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OBJECTIVE Predicting or prioritizing the human genes that cause disease, or "disease genes", is one of the emerging tasks in biomedicine informatics. Research on network-based approach to this problem is carried out upon the key assumption of "the network-neighbour of a disease gene is likely to cause the same or a similar disease", and mostly employs data(More)
Constructing a corpus of parallel sentence pairs is an important work in building a Statistical Machine Translation system. It impacts deeply how the quality of a Statistical Machine Translation could achieve. The more parallel sentence pairs we use to train the system, the better translation's quality it is. Nowadays, comparable non-parallel corpora become(More)
BACKGROUND Many factors that directly or indirectly cause adverse drug reaction (ADRs) varying from pharmacological, immunological and genetic factors to ethnic, age, gender, social factors as well as drug and disease related ones. On the other hand, advanced methods of statistics, machine learning and data mining allow the users to more effectively analyze(More)
In drug development process, adverse drug reaction (ADR) is one of the biggest challenges to evaluate the drug safety for passing to the market. Genomic expression data following in vitro drug treatments and thus have become widely used in ADR identification and prediction. In this research, we develop the prediction method by using system(More)
The 5thAsianConference onMachine Learning (ACML2013)was held on 13–15November 2013, at the Australian National University, Canberra, Australia. ACML aims at providing a leading international forum for researchers inmachine learning and relatedfields to share their new ideas and achievements. The conference called for research papers reporting original(More)