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author:("Wu, jiayin")
1.  Learning to rank diversified results for biomedical information retrieval from multiple features 
BioMedical Engineering OnLine  2014;13(Suppl 2):S3.
Different from traditional information retrieval (IR), promoting diversity in IR takes consideration of relationship between documents in order to promote novelty and reduce redundancy thus to provide diversified results to satisfy various user intents. Diversity IR in biomedical domain is especially important as biologists sometimes want diversified results pertinent to their query.
A combined learning-to-rank (LTR) framework is learned through a general ranking model (gLTR) and a diversity-biased model. The former is learned from general ranking features by a conventional learning-to-rank approach; the latter is constructed with diversity-indicating features added, which are extracted based on the retrieved passages' topics detected using Wikipedia and ranking order produced by the general learning-to-rank model; final ranking results are given by combination of both models.
Compared with baselines BM25 and DirKL on 2006 and 2007 collections, the gLTR has 0.2292 (+16.23% and +44.1% improvement over BM25 and DirKL respectively) and 0.1873 (+15.78% and +39.0% improvement over BM25 and DirKL respectively) in terms of aspect level of mean average precision (Aspect MAP). The LTR method outperforms gLTR on 2006 and 2007 collections with 4.7% and 2.4% improvement in terms of Aspect MAP.
The learning-to-rank method is an efficient way for biomedical information retrieval and the diversity-biased features are beneficial for promoting diversity in ranking results.
PMCID: PMC4304246  PMID: 25560088
2.  Learning to rank-based gene summary extraction 
BMC Bioinformatics  2014;15(Suppl 12):S10.
In recent years, the biomedical literature has been growing rapidly. These articles provide a large amount of information about proteins, genes and their interactions. Reading such a huge amount of literature is a tedious task for researchers to gain knowledge about a gene. As a result, it is significant for biomedical researchers to have a quick understanding of the query concept by integrating its relevant resources.
In the task of gene summary generation, we regard automatic summary as a ranking problem and apply the method of learning to rank to automatically solve this problem. This paper uses three features as a basis for sentence selection: gene ontology relevance, topic relevance and TextRank. From there, we obtain the feature weight vector using the learning to rank algorithm and predict the scores of candidate summary sentences and obtain top sentences to generate the summary.
ROUGE (a toolkit for summarization of automatic evaluation) was used to evaluate the summarization result and the experimental results showed that our method outperforms the baseline techniques.
According to the experimental result, the combination of three features can improve the performance of summary. The application of learning to rank can facilitate the further expansion of features for measuring the significance of sentences.
PMCID: PMC4243090  PMID: 25474678
3.  An approach to identify over-represented cis-elements in related sequences 
Nucleic Acids Research  2003;31(7):1995-2005.
Computational identification of transcription factor binding sites is an important research area of computational biology. Positional weight matrix (PWM) is a model to describe the sequence pattern of binding sites. Usually, transcription factor binding sites prediction methods based on PWMs require user-defined thresholds. The arbitrary threshold and also the relatively low specificity of the algorithm prevent the result of such an analysis from being properly interpreted. In this study, a method was developed to identify over-represented cis-elements with PWM-based similarity scores. Three sets of closely related promoters were analyzed, and only over- represented motifs with high PWM similarity scores were reported. The thresholds to evaluate the similarity scores to the PWMs of putative transcription factors binding sites can also be automatically determined during the analysis, which can also be used in further research with the same PWMs. The online program is available on the website:∼zhengjsh/OTFBS/.
PMCID: PMC152803  PMID: 12655017

Results 1-3 (3)