Mutations are structural changes in DNA that can cause protein malfunction and genetic disease. This paper describes a machine learning approach to analyzing mutations associated to Osteogenesis Imperfecta (OI), also known as brittle bone disease. We apply SORCER, a second-order rule induction system to predict clinical phenotypes of OI from mutation and neighboring amino acid sequences in the COLIA1 gene. On the average, over ten 10-fold cross-validations, SORCER gives more accurate results than C4.5 with average accuracy of about 81.2%. The paper discusses the advantages and limitations of SORCER and demonstrates its use to provide initial exploration of biological sequences.

The success of data mining techniques can be measured by the usefulness of models they produce. Often these models must be explainable as well as accurate. While decision tables are easy to interpret and explain to virtually all users, there has been little study of whether such simple models are powerful enough to use for data mining. This paper presents SORCER, a learning system that induces second-order decision tables from a given data set. Second-order tables are database relations in which rows have sets of atomic values as components. Using sets of values interpreted as disjunctions, secondorder tables provide simple and compact representations that both enhance comprehensibility and facilitate efficient management. Unlike many other learning systems, to further promote comprehensibility, SORCER attempts to generate a minimum number of rows. SORCER’s induction algorithm can be viewed as a table compression technique in which a traditional table, representing training data, is transformed into a second-order table with fewer rows by merging rows in ways that preserve consistency with the training data. We compare SORCER to three classification systems: C4.5, CBA and a Naive Bayesian classifier. Experimental results on 26 data sets show that the average error rate obtained from SORCER, using a simple compression method, is lower than those of the Naive Bayesian classifier and C4.5 and is competitive with CBA’s average error rate. Using a slightly more sophisticated compression, SORCER’s average error rate is the lowest of all.

Investigates a machine learning approach to discovering predictive relationships that can be used to integrate solar and ocean-atmospheric conditions into forecasts of regional water flows. In particular, we apply decision-tree learning and a recently developed inductive technique called "second-order table compression" to generate predictive models of future water inflows of Lake Okeechobee, a primary source of water supply for south Florida, USA. We describe SORCER (Second-Order Relation Compression for Extraction of Rules), which is a 2nd-order table compression learning system, and compare its results with those obtained from a well-established decision tree learner, C4.5. On the average, in ten 10-fold cross-validations, SORCER obtained a slightly lower error rate than C4.5. We discuss the implications of these results.

The problem of deciding whether a full join dependency (JD) ⋈ [R] and a set of functional dependencies (FDs) F imply an embedded join dependency (EJD) ⋈ [S] is known to be NP-complete. We show that the problem can be decided in polynomial time if S ⊆ R and F is embedded in R. Our work uses arguments based on an extension of complete intersection graphs rather than tableaus. This approach has facilitated our results and should prove useful for future research.