How best to present query search results is an important problem in search engines and information retrieval systems. When a single query retrieves many results, simply showing them as a long list will provide users with poor overview. Nowadays, ranking and clustering query search results have been two useful separate post-processing techniques to organize retrieved documents. In this paper, we proposed a spectral analysis method based on the content similarity networks to integrate the clustering and ranking techniques for improving literature search. The new approach organizes all these search results into categories intelligently and simultaneously rank the results in each category. A variety of theoretical and empirical studies have demonstrated that the presented method performs well in real applications, especially in biomedical literature retrieval. Moreover, any free text information can be analyzed with the new method, i.e., the proposed approach can be applied to various information systems, such as Web search engines and literature search service.

How to present information retrieval results is one main problem that needs to tackle in biomedical information retrieval. A single query may retrieve a large number of results and advanced ranking algorithms are necessary to rank the results so that most relevant result is shown on the top of the list. In this paper, we explored to rank MEDLINE citations using HITS (Hyperlink-Induced Topic Search) algorithm. HITS uses web links from one page to another to rank web pages. It has proven to be successful in web search engines. We further extended HITS to supervised HITS to rank citations. Our results showed that supervised HITS algorithm significantly outperforms HITS algorithm (p<0.01). Compared with HITS, supervised HITS can improve citation ranking from 15% to more than 59% in almost all the cases we tested. Furthermore, MeSH terms outperforms text words in ranking citations, especially when HITS was applied (p<0.01).

OBJECTIVE: A major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group. DESIGN: A text mining system framework for automatic document clustering and ranking organized MEDLINE citations following simple PubMed queries. The system grouped the retrieved citations, ranked the citations in each cluster, and generated a set of keywords and MeSH terms to describe the common theme of each cluster. MEASUREMENTS: Several possible ranking functions were compared, including citation count per year (CCPY), citation count (CC), and journal impact factor (JIF). We evaluated this framework by identifying as "important" those articles selected by the Surgical Oncology Society. RESULTS: Our results showed that CCPY outperforms CC and JIF, i.e., CCPY better ranked important articles than did the others. Furthermore, our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster. CONCLUSIONS: The text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups.

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NP-hard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a "spurious" heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in "spurious" heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.

A control flow trace captures the complete sequence of dynamically executed basic blocks and function calls. It is usually stored in compressed form due to its large size. Matching an intraprocedural path in a control flow trace faces path interruption and path context problems and therefore requires the extension of traditional pattern matching algorithms. In this paper we evaluate different path matching schemes including those matching in the compressed data directly and those matching after the decompression. We design simple indices for the compressed data and show that they can greatly improve the performance. Our experimental results show that these schemes are useful and can be adapted to environments with different hardware settings and path matching requests.