BACKGROUND: The exploding growth of the biomedical literature presents many challenges for biological researchers. One such challenge is from the use of a great deal of abbreviations. Extracting abbreviations and their definitions accurately is very helpful to biologists and also facilitates biomedical text analysis. Existing approaches fall into four broad categories: rule based, machine learning based, text alignment based and statistically based. State of the art methods either focus exclusively on acronym-type abbreviations, or could not recognize rare abbreviations. We propose a systematic method to extract abbreviations effectively. At first a scoring method is used to classify the abbreviations into acronym-type and non-acronym-type abbreviations, and then their corresponding definitions are identified by two different methods: text alignment algorithm for the former, statistical method for the latter. RESULTS: A literature mining system MBA was constructed to extract both acronym-type and non-acronym-type abbreviations. An abbreviation-tagged literature corpus, called Medstract gold standard corpus, was used to evaluate the system. MBA achieved a recall of 88% at the precision of 91% on the Medstract gold-standard EVALUATION Corpus. CONCLUSION: We present a new literature mining system MBA for extracting biomedical abbreviations. Our evaluation demonstrates that the MBA system performs better than the others. It can identify the definition of not only acronym-type abbreviations including a little irregular acronym-type abbreviations (e.g., ), but also non-acronym-type abbreviations (e.g., ).

MOTIVATION: Haplotype played an important role in the association studies of disease gene and drug responsivity over the past years, but the low throughput of expensive biological experiments largely limited its application. Alternatively, some efficient statistical methods were developed to deduce haplotypes from genotypes directly. Because these algorithms usually needed to estimate the frequencies of numerous possible haplotypes, the partition and ligation strategy was widely adopted to reduce the time complexity. The haplotypes were usually partitioned uniformly in the past, but recent studies showed that the haplotypes had their own block structure, which may be not uniform. More reasonable block partition and ligation strategy according to the haplotype structure may further improve the accuracy of individual haplotyping. RESULTS: In this article, we presented a simple algorithm for block partition and ligation, which provided better accuracy for individual haplotyping. The block partition and ligation could be completed within O(m(2) logm+m(2n)) time complexity, where m represented the length of genotypes and n represented the number of individuals. We tested the performance of our algorithm on both real and simulated dataset. The result showed that our algorithm yielded better accuracy with short running time. AVAILABILITY: The software is publicly available at http://mail.ustc.edu.cn/~zyzh.

BACKGROUND: Haplotype analysis has gained increasing attention in the context of association studies of disease genes and drug responsivities over the last years. The potential use of haplotypes has led to the initiation of the HapMap project which is to investigate haplotype patterns in the human genome in different populations. Haplotype inference and frequency estimation are essential components of this endeavour. RESULTS: We present a two-stage method to estimate haplotype frequencies in pedigrees, which includes haplotyping stage and estimation stage. In the haplotyping stage, we propose a linear time algorithm to determine all zero-recombinant haplotype configurations for each pedigree. In the estimation stage, we use the expectation-maximization (EM) algorithm to estimate haplotype frequencies based on these haplotype configurations. The experiments demonstrate that our method runs much faster and gives more credible estimates than other popular haplotype analysis software that discards the pedigree information. CONCLUSION: Our method suggests that pedigree information is of great importance in haplotype analysis. It can be used to speedup estimation process, and to improve estimation accuracy as well. The result also demonstrates that the whole haplotype configuration space can be substituted by the space of zero-recombinant haplotype configurations in haplotype frequency estimation, especially when the considered haplotype block is relatively short.