Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Electronic Resource
    Electronic Resource
    Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL (1998)
    Springer
    Machine learning 30 (1998), S. 241-270 
    Details
    ISSN: 0885-6125
    Keywords: inductive logic programming ; pharmacophore ; structure-activity prediction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a case study of a machine-aided knowledge discovery process within the general area of drug design. Within drug design, the particular problem of pharmacophore discovery is isolated, and the Inductive Logic Programming (ILP) system progol is applied to the problem of identifying potential pharmacophores for ACE inhibition. The case study reported in this paper supports four general lessons for machine learning and knowledge discovery, as well as more specific lessons for pharmacophore discovery, for Inductive Logic Programming, and for ACE inhibition. The general lessons for machine learning and knowledge discovery are as follows. 1. An initial rediscovery step is a useful tool when approaching a new application domain. 2. General machine learning heuristics may fail to match the details of an application domain, but it may be possible to successfully apply a heuristic-based algorithm in spite of the mismatch. 3. A complete search for all plausible hypotheses can provide useful information to a user, although experimentation may be required to choose between competing hypotheses. 4. A declarative knowledge representation facilitates the development and debugging of background knowledge in collaboration with a domain expert, as well as the communication of final results.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007460424845
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    The discovery of indicator variables for QSAR using inductive logic programming (1997)
    Springer
    Journal of computer aided molecular design 11 (1997), S. 571-580 
    Details
    ISSN: 1573-4951
    Keywords: Artificial intelligence ; Machine learning ; Regression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract A central problem in forming accurate regression equations in QSAR studies isthe selection of appropriate descriptors for the compounds under study. Wedescribe a novel procedure for using inductive logic programming (ILP) todiscover new indicator variables (attributes) for QSAR problems, and show thatthese improve the accuracy of the derived regression equations. ILP techniqueshave previously been shown to work well on drug design problems where thereis a large structural component or where clear comprehensible rules arerequired. However, ILP techniques have had the disadvantage of only being ableto make qualitative predictions (e.g. active, inactive) and not to predictreal numbers (regression). We unify ILP and linear regression techniques togive a QSAR method that has the strength of ILP at describing stericstructure, with the familiarity and power of linear regression. We evaluatedthe utility of this new QSAR technique by examining the prediction ofbiological activity with and without the addition of new structural indicatorvariables formed by ILP. In three out of five datasets examined the additionof ILP variables produced statistically better results (P 〈 0.01) over theoriginal description. The new ILP variables did not increase the overallcomplexity of the derived QSAR equations and added insight into possiblemechanisms of action. We conclude that ILP can aid in the process of drugdesign.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007967728701
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Feature construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity Aided by Structural Attributes (1999)
    Springer
    Data mining and knowledge discovery 3 (1999), S. 37-57 
    Details
    ISSN: 1573-756X
    Keywords: constructive induction ; indicator variables ; ILP ; QSAR ; drug design ; scientific discovery
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Recently, computer programs developed within the field of Inductive Logic Programming (ILP) have received some attention for their ability to construct restricted first-order logic solutions using problem-specific background knowledge. Prominent applications of such programs have been concerned with determining “structure-activity” relationships in the areas of molecular biology and chemistry. Typically the task here is to predict the “activity” of a compound (for example, toxicity), from its chemical structure. A summary of the research in the area is: (a) ILP programs have largely been restricted to qualitative predictions of activity (“high”, “low” etc.); (b) When appropriate attributes are available, ILP programs have equivalent predictivity to standard quantitative analysis techniques like linear regression. However ILP programs usually perform better when such attributes are unavailable; and (c) By using structural information as background knowledge, an ILP program can provide comprehensible explanations for biological activity. This paper examines the use of ILP programs as a method of “discovering” new attributes. These attributes could then be used by methods like linear regression, thus allowing for quantitative predictions while retaining the ability to use structural information as background knowledge. Using structure-activity tasks as a test-bed, the utility of ILP programs in constructing new features was evaluated by examining the prediction of biological activity using linear regression, with and without the aid of ILP learnt logical attributes. In three out of the five data sets examined the addition of ILP attributes produced statistically better results. In addition six important structural features that have escaped the attention of the expert chemists were discovered. The method used here to construct new attributes is not specific to the problem of predicting biological activity, and the results obtained suggest a wider role for ILP programs in aiding the process of scientific discovery.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009815821645
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    A Study of Two Sampling Methods for Analyzing Large Datasets with ILP (1999)
    Springer
    Data mining and knowledge discovery 3 (1999), S. 95-123 
    Details
    ISSN: 1573-756X
    Keywords: sampling methods ; windowing ; ILP ; scaling-up
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper is concerned with problems that arise when submitting large quantities of data to analysis by an Inductive Logic Programming (ILP) system. Complexity arguments usually make it prohibitive to analyse such datasets in their entirety. We examine two schemes that allow an ILP system to construct theories by sampling from this large pool of data. The first, “subsampling”, is a single-sample design in which the utility of a potential rule is evaluated on a randomly selected sub-sample of the data. The second, “logical windowing”, is multiple-sample design that tests and sequentially includes errors made by a partially correct theory. Both schemes are derived from techniques developed to enable propositional learning methods (like decision trees) to cope with large datasets. The ILP system CProgol, equipped with each of these methods, is used to construct theories for two datasets—one artificial (a chess endgame) and the other naturally occurring (a language tagging problem). In each case, we ask the following questions of CProgol equipped with sampling: (1) Is its theory comparable in predictive accuracy to that obtained if all the data were used (that is, no sampling was employed)?; and (2) Is its theory constructed in less time than the one obtained with all the data? For the problems considered, the answers to these questions is “yes”. This suggests that an ILP program equipped with an appropriate sampling method could begin to address problems satisfactorily that have hitherto been inaccessible simply due to data extent.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009824123462
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...