| 1. |
- Bodén, Mikael, et al.
(författare)
-
A Connectionist Model of Nonmonotonic Reasoning : Handling Exceptions in Inheritance Hierarchies
- 1992
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Nonmonotonic reasoning is a core problem in AI. An example of nonmonotonic reasoning is the type of default reasoning which occurs with inheritance structures which allow exceptions. This paper describes a connectionist model of a hierarchical inheritance structure with exceptions. Existing symbolic and related connectionist research are described, and their limitations summarized. The requirements for an adaptable connectionist model are laid out, and a representational architecture is constructed. The architecture requires relations (i.e. links between objects) to be bi-directional or directional, where the former is meant to capture those relations for which it is useful to have the inverse relation (e.g. `isa', `part-of'). The general assumption is that inferential distances are best captured by relying on representational similarities in the semantic features of tokens and types. Both the encoding mechanism and the decoding mechanism (for checking the uniqueness of the distributed representations) are described in detail. The representational architecture is implemented in recursive autoassociative memory. The model is successful, and future adaptation or handling multiple inheritance with exceptions is briefly explored.
|
|
| 2. |
- Bodén, Mikael, et al.
(författare)
-
A Representational Architecture for Nonmonotonic Inheritance Structures
- 1993
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper describes a connectionist system for representing and reasoning with multiple inheritance structures with exceptions. The representational architecture has three characteristics. First, it merges relational with taxonomic representations. Secondly, it handles conflicts generated by exceptions and the use of multiple superclasses. Thirdly, it uses fully distributed representations. One novel feature is that, since the distributed representation of an entity is influenced by its position in the inheritance structure, representations of assertions are influenced by the context of the entities. An extension to the model which implements and makes use of confluent inference is described.
|
|
| 3. |
- Narayanan, Ajit, et al.
(författare)
-
Artificial intelligence techniques for bioinformatics
- 2002
-
Ingår i: Applied Bioinformatics. - 1175-5636. ; 1:4, s. 191-222
-
Forskningsöversikt (refereegranskat)abstract
- This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Narayanan, Ajit, et al.
(författare)
-
Single Layer Artificial Neural Networks for Gene Expression Analysis
- 2004
-
Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 61, s. 217-240
-
Tidskriftsartikel (refereegranskat)abstract
- Gene expression datasets are being produced in increasing quantities and made available on the web. Several thousands of genes are usually measured for their mRNA expression levels per sample using Affymetrix gene chips and Stanford microarrays, for instance. Such datasets are normally separated into distinct, objectively measured classes, typically disease states or other objectively measured phenotypes. A major problem for current gene expression analysis is, given the disparity between the number of genes measured (typically, thousands) and number of individuals sampled (typically, dozens), how to identify the handful of genes which, individually or in combination, help classify individuals. Previous approaches when faced with the dimensionality of the problem have tended to use unsupervised or supervised techniques that result in smaller clusters of genes, but clusters by themselves do not yield classification rules. This is especially the case with temporal microarray data, which represents the activity of genes within a cell, tissue or organism over time. The expression levels of some genes at a particular time-point can be controlled by the expression levels of other genes at a previous time-point. It is the extraction of these temporal connections within the data that is of great interest to biomolecular scientists and researchers within the pharmaceutical industry. If these so-called gene networks can be found that explain disease inception and progression, drugs can be designed to target specific genes so that the disease either does not progress or is even eradicated from an individual. In this paper we describe novel experiments using single-layer artificial neural networks for modelling both non-temporal (classificatory) and temporal microarray data.
|
|
