EnzyMiner: automatic identification of protein level mutations and their impact on target enzymes from PubMed abstracts

BACKGROUND: A better understanding of the mechanisms of an enzyme's functionality and stability, as well as knowledge and impact of mutations is crucial for researchers working with enzymes. Though, several of the enzymes' databases are currently available, scientific literature still remains at large for up-to-date source of learning the effects of a mutation on an enzyme. However, going through vast amounts of scientific documents to extract the information on desired mutation has always been a time consuming process. In this paper, therefore, we describe an unique method, termed as EnzyMiner, which automatically identifies the PubMed abstracts that contain information on the impact of a protein level mutation on the stability and/or the activity of a given enzyme. RESULTS: We present an automated system which identifies the abstracts that contain an amino-acid-level mutation and then classifies them according to the mutation's effect on the enzyme. In the case of mutation identification, MuGeX, an automated mutation-gene extraction system has an accuracy of 93.1% with a 91.5 F-measure. For impact analysis, document classification is performed to identify the abstracts that contain a change in enzyme's stability or activity resulting from the mutation. The system was trained on lipases and tested on amylases with an accuracy of 85%. CONCLUSION: EnzyMiner identifies the abstracts that contain a protein mutation for a given enzyme and checks whether the abstract is related to a disease with the help of information extraction and machine learning techniques. For disease related abstracts, the mutation list and direct links to the abstracts are retrieved from the system and displayed on the Web. For those abstracts that are related to non-diseases, in addition to having the mutation list, the abstracts are also categorized into two groups. These two groups determine whether the mutation has an effect on the enzyme's stability or functionality followed by displaying these on the web

Spatial orientation and postural control in patients with Parkinson’s disease

Postural instability is one of the most disabling and risky symptoms of advanced Parkinson's disease (PD). The purpose of this study was to investigate whether and how this is mediated by a centrally impaired spatial orientation. Therefore, we performed a spatial orientation study in 21 PD patients (mean age: 68 years, SD: 8.5, 9 women) in a medically on condition and 21 healthy controls (mean age 68.9 years, SD 5.5 years, 14 women). We compared spatial responses to the horizontal axis (Sakashita's visual target cancellation task), the vertical axis (bucket-test), the sagittal axis (tilt table test) and postural stability using the Fullerton Advanced Balance (FAB) Scale. We found larger deviations on the vertical axis in PD patients, although the direct comparisons of performance in PD patients and healthy controls did not reveal significant differences. While the FAB Scale was significantly worse in PD (25.9 points, SD 7.2 points) compared to controls (35.1 points, SD 2.3 points, p < 0.01), the results from the spatialorientation task did not correlate with the FAB Scale. In summary, our results argue against a relation between perceptional deficits of spatial information and postural control in PD. These results are in favor of a deficit in higher order integration of spatial stimuli in PD that might influence balance control

Semistructured Data Search Evaluation

Semistructured data is of increasing importance in many application domains, but one of its core use cases is representing documents. Consequently, effectively retrieving information from semistructured documents is an important problem that has seen work from both the information retrieval (IR) and databases (DB) communities. Comparing the large number of retrieval models and systems is a non-trivial task for which established benchmark initiatives such as TREC with their focus on unstructured documents are not appropriate. This chapter gives an overview of semistructured data in general and the INEX initiative for the evaluation of XML retrieval, focusing on the most prominent Adhoc Search Track