ABSTRACT Background: Osteoid osteoma is a benign bony neoplasm and its classic treatment is surgery. In the r ecent decades percutaneous laser therapy was suggested to be replaced by surgery. In this study we have reviewed the results of the first applications of interstitial laser photocoagulation (ILP) for treatment of osteoid osteoma in Iranian patients. Materials and Methods: In this case s...

The generic task of Inductive Logic Programming (ILP) is to search a predeened subspace of rst-order logic for hypotheses that in some respect explain examples and background knowledge. In this paper we consider the development of parallel implementations of ILP systems. A rst part discusses the division of the ILP-task into subtasks that can be handled concurrently by multiple processes execut...

Inductive Logic Programming (ILP) combines both Machine Learning and Logic Programming techniques. ILP uses rst-order predicate logic restricted to Horn clauses as an underlying language. Thus, programs induced by an ILP system inherit the classical limitations of PROLOG programs. Constraint Logic Programming avoids some of the limitations of Logic Programming and so ILP aims to induce programs...

Inductive Logic Programming (ILP) is a Machine Learning technique that has been quite successful in knowledge discovery for relational domains. ILP systems implemented in Prolog challenge the limits of Prolog systems due to heavy usage of resources such as database accesses and memory usage, and to very long execution times. The major reason to implement ILP systems in Prolog is that the infere...

Intron length polymorphisms (ILPs) have been used as genetic markers in some studies. However, a systematic investigation and large-scale exploitation of ILP markers has not been reported. In this study, we performed a genome-wide search of ILPs between two subspecies (indica and japonica) in rice using the draft genomic sequences of cultivars 93-11 (indica) and Nipponbare (japonica) and 32,127...

A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive Logic Programming (ILP) algorithms are a class of machine learning algorithm that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives...

We present stable ILP, a cross-disciplinary concept straddling machine learning and nonmonotonic reasoning. Stable models give meaning to logic programs containing negative assertions. In stable ILP, we employ stable models to represent the current state speciied by (possibly) negative EDB and IDB rules. The state then serves as the background knowledge for a top-down ILP learner. We present a ...

We present stable ILP, a cross-disciplinary concept straddling machine learning and nonmonotonic reasoning. Stable models give meaning to logic programs containing negative assertions. In stable ILP, we employ stable models to represent the current state speciied by (possibly) negative EDB and IDB rules. The state then serves as the background knowledge for a top-down ILP learner. We present a ...

The weighted constraint satisfaction problem (WCSP) occurs in the crux of many real-world applications of operations research, artificial intelligence, bioinformatics, etc. Despite its importance as a combinatorial substrate, many attempts for building an efficient WCSP solver have been largely unsatisfactory. In this paper, we introduce a new method for encoding a (Boolean) WCSP instance as an...

In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest...