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BACKGROUND Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part.(More)
Protein-protein interactions play a key role in various aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. However, the amount of biomedical literature regarding protein interactions is increasing rapidly and it is difficult for interaction database curators to(More)
BACKGROUND Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for(More)
Automatic extracting protein–protein interaction information from biomedical literature can help to build protein relation network, predict protein function and design new drugs. This paper presents a protein–protein interaction extraction system BioPPIExtractor for biomedical literature. This system applies Conditional Random Fields model to tag protein(More)
As an education pattern, e-learning systems are becoming more and more popular. For developing of e-learning systems, it is important to know users' opinions and evaluation about them. It is involved in applying the automatic text analysis to extract the opinions and adopting automatic sentiment analysis to identify the sentiment of opinions from the Web(More)
BACKGROUND Extracting protein-protein interactions from biomedical literature is an important task in biomedical text mining. Supervised machine learning methods have been used with great success in this task but they tend to suffer from data sparseness because of their restriction to obtain knowledge from limited amount of labelled data. In this work, we(More)
To extract biomedical information about bio-entities from the huge amount of biomedical literature, the first key step is recognizing their names in these literatures, which remains a challenging task due to the irregularities and ambiguities in bio-entities nomenclature. The recognition performances of the current popular methods, machine learning(More)
Nanoparticles (NPs) were widely used in drugs/probes delivery for improved disease diagnosis and/or treatment. Targeted delivery to cancer cells is a highly attractive application of NPs. However, few studies have been performed on the targeting mechanisms of these ligand-modified delivery systems. Additional studies are needed to understand the transport(More)
In this paper, we use query-level regression as the loss function. The regression loss function has been used in pointwise methods, however pointwise methods ignore the query boundaries and treat the data equally across queries, and thus the effectiveness is limited. We show that regression is an effective loss function for learning to rank when used in(More)