Cat Spirits in North-western China: Worship Practices, Origin, and External Relations

This paper examines the cult of cat spirits in north-western China and their veneration by the Han Chinese, Tibetans, and Monguors. These spirits are revered as family spirits and guardians of wealth and property, but possess resentful and revengeful personalities. The paper explores the origins of the cult, local worship and summoning practices, protection methods, and links with other vernacular traditions in the region. The study uses a combination of research methods, including analysis of Chinese historical sources, published modern narratives, and the authors’ own fieldwork in Mongolia. The paper employs a qualitative and comparativeapproach to identify invariant features of cat spirits across various local traditions and highlights the assimilation of the cult into different traditional belief systems where it is enriched with new traits. The paper sheds light on the rich and complex tapestry of beliefs and practices associated with cat spirits. The articlesuggests that the cult of cat spirits may have had non-Han and non-Tibetan origins, possibly connected to Proto-Mongolic tribes

Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks

<p>Abstract</p> <p>Background</p> <p>Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets.</p> <p>Results</p> <p>We developed a method for automatic extraction of protein functional annotation from scientific text based on the Natural Language Processing (NLP) technology. For the protein annotation extracted from the entire PubMed, we evaluated the precision and recall rates, and compared the performance of the automatic extraction technology to that of manual curation used in public Gene Ontology (GO) annotation. In the second part of our presentation, we reported a large-scale investigation into the correspondence between communities in the literature-based protein networks and GO annotation groups of functionally related proteins. We found a comprehensive two-way match: proteins within biological annotation groups form significantly denser linked network clusters than expected by chance and, conversely, densely linked network communities exhibit a pronounced non-random overlap with GO groups. We also expanded the publicly available GO biological process annotation using the relations extracted by our NLP technology. An increase in the number and size of GO groups without any noticeable decrease of the link density within the groups indicated that this expansion significantly broadens the public GO annotation without diluting its quality. We revealed that functional GO annotation correlates mostly with clustering in a physical interaction protein network, while its overlap with indirect regulatory network communities is two to three times smaller.</p> <p>Conclusion</p> <p>Protein functional annotations extracted by the NLP technology expand and enrich the existing GO annotation system. The GO functional modularity correlates mostly with the clustering in the physical interaction network, suggesting that the essential role of structural organization maintained by these interactions. Reciprocally, clustering of proteins in physical interaction networks can serve as an evidence for their functional similarity.</p

Automatic pathway building in biological association networks

BACKGROUND: Scientific literature is a source of the most reliable and comprehensive knowledge about molecular interaction networks. Formalization of this knowledge is necessary for computational analysis and is achieved by automatic fact extraction using various text-mining algorithms. Most of these techniques suffer from high false positive rates and redundancy of the extracted information. The extracted facts form a large network with no pathways defined. RESULTS: We describe the methodology for automatic curation of Biological Association Networks (BANs) derived by a natural language processing technology called Medscan. The curated data is used for automatic pathway reconstruction. The algorithm for the reconstruction of signaling pathways is also described and validated by comparison with manually curated pathways and tissue-specific gene expression profiles. CONCLUSION: Biological Association Networks extracted by MedScan technology contain sufficient information for constructing thousands of mammalian signaling pathways for multiple tissues. The automatically curated MedScan data is adequate for automatic generation of good quality signaling networks. The automatically generated Regulome pathways and manually curated pathways used for their validation are available free in the ResNetCore database from Ariadne Genomics, Inc. [1]. The pathways can be viewed and analyzed through the use of a free demo version of PathwayStudio software. The Medscan technology is also available for evaluation using the free demo version of PathwayStudio software

Facial pain with localized and widespread manifestations: Separate pathways of vulnerability

Human association studies of common genetic polymorphisms have identified many loci that are associated with risk of complex diseases, although individual loci typically have small effects. However, by envisaging genetic associations in terms of cellular pathways, rather than any specific polymorphism, combined effects of many biologically-relevant alleles can be detected. The effects are likely to be most apparent in investigations of phenotypically-homogenous subtypes of complex diseases. We report findings from a case-control, genetic association study of relationships between 2,925 SNPs and two subtypes of a commonly occurring chronic facial pain condition, temporomandibular disorder (TMD): 1) localized TMD; and 2) TMD with widespread pain. When compared to healthy controls, cases with localized TMD differed in allelic frequency of SNPs that mapped to a serotonergic receptor pathway (P=0.0012), while cases of TMD with widespread pain differed in allelic frequency of SNPs that mapped to a T-cell receptor pathway (P=0.0014). A risk index representing combined effects of six SNPs from the serotonergic pathway was associated with greater odds of localized TMD (odds ratio = 2.7, P=1.3×10−9), and the result was reproduced in a replication case-control cohort study of 639 people (odds ratio = 1.6, P=0.014). A risk index representing combined effects of eight SNPs from the T-cell receptor pathway was associated with greater odds of TMD with widespread pain (P=1.9×10−8), although the result was not significant in the replication cohort. These findings illustrate potential for clinical classification of chronic pain based on distinct molecular profiles and genetic background

Isolation of genetic suppressor elements promoting neoplastic transformation.

Isolation of genetic suppressor elements promoting neoplastic transformation

Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics Extracting Protein Function Information from MEDLINE Using a Full-Sentence Parser

The living cell is a complex machine that depends on the proper functioning of its numerous parts, including proteins. Understanding protein functions and how they modify and regulate each other is the next great challenge for life science researchers. The collective knowledge about protein functions and pathways is scattered throughout numerous publications in scientific journals. Bringing the relevant information together creates a bottleneck in the research and discovery process. The volume of such information grows exponentially which, in turn, renders manual curation impractical. As a viable alternative, automated literature processing tools could be employed to extract and organize biological data into a knowledge base, making it amenable to computational analysis and data mining. We present MedScan, a completely automated NLP-based information extraction system. We have used MedScan to extract about 280,000 mammalian proteins functional links from the entire 2003 release of MEDLINE in only 21 hours. The precision of the extracted information was found to be 91%. We have compared the extracted data with protein co-occurrence data and with the nine well-studied cellular signaling pathways and estimated the recovery rate of MedScan for the entirety of MEDLINE to be between 30 % and 50%. Further improvement of the MedScan technology is discussed. 1