The IPB has once again been recognized for its exemplary actions in terms of equal opportunity-oriented personnel and organizational policies and has received the TOTAL E-QUALITY certification for the…
The Plant Science Student Conference (PSSC) has been organised by students from the two Leibniz institutes, IPK and IPB, every year for the last 20 years. In this interview, Christina Wäsch (IPK) and…
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.