| 1. |
- Bryant, C.H., et al.
(author)
-
Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes
- 2001
-
Reports (other academic/artistic)abstract
- We aim to partially automate some aspects of scientific work, namely the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. We have developed ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. We have developed a novel form of learning curve, which in contrast to the form of learning curve normally used in Active Learning, allows one to compare the costs incurred by different leaning strategies.We plan to combine ASE-Progol with a standard laboratory robot to create a general automated approach to Functional Genomics. As a first step towards this goal, we are using ASE-Progol to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. Our approach involves auxotrophic mutant trials. To date, ASE-Progol has conducted such trials in silico. However we describe how they will be performed automatically in vitro by a standard laboratory robot designed for these sorts of liquid handling tasks, namely the Beckman/Coulter Biomek 2000.Although our work to date has been limited to trials conducted in silico, the results have been encouraging. Parts of the model were removed and the ability of ASE-Progol to efficiently recover the performance of the model was measured. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy in the range 46-88% was reduced if trials were selected by ASE-Progol rather than if they were sampled at random (without replacement). To reach an accuracy in the range 46-80%, ASE-Progol incurs five orders of magnitude less experimental costs than random sampling. ASE-Progol requires less time to converge upon a hypothesis with an accuracy in the range 74-87% than if trials are sampled at random (without replacement) or selected using the naive strategy of always choosing the cheapest trial from the set of candidate trials. For example to reach an accuracy of 80%, ASE-Progol requires 4 days while random sampling requires 6 days and the naive strategy requires 10 days.
|
|
| 2. |
- Muggleton, S.H., et al.
(author)
-
Are grammatical representations useful for learning from biological sequence data? - a case study
- 2001
-
Reports (other academic/artistic)abstract
- This paper investigates whether Chomsky-like grammar representations are useful for learning cost-effective, comprehensible predictors of members of biological sequence families. The Inductive Logic Programming (ILP) Bayesian approach to learning from positive examples is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). Collectively, five of the co-authors of this paper, have extensive expertise on NPPs and general bioinformatics methods. Their motivation for generating a NPP grammar was that none of the existing bioinformatics methods could provide sufficient cost-savings during the search for new NPPs. Prior to this project experienced specialists at SmithKline Beecham had tried for many months to hand-code such a grammar but without success. Our best predictor makes the search for novel NPPs more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the ILP Bayesian approach to learning from positive examples.A group of features is derived from this grammar. Other groups of features of NPPs are derived using other learning strategies. Amalgams of these groups are formed. A recognition model is generated for each amalgam using C4.5 and C4.5rules and its performance is measured using both predictive accuracy and a new cost function, Relative Advantage ( RA ). The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives.
|
|
| 3. |
- Selpi, Selpi, 1977, et al.
(author)
-
A First Step towards Learning which uORFs Regulate Gene Expression
- 2006
-
In: Journal of integrative bioinformatics. - 1613-4516. ; 3:2, s. 31-
-
Journal article (peer-reviewed)abstract
- We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combining a bioinformatics tool, ORF Finder, with a machine learning technique, inductive logic programming (ILP). Here, we report the challenge of using ILP as part of this integrative system, in order to automatically generate a model that identifies functional uORFs. Our method makes searching for novel functional uORFs more efficient than random sampling. An attempt has been made to predict novel functional uORFs using our method. Some preliminary evidence that our model may be biologically meaningful is presented.
|
|
| 4. |
|
|
| 5. |
- Selpi, Selpi, 1977, et al.
(author)
-
Using mRNA Secondary Structure Predictions Improves Recognition of Known Yeast Functional uORFs
- 2008
-
In: Wehenkel, L., d'Alché-Buc, F., Moreau, Y. and Geurts, P. (eds.) MLSB08, The Second International Workshop on Machine Learning in Systems Biology, Brussels, 13-14 September 2008.. ; , s. 85-93
-
Conference paper (peer-reviewed)abstract
- We are interested in using inductive logic programming (ILP) to generate rules for recognising functional upstream open reading frames (uORFs) in the yeast Saccharomyces cerevisiae. This paper empirically investigates whether providing an ILP system with predicted mRNA secondary structure can increase the performance of the resulting rules. Two sets of experiments, with and without mRNA secondary structure predictions as part of thebackground knowledge, were run. For each set, stratified 10-fold cross-validation experiments were run 100 times, each time randomly permuting the order of the positive training examples, and the performance of the resulting hypotheses were measured. Our results demonstrate that the performance of an ILP system in recognising known functional uORFs in the yeast S. cerevisiae significantly increases when mRNA secondary structure predictions are added to the background knowledge and suggest that mRNA secondary structure can affect the ability of uORFs to regulate gene expression.
|
|
