultaneous measurements to be made in one experiment.
370 skos:inScheme sg:ontologies/subjects/
371 skos:prefLabel Microarrays
372 sg:ontologies/subjects/network-topology sgo:sdDataset onto_subjects
373 rdf:type npg:Subject
374 skos:Concept
375 rdfs:label Network topology
376 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
377 skos:definition Network topology is the "shape" of a network of biological interactions and shows how the nodes — the parts of the network where elements interact — are connected to each other. Common topologies that are found in biological networks include the bus network, star network and ring network.
378 skos:inScheme sg:ontologies/subjects/
379 skos:prefLabel Network topology
380 sg:ontologies/subjects/phylogeny sgo:sdDataset onto_subjects
381 rdf:type npg:Subject
382 skos:Concept
383 rdfs:label Phylogeny
384 skos:altLabel Phylogenies
385 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
386 skos:definition A phylogeny is a hypothetical reconstruction of the evolutionary relationships of a group of organisms or a set of sequences (nucleotide or amino acid). Phylogenies are often represented graphically in the form of a 'tree' and enable scientists to find new relationships between organisms.
387 skos:inScheme sg:ontologies/subjects/
388 skos:prefLabel Phylogeny
389 sg:ontologies/subjects/power-law sgo:sdDataset onto_subjects
390 rdf:type npg:Subject
391 skos:Concept
392 rdfs:label Power law
393 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
394 skos:definition A power law is a special mathematical relationship between two quantities in which one quantity varies as a power of the other. Numerous examples of power laws occur in nature, for example, the frequency of any word in a language is inversely proportional to its ranking in a frequency table.
395 skos:inScheme sg:ontologies/subjects/
396 skos:prefLabel Power law
397 sg:ontologies/subjects/predictive-medicine sgo:sdDataset onto_subjects
398 rdf:type npg:Subject
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400 rdfs:label Predictive medicine
401 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
402 skos:definition Predictive medicine is a branch of medicine that aims to identify patients at risk of developing a disease, thereby enabling either prevention or early treatment of that disease. Either single or more commonly multiple analyses are used to identify markers of future disposition to a disease.
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404 skos:prefLabel Predictive medicine
405 sg:ontologies/subjects/probabilistic-data-networks sgo:sdDataset onto_subjects
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407 skos:Concept
408 rdfs:label Probabilistic data networks
409 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
410 skos:definition Probabilistic data networks represent sets of uncertain data whose behaviour cannot exactly be predicted. The likeliness of any version of the network being true is associated with a probability.
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412 skos:prefLabel Probabilistic data networks
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417 skos:altLabel Programming Languages
418 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
419 sg:ontologies/subjects/systems-biology
420 skos:definition A programming language is a set of symbols whose strings are governed by rules apt to communicate instructions to a particular machine. Such strings may be concatenated into longer code and implement abstract algorithms in the form of programs specific to actual computing devices.
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423 sg:ontologies/subjects/protein-analysis sgo:sdDataset onto_subjects
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426 rdfs:label Protein analysis
427 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
428 skos:definition Protein analysis is the bioinformatic study of protein structure and function using database searches, sequence comparisons, structural and functional predictions.
429 skos:inScheme sg:ontologies/subjects/
430 skos:narrower sg:ontologies/subjects/protein-array-analysis
431 sg:ontologies/subjects/protein-sequence-analyses
432 skos:prefLabel Protein analysis
433 sg:ontologies/subjects/protein-design sgo:sdDataset onto_subjects
434 rdf:type npg:Subject
435 skos:Concept
436 rdfs:label Protein design
437 skos:altLabel Directed Evolution
438 Protein Engineering
439 Protein Evolution
440 Rational Design
441 skos:broader sg:ontologies/subjects/chemical-biology
442 sg:ontologies/subjects/computational-biology-and-bioinformatics
443 sg:ontologies/subjects/molecular-engineering
444 skos:definition Protein design (or protein engineering) is a technique by which proteins with enhanced or novel functional properties are created. Proteins can be engineered by rational design, which typically uses computational tools to identify useful mutations, or by directed evolution, which uses random mutagenesis coupled with a selection process to identify desired variants.
445 skos:inScheme sg:ontologies/subjects/
446 skos:prefLabel Protein design
447 sg:ontologies/subjects/protein-folding sgo:sdDataset onto_subjects
448 rdf:type npg:Subject
449 skos:Concept
450 rdfs:label Protein folding
451 skos:altLabel Globular Protein Folding
452 Globular Protein Foldings
453 Protein Foldings
454 skos:broader sg:ontologies/subjects/biochemistry
455 sg:ontologies/subjects/cell-biology
456 sg:ontologies/subjects/chemical-biology
457 sg:ontologies/subjects/computational-biology-and-bioinformatics
458 sg:ontologies/subjects/molecular-biology
459 skos:definition Protein folding is the process by which proteins achieve their mature functional (native) tertiary structure, and often begins co-translationally. Protein folding requires chaperones and often involves stepwise establishment of regular secondary and supersecondary structures, namely α-helices and β-sheets, that fold rapidly, stabilized by hydrogen bonding and disulphide bridges, and then tertiary structure.
460 skos:inScheme sg:ontologies/subjects/
461 skos:narrower sg:ontologies/subjects/chaperones
462 sg:ontologies/subjects/endoplasmic-reticulum
463 sg:ontologies/subjects/prions
464 sg:ontologies/subjects/protein-aggregation
465 skos:prefLabel Protein folding
466 sg:ontologies/subjects/protein-function-predictions sgo:sdDataset onto_subjects
467 rdf:type npg:Subject
468 skos:Concept
469 rdfs:label Protein function predictions
470 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
471 skos:definition Protein function predictions are bioinformatic methods (i.e. involving computation using software) that aim to predict the function of a protein. Protein function analyses use protein sequences and structures as inputs into software that generates experimentally testable predictions of function.
472 skos:inScheme sg:ontologies/subjects/
473 skos:prefLabel Protein function predictions
474 sg:ontologies/subjects/protein-structure-predictions sgo:sdDataset onto_subjects
475 rdf:type npg:Subject
476 skos:Concept
477 rdfs:label Protein structure predictions
478 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
479 skos:definition Protein structure predictions are bioinformatic analyses that produce predicted protein structures automatically using the protein amino acid sequence. Protein structures are important because structural features can shed light on biological functions.
480 skos:inScheme sg:ontologies/subjects/
481 skos:prefLabel Protein structure predictions
482 sg:ontologies/subjects/proteome-informatics sgo:sdDataset onto_subjects
483 rdf:type npg:Subject
484 skos:Concept
485 rdfs:label Proteome informatics
486 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
487 skos:definition The proteome is the entire complement of proteins that is or can be expressed by a cell, tissue, or organism. Proteome informatics is a set of bioinformatic methods (i.e. methods for collecting and analysing complex datasets) that can be used to analyse the outputs of experiments that determine the proteome of a cell, tissue, organ or organism.
488 skos:inScheme sg:ontologies/subjects/
489 skos:prefLabel Proteome informatics
490 sg:ontologies/subjects/quality-control sgo:sdDataset onto_subjects
491 rdf:type npg:Subject
492 skos:Concept
493 rdfs:label Quality control
494 skos:altLabel Quality Controls
495 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
496 skos:definition Quality control is a set of methods for maintaining standards in production lines or in a process. Quality control can be applied to methods, equipment, processes or products and usually involves spot tests or products or procedures to ensure that standards are maintained.
497 skos:inScheme sg:ontologies/subjects/
498 skos:prefLabel Quality control
499 sg:ontologies/subjects/scale-invariance sgo:sdDataset onto_subjects
500 rdf:type npg:Subject
501 skos:Concept
502 rdfs:label Scale invariance
503 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
504 skos:definition Scale invariance is a term used in mathematics, economics and physics and is a feature of an object that does not change if all scales in the object are multiplied by a common factor.
505 skos:inScheme sg:ontologies/subjects/
506 skos:prefLabel Scale invariance
507 sg:ontologies/subjects/sequence-annotation sgo:sdDataset onto_subjects
508 rdf:type npg:Subject
509 skos:Concept
510 rdfs:label Sequence annotation
511 skos:altLabel Gene Annotation
512 Gene Annotations
513 Molecular Sequence Annotation
514 Molecular Sequence Annotations
515 Protein Annotation
516 Protein Annotations
517 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
518 sg:ontologies/subjects/sequencing
519 skos:definition Sequence annotation is the process of marking specific features in a DNA, RNA or protein sequence with descriptive information about structure or function.
520 skos:inScheme sg:ontologies/subjects/
521 skos:prefLabel Sequence annotation
522 sg:ontologies/subjects/software sgo:sdDataset onto_subjects
523 rdf:type npg:Subject
524 skos:Concept
525 rdfs:label Software
526 skos:altLabel Computer Program
527 Computer Programs
528 Computer Programs and Programming
529 Computer Software
530 Software Engineering
531 Software Tool
532 Software Tools
533 skos:broader sg:ontologies/subjects/biological-techniques
534 sg:ontologies/subjects/computational-biology-and-bioinformatics
535 sg:ontologies/subjects/mathematics-and-computing
536 sg:ontologies/subjects/systems-biology
537 skos:definition Software is a set of computer instructions and can refer to executable programs, scripts and libraries.
538 skos:inScheme sg:ontologies/subjects/
539 skos:prefLabel Software
540 sg:ontologies/subjects/standards sgo:sdDataset onto_subjects
541 rdf:type npg:Subject
542 skos:Concept
543 rdfs:label Standards
544 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
545 skos:definition Standards in research refer to a specified level of quality or achievement. For example, in medical research, reporting standards have been drawn up to ensure authors of publications report everything necessary for a full understanding and replication of published research. Standards can also include agreed nomenclature or identifiers.
546 skos:inScheme sg:ontologies/subjects/
547 skos:prefLabel Standards
548 sg:ontologies/subjects/statistical-methods sgo:sdDataset onto_subjects
549 rdf:type npg:Subject
550 skos:Concept
551 rdfs:label Statistical methods
552 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
553 skos:definition Statistical methods are mathematical formulas, models, and techniques that are used in statistical analysis of raw research data. The application of statistical methods extracts information from research data and provides different ways to assess the robustness of research outputs.
554 skos:inScheme sg:ontologies/subjects/
555 skos:prefLabel Statistical methods
556 sg:ontologies/subjects/virtual-drug-screening sgo:sdDataset onto_subjects
557 rdf:type npg:Subject
558 skos:Concept
559 rdfs:label Virtual drug screening
560 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
561 sg:ontologies/subjects/molecular-engineering
562 skos:definition Virtual drug screening is a computational approach to predict drug activity by fitting chemical structures to targets. This type of screening is commonly used to rapidly test a library of putative drugs for their potential to bind and inhibit receptor or enzyme targets.
563 skos:inScheme sg:ontologies/subjects/
564 skos:prefLabel Virtual drug screening
565 skos:Concept sgo:sdDataset for_codes
566 rdf:type rdfs:Class
567 rdfs:Resource
568 rdfs:subClassOf rdfs:Resource
569 skos:Concept
 




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