TY - GEN
T1 - Logical data fusion for biological hypothesis evaluation
AU - Racunas, Stephen
AU - Griffin, Christopher
PY - 2005/1/1
Y1 - 2005/1/1
N2 - We use techniques from Finite Model Theory to construct a framework for hypothesis creation and ranking to aid biologists with hypothesis evaluation and experimental design. Most bioinformatics research is geared toward pattern recognition and biological database management. Our work has some-what different aims. First, we seek to determine the structure of the space of biological hypotheses that can be composed about a given system. Second, we seek to combine a wide variety of experimental data and literature sources for use in "proofreading" such hypotheses. This data fusion problem has been a major stumbling block in modeling biological pathways. Consequently, most modeling frameworks make use of only one or two types of data, typically promoter sequences and microarray data. We present a modeling framework that is contradiction based and that performs data fusion on the logical level for an arbitrary number of sources. This greatly facilitates the incorporation of new data sources as they become available. Once a new hypothesis has been constructed, data from existing experimental databases can be fused to rank the hypothesis based on corroborating and contradictory experimental evidence. We demonstrate the logical underpinnings of this process, and show how inflationary and deflationary logical extensions alter the process.
AB - We use techniques from Finite Model Theory to construct a framework for hypothesis creation and ranking to aid biologists with hypothesis evaluation and experimental design. Most bioinformatics research is geared toward pattern recognition and biological database management. Our work has some-what different aims. First, we seek to determine the structure of the space of biological hypotheses that can be composed about a given system. Second, we seek to combine a wide variety of experimental data and literature sources for use in "proofreading" such hypotheses. This data fusion problem has been a major stumbling block in modeling biological pathways. Consequently, most modeling frameworks make use of only one or two types of data, typically promoter sequences and microarray data. We present a modeling framework that is contradiction based and that performs data fusion on the logical level for an arbitrary number of sources. This greatly facilitates the incorporation of new data sources as they become available. Once a new hypothesis has been constructed, data from existing experimental databases can be fused to rank the hypothesis based on corroborating and contradictory experimental evidence. We demonstrate the logical underpinnings of this process, and show how inflationary and deflationary logical extensions alter the process.
UR - http://www.scopus.com/inward/record.url?scp=33847120408&partnerID=8YFLogxK
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U2 - 10.1109/ICIF.2005.1592018
DO - 10.1109/ICIF.2005.1592018
M3 - Conference contribution
AN - SCOPUS:33847120408
SN - 0780392868
SN - 9780780392861
T3 - 2005 7th International Conference on Information Fusion, FUSION
SP - 1388
EP - 1395
BT - 2005 7th International Conference on Information Fusion, FUSION
PB - IEEE Computer Society
T2 - 2005 8th International Conference on Information Fusion, FUSION
Y2 - 25 July 2005 through 28 July 2005
ER -