Knowledge Sharing from Domain-specific Documents

Recently, collaborative discussions based on the participant generated documents, e.g., customer questionnaires, aviation reports and medical records, are required in various fields such as marketing, transport facilities and medical treatment, in order to share useful knowledge which is crucial to maintain various kind of securities, e.g., avoiding air-traffic accidents and malpractice. We introduce several techniques in natural language processing for extracting information from such text data and verify the validity of such techniques by using aviation documents as an example. We automatically and statistically extract from the documents related words that have not only taxonomical relations like synonyms but also thematic (non-taxonomical) relations including causal and entailment relations. These related words are useful for sharing information among participants. Moreover, we acquire domain-specific terms and phrases from the documents in order to pick up and share important topics from such reports

Building an RDFized Life Science Dictionary

Abstract. There is a growing need for efficient and integrated access to databases provided by diverse institutions. Using Resource Description Framework (RDF) to publish a dataset makes it more reusable. Furthermore, providing a dictionary to translate words into another language in RDF is useful when we want to access datasets across a language barrier. Here, we built an RDF version of the Life Science Dictionary (LSD). LSD consists of various lexical resources including English-Japanese / Japanese-English dictionaries and a thesaurus. Since we believe that LSD is a useful language resource in the life science domain to use multilingual data seamlessly, we assumed that its RDF version enables us to make LSD more reusable and therefore contributes to the life science research community

Tumor-targeting Salmonella typhimurium A1-R prevents experimental human breast cancer bone metastasis in nude mice.

Bone metastasis is a lethal and morbid late stage of breast cancer that is currently treatment resistant. More effective mouse models and treatment are necessary. High bone-metastatic variants of human breast cancer cells were selected in nude mice by cardiac injection. After cardiac injection of a high bone-metastatic variant of breast cancer, all untreated mice had bone metastases compared to only 20% with parental cells. Treatment with tumor-targeting Salmonella typhimurium A1-R completely prevented the appearance of bone metastasis of the high metastatic variant in nude mice (P < 0.001). After injection of the highly bone-metastatic breast cancer variant to the tibia of nude mice, S. typhimurium A1-R treatment significantly reduced tumor growth in the bone (P < 0.001). These data indicated that S. typhimurium A1-R is useful to prevent and inhibit breast cancer bone metastasis and should be of future clinical use for breast cancer in the adjuvant setting

Discriminative application of string similarity methods to chemical and non-chemical names for biomedical abbreviation clustering

BACKGROUND: Various computational methods are presently available to classify whether a protein variation is disease-associated or not. However data derived from recent technological advancements make it feasible to extend the annotation of disease-associated variations in order to include specific phenotypes. Here we tackle the problem of distinguishing between genetic variations associated to cancer and variations associated to other genetic diseases. RESULTS: We implement a new method based on Support Vector Machines that takes as input the protein variant and the protein function, as described by its associated Gene Ontology terms. Our approach succeeds in discriminating between germline variants that are likely to be cancer-associated from those that are related to other genetic disorders. The method performs with values of 90% accuracy and 0.61 Matthews correlation coefficient on a set comprising 6478 germline variations (16% are cancer-associated) in 592 proteins. The sensitivity and the specificity on the cancer class are 69% and 66%, respectively. Furthermore the method is capable of correctly excluding some 96% of 3392 somatic cancer-associated variations in 1983 proteins not included in the training/testing set. CONCLUSIONS: Here we prove feasible that a large set of cancer associated germline protein variations can be successfully discriminated from those associated to other genetic disorders. This is a step further in the process of protein variant annotation. Scoring largely improves when protein function as encoded by Gene Ontology terms is considered, corroborating the role of protein function as a key feature for a correct annotation of its variations

Inhibition of spontaneous and experimental lung metastasis of soft-tissue sarcoma by tumor-targeting Salmonella typhimurium A1-R.

Prognosis of patients with lung metastases of soft-tissue sarcoma is still poor. Therefore, novel systemic therapy is needed to improve the survival of soft-tissue sarcoma. In the present study, tumor-targeting therapy with a genetically-modified auxotrophic strain of Salmonella typhimurium, termed A1-R, was evaluated. Mouse models of primary soft tissue sarcoma and spontaneous lung metastasis were obtained by orthotopic intra-muscular injection of HT1080-RFP human fibrosarcoma cells. S. typhimurium A1-R was administered from day 14, once a week for two weeks. On day 28, lung samples were excised and observed with a fluorescence imaging system. The number of lung metastasis was 8.8 ± 3.4 in the untreated group and 0.8 ± 0.8 in the treated group (P = 0.024). A mouse model of experimental lung metastasis was obtained by tail vein injection of HT1080-RFP cells. The mice were treated with S. typhimurium A1-R (i.v.) on day 7, once a week for three weeks. S. typhimurium A1-R significantly reduced lung metastases and improved overall survival (P = 0.004). S. typhimurium A1-R bacterial therapy has future potential for treating advanced soft tissue sarcoma and improving prognosis of patients with lung metastasis