Name
Abstract | ||
|---|---|---|
There are several dimensions and levels of complexity in which information on protein motifs may be available. For example, one-dimensional sequence motifs may be associated with secondary structure identifiers. Alternatively, three-dimensional information on polypeptide segments may be used to induce prototypical three-dimensional structure templates. This paper surveys various representations encountered in the protein motif discovery literature. Many of the representations are based on incompatible semantics, making difficult the comparison and combination of previous results. To make better use of machine learning techniques and to provide for an integrated knowledge representation framework, a general representation language--in which all types of motifs can be encoded and given a uniform semantics--is required. In this paper we propose such a model, called a spatial description logic, and present a machine learning approach based on the model. |
Year | Venue | Keywords |
|---|---|---|
1993 | ISMB | protein motifs,machine learning,knowledge representation,secondary structure,three dimensional,description logic,sequence motif,protein motif |
DocType | Volume | ISSN |
Conference | 1 | 1553-0833 |
ISBN | Citations | PageRank |
0-929280-47-4 | 6 | 0.85 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Darrell Conklin | 1 | 217 | 31.52 |
| Suzanne Fortier | 2 | 39 | 5.11 |
| Janice I. Glasgow | 3 | 392 | 127.97 |