A machine learning system for identifying transmembrane domains from amino acid sequences

We present our machine learning system, that uses inductive logic programming techniques to learn how to identify transmembrane domains from amino acid sequences. Our system facilitates the use of operators such as ‘contains’, that act on entire sequences, rather than on individual elements of a sequence. The prediction accuracy of our new system is around 93%, and this compares favourably with earlier results. This work was carried out with the support of a research grant from ISIS, Fujitsu Laboratories.     

Inductive logic programming for knowledge discovery from MRI data

Describes a tool for quantitatively discriminating between meningioma and astrocytoma tumors. One of the uses of magnetic resonance imaging (MRI) in clinical diagnosis is in-vivo discrimination between tumor and normal tissue and between tumor types in the brain. There is much interest in increasing the qualitative and quantitative information available from these images. This article presents a study that uses the inductive logic programming tool Progol on measurements of signal intensities in clinical scan images of 28 patients (18 with meningiomas and 10 with astrocytomas) to attempt to discover knowledge that quantitatively dissriminates between the two types of tumors     

Infinite arrays and controlled deterministic table 0L array systems

Deterministic table 0L array systems with control are considered for the generation of infinite arrays. Rewriting of a rectangular array is done in parallel by a table of rules, the rightmost edge horizontally or the lowermost edge vertically downwards. The application of the tables is controlled by a control set. Cube-free and square-free infinite arrays are obtained as an application of this model. The adherence of the array language of a controlled deterministic table 0L array system is related to the adherence of its control set. The limit language equivalence problem and the adherence equivalence problem are shown to be undecidable for this system.     

A note on an extension of matrix grammars generating two-dimensional languages

An extended version of the parallel/sequential table matrix models called extended table matrix grammars (Ex-TMG), generating arrays of symbols, is proposed. The effect of imposing different types of control on the sequence of application of the tables is studied. The notion of assigning “terminal weights” to matrix grammars for describing parquet deformations is extended to Ex-TMGs.     

Consistency and Completeness in Rough Sets

Consistency and completeness are defined in the context of rough set theory and shown to be related to the lower approximation and upper approximation, respectively. A member of a composed set (union of elementary sets) that is consistent with respect to a concept, surely belongs to the concept. An element that is not a member of a composed set that is complete with respect to a concept, surely does not belong to the concept. A consistent rule and a complete rule are useful in addition to any other rules learnt to describe a concept. When an element satisfies the consistent rule, it surely belongs to the concept, and when it does not satisfy the complete rule, it surely does not belong to the concept. In other cases, the other learnt rules are used. The results in the finite universe are extended to the infinite universe, thus introducing a rough set model for the learning from examples paradigm. The results in this paper have application in knowledge discovery or learning from database environments that are inconsistent, but at the same time demand accurate and definite knowledge. This study of consistency and completeness in rough sets also lays the foundation for related work at the intersection of rough set theory and inductive logic programming.     

The Variable Precision Rough Set Inductive Logic Programming Model and Strings

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The generic Rough Set Inductive Logic Programming (gRS-ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS-ILP model is extended in this paper to the VPRSILP model by including features of the VPRS model. The VPRSILP model is applied to strings and an illustrative experiment on transmembrane domains in amino acid sequences is presented.     

On the generative capacity of compound string and array grammars

The operation of composition is a device introduced by Abraham [1] to increase the generative capacity of regular matrix grammars. But not all families of grammars have their generative capacity increased by this operation. P?un [5] has proved that composition increases the generative capacity of linear grammars but not of regular, CF, CS or PS grammars. In this note, we show that the generative power of k-linear (k ? 1) grammars is increased by composition. In fact, it is of interest to note that the families of compound linear and compound k-linear languages are equal. Furthermore, the family of compound linear languages is an AFL, properly contained in the family of CFLs. Certain other families of string and array grammars whose generative capacities are increased by the operation of composition are also provided.     

Computer recognition of printed Tamil characters

Computer recognition of machine-printed letters of the Tamil alphabet is described. Each character is represented as a binary matrix and encoded into a string using two different methods. The encoded strings form a dictionary. A given text is presented symbol by symbol and information from each symbol is extracted in the form of a string and compared with the strings in the dictionary. When there is agreement the letters are recognized and printed out in Roman letters following a special method of transliteration. The lengthening of vowels and hardening of consonants are indicated by numerals printed above each letter.