Database relationships

GIMs such as 1.7.2a and 1.7.2b represent a constant ATb value consistent with the group additivity assumption underlying the GCM approach. However, Tb, along with many other properties, is not generally constitutive-additive. For such cases, the query-database relationships can be presented in the following general form ... 

Primary structure peptide aird/or nucleotide sequence and the relationship between the PUB sequence and that found in the sequence database(s) StQUHS... 

The primal advantage of hierarchical databases is that the relationship between the data at the different levels is easy. The simplicity and efficiency of the data model is a great advantage of the hierarchical DBS. Large data sets (scries of measurements where the data values are dependent on different parameters such as boiling point, temperature, or pressure) could be implemented with an acceptable response time. 

The characteristic of a relational database model is the organization of data in different tables that have relationships with each other. A table is a two-dimensional consti uction of rows and columns. All the entries in one column have an equivalent meaning (c.g., name, molecular weight, etc. and represent a particular attribute of the objects (records) of the table (file) (Figure 5-9). The sequence of rows and columns in the tabic is irrelevant. Different tables (e.g., different objects with different attributes) in the same database can be related through at least one common attribute. Thus, it is possible to relate objects within tables indirectly by using a key. The range of values of an attribute is called the domain, which is defined by constraints. Schemas define and store the metadata of the database and the tables. 

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

A variety of methods have been developed by mathematicians and computer scientists to address this task, which has become known as data mining (see Chapter 9, Section 9.8). Fayyad defined and described the term data mining as the nontrivial extraction of impHcit, previously unknown and potentially useful information from data, or the search for relationships and global patterns that exist in databases [16]. In order to extract information from huge quantities of data and to gain knowledge from this information, the analysis and exploration have to be performed by automatic or semi-automatic methods. Methods applicable for data analysis are presented in Chapter 9. 

The relationship between two bond vectors can be represmted using a distance, two angles and a torsion indicated (top). To derive the data for the database all possible pairs ofexocyclic vectors are considered and ur geometric parameters calculated. 

Holiday J D, S R Ranade and P Willett 1995. A Fast Algorithm For Selecting Sets Of Dissimilar Molecule From Large Chemical Databases. Quantitative Structure-Activity Relationships 14 501-506. 

Hudson B D, R M Hyde, E Rahr, J Wood and J Osman 1996. Parameter Based Methods for Compoun Selection from Chemical Databases. Quantitative Structure-Activity Relationships 15 285-289. 

Relational databases can store unlimited numbers of results for every sample and unlimited samples for every request. The advantage of a relational DBMS over a more traditional hierarchical system, in which data sets may contain other data sets, is that the design of the database only has to consider relationships between data elements, not the number of instances for any given variable. 

EDOC, available on the Questel host from INPI, is unique among non-Japanese language databases in including information on C-stage Japanese patents, ie, those that have successfiiUy weathered the pregrant opposition period and been sealed as patents under pre-1966 patent law. It also contains some information on patent family relationships from the period long before the advent of patent family databases. 

Citation Searching. In the scholarly Hterature, authors cite earHer pubHcations that relate to the work being reported, thus a subject relationship exists between the citing and cited Hterature. This relationship has formed the basis for the Science Citation Index and related products, developed by the Institute for Scientific Information. Known as Scisearch in its on-line version, the Science Citation Index has become an important information retrieval tool in the second half of the twentieth century. It has been used for straightforward subject searching, in which mode it complements traditional indexed databases and indexes. It has also become a popular tool for hihliometric studies of various sorts, such as attempts to measure the relative impact of research carried out by different individuals or organizations, or the relative impact of pubHcations in different journals. 

Databases differ in their strengths and weaknesses, as well as in their focus. As a result, dupHcate searches carried out on different databases generally produce different results. This has been demonstrated in comparative studies of retrieval results for a group of patent databases (31,32). Participants in one study (31) made an effort to develop optimal search strategies in each database tested, yet in no instance did one file produce perfect retrieval. Both investigations (31,32) found that results from the various databases complemented each other. As a result, searchers are counseled to use multiple databases whenever possible. There is no pat answer to the question of how many files to use or which files to use however, more files mean more expenditure, and searchers must develop their own cost—benefit relationship. 

A sampling of appHcations of Kamlet-Taft LSERs include the following. (/) The Solvatochromic Parameters for Activity Coefficient Estimation (SPACE) method for infinite dilution activity coefficients where improved predictions over UNIEAC for a database of 1879 critically evaluated experimental data points has been claimed (263). (2) Observation of inverse linear relationship between log 1-octanol—water partition coefficient and Hquid... 

The second classification is the physical model. Examples are the rigorous modiiles found in chemical-process simulators. In sequential modular simulators, distillation and kinetic reactors are two important examples. Compared to relational models, physical models purport to represent the ac tual material, energy, equilibrium, and rate processes present in the unit. They rarely, however, include any equipment constraints as part of the model. Despite their complexity, adjustable parameters oearing some relation to theoiy (e.g., tray efficiency) are required such that the output is properly related to the input and specifications. These modds provide more accurate predictions of output based on input and specifications. However, the interactions between the model parameters and database parameters compromise the relationships between input and output. The nonlinearities of equipment performance are not included and, consequently, significant extrapolations result in large errors. Despite their greater complexity, they should be considered to be approximate as well. 

CA Orengo, EMC Pearl, JE Bray, AE Todd, AC Martin, L Lo Conte, JM Thornton. The GATH database provides insights into protein stmcture/function relationship. Nucleic Acids Res 27 275-279, 1999. 

Values of Cp for simple building geometries may be obtained from the British Standards Institution or from Liddament. The following relationship between wind incident angle a, building side ratio, and average surface pressure coefficient is based on the database developed by Swami and Chandra ... 

---