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Background: The natural products, metabolites, of gut microbes are crucial effect factors on diseases. Comprehensive identification and annotation of relationships among disease, metabolites, and microbes can provide efficient and targeted solutions towards understanding the mechanism of complex disease and development of new markers and drugs. Results: We developed Gut Microbial Metabolite Association with Disease (GMMAD), a manually curated database of associations among human diseases, gut microbes, and metabolites of gut microbes. Here, this initial release (i) contains 3,836 disease-microbe associations and 879,263 microbe-metabolite associations, which were extracted from literatures and available resources and then experienced our manual curation; (ii) defines an association strength score and a confidence score. With these two scores, GMMAD predicted 220,690 disease-metabolite associations, where the metabolites all belong to the gut microbes. We think that the positive effective (with both scores higher than suggested thresholds) associations will help identify disease marker and understand the pathogenic mechanism from the sense of gut microbes. The negative effective associations would be taken as biomarkers and have the potential as drug candidates. Literature proofs supported our proposal with experimental consistence; (iii) provides a user-friendly web interface that allows users to browse, search, and download information on associations among diseases, metabolites, and microbes. The resource is freely available at http://guolab.whu.edu.cn/GMMAD. Conclusions: As the online-available unique resource for gut microbial metabolite-disease associations, GMMAD is helpful for researchers to explore mechanisms of disease- metabolite-microbe and screen the drug and marker candidates for different diseases. [ABSTRACT FROM AUTHOR]

In natural language processing, multiword expressions (MWEs) play a significant role in understanding the context and meaning of a sentence. A MWE comprises two or more words that are handled as if they were one. MWE has the property that the constituent words are consistent and are often used in related contexts. Hindi is used as a case study. We employed three properties: linguistic or syntactical pattern, a relationship between constituent words, and context similarity and proposed a three-phase hybrid approach to extract MWE from unstructured Hindi text. Experimental analysis and comparison of results on the TDIL dataset show the superiority of the proposed hybrid method over the context-based method and association-based methods. [ABSTRACT FROM AUTHOR]

Hanahan and Weinberg introduced the "hallmarks of cancer" and typified essential biological abilities acquired by human cancer. Since then, a growing understanding of hallmark principles associated with breast cancer has assisted knowledge-based therapeutics development; however, despite the rapidly increasing number of targeted therapeutics, enduring disease-free responses for most forms of breast cancer is rare. Invasion and metastasis are the most defining feature of breast cancer malignancy and the leading cause of patient mortality. Hence, we propose a modified hallmarks model adapted to breast cancer, in which invasion and metastasis are shifted to the center of attention, thereby emphasizing it as a potentially superior therapeutic target. Although the scientific community highly appreciates the importance of the invasion and metastasis hallmark, as can be demonstrated by the growing number of publications on breast cancer metastasis, very few clinical trials concentrate on testing anti-metastasis inhibitors and even fewer trials focus on inhibitors for breast cancer metastasis. Here, we discuss the obstacles of applying research on invasion and metastasis therapeutics into the clinic and present current developments that could provide a potential solution to this dilemma. [ABSTRACT FROM AUTHOR]