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  • 1
    Electronic Resource
    Electronic Resource
    Overhauling the PDB (2007)
    [s.l.] : Nature Publishing Group
    Nature biotechnology 25 (2007), S. 437-442 
    Details
    ISSN: 1546-1696
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: [Auszug] Although structural biology was once a leader in both the development of standards for the preservation and sharing of scientific data and for database development, this lead has been lost. The main data standard used by PDB, mmCIF, does not meet state-of-the-art standards in biology for ...
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1038/nbt0407-437
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  • 2
    Electronic Resource
    Electronic Resource
    Are the current ontologies in biology good ontologies? (2005)
    [s.l.] : Nature Publishing Group
    Nature biotechnology 23 (2005), S. 1095-1098 
    Details
    ISSN: 1546-1696
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: [Auszug] “Progress will still need to be driven by the logic of genetics and by further increases in abstraction.” Edward B. Lewis, Nobel lecture The research histories of both biology and ontologies originate with the philosopher Aristotle. For 2,400 years, the two subjects have taken separate ...
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1038/nbt0905-1095
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  • 3
    Electronic Resource
    Electronic Resource
    Functional genomic hypothesis generation and experimentation by a robot scientist (2004)
    [s.l.] : Macmillian Magazines Ltd.
    Nature 427 (2004), S. 247-252 
    Details
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented ...
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1038/nature02236
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  • 4
    Electronic Resource
    Electronic Resource
    Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines (1994)
    Springer
    Journal of computer aided molecular design 8 (1994), S. 421-432 
    Details
    ISSN: 1573-4951
    Keywords: QSAR ; Artificial intelligence ; Neural networks ; DHFR inhibitors
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR) — the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives — has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00125376
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  • 5
    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
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  • 6
    Electronic Resource
    Electronic Resource
    Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines (1994)
    Springer
    Journal of computer aided molecular design 8 (1994), S. 405-420 
    Details
    ISSN: 1573-4951
    Keywords: QSAR ; Artificial intelligence ; Neural networks ; DHFR inhibitors
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substitured benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00125375
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  • 7
    Electronic Resource
    Electronic Resource
    New approaches to QSAR: Neural networks and machine learning (1993)
    Springer
    Perspectives in drug discovery and design 1 (1993), S. 279-290 
    Details
    ISSN: 1573-9023
    Keywords: Artificial intelligence ; Drug design
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary Neural networks and machine learning are two methods that are increasingly being used to model QSARs. They make few statistical assumptions and are nonlinear and nonparametric. We describe back-propagation from the field of neural networks, and GOLEM from machine learning, and illustrate their learning mechanisms using a simple expository problem. Back-propagation and GOLEM are then compared with multiple linear regression (using the parameters and their squares) on two real drug design problems: the inhibition ofEscherichia coli dihydrofolate reductase (DHFR) by pyrimidines and the inhibition of rat/mouse tumour DHFR by triazines.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02174529
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  • 8
    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
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  • 9
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    Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast (2019)
    National Academy of Sciences
    Details
    Publication Date: 2019-08-16
    Description: One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast (Saccharomyces cerevisiae) diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles.
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 10
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    Rise of the Robo Scientists (2011)
    Springer Nature
    Details
    Publication Date: 2011-01-01
    Print ISSN: 0036-8733
    Electronic ISSN: 1946-7087
    Topics: Biology , Natural Sciences in General , Physics
    Published by Springer Nature
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