Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
The Conserved Exon Method for Gene Finding
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Finding Themes in Medline Documents: Probabilistic Similarity Search
ADL '00 Proceedings of the IEEE Advances in Digital Libraries 2000
Representing sentence structure in hidden Markov models for information extraction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Meta-clustering of gene expression data and literature-based information
ACM SIGKDD Explorations Newsletter
Biomedical knowledge navigation by literature clustering
Journal of Biomedical Informatics
Intelligent text processing techniques for textual-profile gene characterization
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Journal of Biomedical Informatics
Semantic web technologies for interpreting DNA microarray analyses: the MEAT system
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
Gene relation finding through mining microarray data and literature
Transactions on Computational Systems Biology V
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
| Hi-index | 0.01 |
Current genomic research has generated an immense volume of data and a tremendous increase in the number of gene-related publications. This wealth of information presents a major data analysis challenge. The ultimate goal is to understand the complex biological interrelationships among all discovered genes and proteins. Meeting this goal will require both scanning the abundant literature about each gene and plenty of human expertise. As several research groups have recently noted, automated systems for extracting relevant information from the literature can complement existing techniques, speed up analysis, and greatly enhance our understanding of genetic processes. The authors present a method, based on probabilistic information retrieval, that uses the literature to establish functional relationships among genes on a genome-wide scale. Experiments applied to documents discussing yeast genes, and a comparison of the results to well-established gene functions, demonstrate the method's effectiveness.