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Molecular Signal Processing
Bioorganic Chemistry
Biochemistry of Plant Interactions
Cell and Metabolic Biology
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Publications
Publications - Cell and Metabolic Biology
Books and chapters
Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring substances is an important task to investigate new potentially useful chemical compounds. Multi-dimensional NMR-spectra are relational objects like documents, but consists of continuous multi-dimensional points called peaks instead of words. We develop several mappings from continuous NMR-spectra to discrete text-like data. With the help of those mappings any text retrieval method can be applied. We evaluate the performance of two retrieval methods, namely the standard vector space model and probabilistic latent semantic indexing (PLSI). PLSI learns hidden topics in the data, which is in case of 2D-NMR data interesting in its owns rights. Additionally, we develop and evaluate a simple direct similarity function, which can detect duplicates of NMR-spectra. Our experiments show that the vector space model as well as PLSI, which are both designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI is able to find meaningful ”topics” in the NMR-data.
Books and chapters
Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring products is an important task to investigate new potentially useful chemical compounds. We develop a set-based similarity function, which, however, does not sufficiently capture more abstract aspects of similarity. NMR-spectra are like documents, but consists of continuous multi-dimensional points instead of words. Probabilistic semantic indexing (PLSI) is an retrieval method, which learns hidden topics. We develop several mappings from continuous NMR-spectra to discrete text-like data. The new mappings include redundancies into the discrete data, which proofs helpful for the PLSI-model used afterwards. Our experiments show that PLSI, which is designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI combined with the new mappings is able to find meaningful ”topics” in the NMR-data.
Publications
The most abundant jasmonate-induced protein (JIP) in barley leaves is a 23 kDa protein (JIP23). Its function, however, is unknown. In order to analyze its function by homologous transformation, new plasmid vectors have been constructed. They carry the cDNA coding for JIP23 in sense or antisense orientation under the control of the Ubi-1-promoter as well as the pat resistance gene under the control of the 35S promoter. Barley mesophyll protoplasts were transiently transformed with the sense constructs. PAT activity and immunological detection of JIP23 could be achieved in transformed protoplasts but not in untransformed protoplasts indicating that the construct was active. Thus, these new vectors are suitable for stable transformation of barley. Carrying a multiple cloning site (MCS), these vectors can be used now in a wide range of transformation of barley.
This page was last modified on 27 Jan 2025 .
