Lana Yeganova

Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach

Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a body of unstructured medical text which is growing exponentially. This calls for developing text mining techniques for identifying DDIs. The state-of-the-art DDI extraction methods use Support Vector Machines (SVMs) with non-linear composite kernels to explore diverse contexts in literature. While computationally less expensive, linear kernel-based systems have not achieved a comparable performance in DDI extraction tasks. In this work, we propose an efficient and scalable system using a linear kernel to identify DDI information. The proposed approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that when equipped with a rich set of lexical and syntactic features, a linear SVM classifier is able to achieve a competitive performance in detecting DDIs. In addition, the one-against-one strategy proves vital for addressing an imbalance issue in DDI type classification. Applied to the DDIExtraction 2013 corpus, our system achieves an F1 score of 0.670, as compared to 0.651 and 0.609 reported by the top two participating teams in the DDIExtraction 2013 challenge, both based on non-linear kernel methods.

Author name disambiguation for PubMed

Log analysis shows that PubMed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity may lead to irrelevant retrieval results. To improve the PubMed user experience with author name queries, we designed an author name disambiguation system consisting of similarity estimation and agglomerative clustering. A machine‐learning method was employed to score the features for disambiguating a pair of papers with ambiguous names. These features enable the computation of pairwise similarity scores to estimate the probability of a pair of papers belonging to the same author, which drives an agglomerative clustering algorithm regulated by 2 factors: name compatibility and probability level. With transitivity violation correction, high precision author clustering is achieved by focusing on minimizing false‐positive pairing. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results. When compared with a state‐of‐the‐art system, our evaluation shows that among all the pairs the lumping error rate drops from 10.1% to 2.2% for our system, while the splitting error rises from 1.8% to 7.7%. This results in an overall error rate of 9.9%, compared with 11.9% for the state‐of‐the‐art method. Other evaluations based on gold standard data also show the increase in accuracy of our clustering. We attribute the performance improvement to the machine‐learning method driven by a large‐scale training set and the clustering algorithm regulated by a name compatibility scheme preferring precision. With integration of the author name disambiguation system into the PubMed search engine, the overall click‐through‐rate of PubMed users on author name query results improved from 34.9% to 36.9%.

Identification of related gene/protein names based on an HMM of name variations

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a special case of the problem of named entity recognition, while the second is a special case of the problem of automatic term recognition (ATR). We study the second problem, that of gene or protein name variation. Here we describe a system which, given a query gene or protein name, identifies related gene or protein names in a large list. The system is based on a dynamic programming algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a fully trainable hidden Markov model.

Meshable: searching PubMed abstracts by utilizing MeSH and MeSH-derived topical terms

Summary: Medical Subject Headings (MeSH®) is a controlled vocabulary for indexing and searching biomedical literature. MeSH terms and subheadings are organized in a hierarchical structure and are used to indicate the topics of an article. Biologists can use either MeSH terms as queries or the MeSH interface provided in PubMed® for searching PubMed abstracts. However, these are rarely used, and there is no convenient way to link standardized MeSH terms to user queries. Here, we introduce a web interface which allows users to enter queries to find MeSH terms closely related to the queries. Our method relies on co-occurrence of text words and MeSH terms to find keywords that are related to each MeSH term. A query is then matched with the keywords for MeSH terms, and candidate MeSH terms are ranked based on their relatedness to the query. The experimental results show that our method achieves the best performance among several term extraction approaches in terms of topic coherence. Moreover, the interface can be effectively used to find full names of abbreviations and to disambiguate user queries. Availability and Implementation: https://www.ncbi.nlm.nih.gov/IRET/MESHABLE/ Contact: sun.kim@nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Finding abbreviations in biomedical literature: three BioC-compatible modules and four BioC-formatted corpora.

BioC is a recently created XML format to share text data and annotations, and an accompanying input/output library to promote interoperability of data and tools for natural language processing of biomedical text. This article reports the use of BioC to address a common challenge in processing biomedical text information—that of frequent entity name abbreviation. We selected three different abbreviation definition identification modules, and used the publicly available BioC code to convert these independent modules into BioC-compatible components that interact seamlessly with BioC-formatted data, and other BioC-compatible modules. In addition, we consider four manually annotated corpora of abbreviations in biomedical text: the Ab3P corpus of 1250 PubMed abstracts, the BIOADI corpus of 1201 PubMed abstracts, the old MEDSTRACT corpus of 199 PubMed® citations and the Schwartz and Hearst corpus of 1000 PubMed abstracts. Annotations in these corpora have been re-evaluated by four annotators and their consistency and quality levels have been improved. We converted them to BioC-format and described the representation of the annotations. These corpora are used to measure the three abbreviation-finding algorithms and the results are given. The BioC-compatible modules, when compared with their original form, have no difference in their efficiency, running time or any other comparable aspects. They can be conveniently used as a common pre-processing step for larger multi-layered text-mining endeavors. Database URL: Code and data are available for download at the BioC site: http://bioc.sourceforge.net.

Machine learning with naturally labeled data for identifying abbreviation definitions.

BackgroundThe rapid growth of biomedical literature requires accurate text analysis and text processing tools. Detecting abbreviations and identifying their definitions is an important component of such tools. Most existing approaches for the abbreviation definition identification task employ rule-based methods. While achieving high precision, rule-based methods are limited to the rules defined and fail to capture many uncommon definition patterns. Supervised learning techniques, which offer more flexibility in detecting abbreviation definitions, have also been applied to the problem. However, they require manually labeled training data.MethodsIn this work, we develop a machine learning algorithm for abbreviation definition identification in text which makes use of what we term naturally labeled data. Positive training examples are naturally occurring potential abbreviation-definition pairs in text. Negative training examples are generated by randomly mixing potential abbreviations with unrelated potential definitions. The machine learner is trained to distinguish between these two sets of examples. Then, the learned feature weights are used to identify the abbreviation full form. This approach does not require manually labeled training data.ResultsWe evaluate the performance of our algorithm on the Ab3P, BIOADI and Medstract corpora. Our system demonstrated results that compare favourably to the existing Ab3P and BIOADI systems. We achieve an F-measure of 91.36% on Ab3P corpus, and an F-measure of 87.13% on BIOADI corpus which are superior to the results reported by Ab3P and BIOADI systems. Moreover, we outperform these systems in terms of recall, which is one of our goals.

Retro: concept-based clustering of biomedical topical sets

MOTIVATION Clustering methods can be useful for automatically grouping documents into meaningful clusters, improving human comprehension of a document collection. Although there are clustering algorithms that can achieve the goal for relatively large document collections, they do not always work well for small and homogenous datasets. METHODS In this article, we present Retro-a novel clustering algorithm that extracts meaningful clusters along with concise and descriptive titles from small and homogenous document collections. Unlike common clustering approaches, our algorithm predicts cluster titles before clustering. It relies on the hypergeometric distribution model to discover key phrases, and generates candidate clusters by assigning documents to these phrases. Further, the statistical significance of candidate clusters is tested using supervised learning methods, and a multiple testing correction technique is used to control the overall quality of clustering. RESULTS We test our system on five disease datasets from OMIM(®) and evaluate the results based on MeSH(®) term assignments. We further compare our method with several baseline and state-of-the-art methods, including K-means, expectation maximization, latent Dirichlet allocation-based clustering, Lingo, OPTIMSRC and adapted GK-means. The experimental results on the 20-Newsgroup and ODP-239 collections demonstrate that our method is successful at extracting significant clusters and is superior to existing methods in terms of quality of clusters. Finally, we apply our system to a collection of 6248 topical sets from the HomoloGene(®) database, a resource in PubMed(®). Empirical evaluation confirms the method is useful for small homogenous datasets in producing meaningful clusters with descriptive titles. AVAILABILITY AND IMPLEMENTATION A web-based demonstration of the algorithm applied to a collection of sets from the HomoloGene database is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Wilbur/IRET/CLUSTERING_HOMOLOGENE/index.html. CONTACT lana.yeganova@nih.gov SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

The Synergy Between PAV and AdaBoost

Schapire and Singers improved version of AdaBoost for handling weak hypotheses with confidence rated predictions represents an important advance in the theory and practice of boosting. Its success results from a more efficient use of information in weak hypotheses during updating. Instead of simple binary voting a weak hypothesis is allowed to vote for or against a classification with a variable strength or confidence. The Pool Adjacent Violators (PAV) algorithm is a method for converting a score into a probability. We show how PAV may be applied to a weak hypothesis to yield a new weak hypothesis which is in a sense an ideal confidence rated prediction and that this leads to an optimal updating for AdaBoost. The result is a new algorithm which we term PAV-AdaBoost. We give several examples illustrating problems for which this new algorithm provides advantages in performance.

Finding biomedical categories in Medline

BackgroundThere are several humanly defined ontologies relevant to Medline. However, Medline is a fast growing collection of biomedical documents which creates difficulties in updating and expanding these humanly defined ontologies. Automatically identifying meaningful categories of entities in a large text corpus is useful for information extraction, construction of machine learning features, and development of semantic representations. In this paper we describe and compare two methods for automatically learning meaningful biomedical categories in Medline. The first approach is a simple statistical method that uses part-of-speech and frequency information to extract a list of frequent nouns from Medline. The second method implements an alignment-based technique to learn frequent generic patterns that indicate a hyponymy/hypernymy relationship between a pair of noun phrases. We then apply these patterns to Medline to collect frequent hypernyms as potential biomedical categories.ResultsWe study and compare these two alternative sets of terms to identify semantic categories in Medline. We find that both approaches produce reasonable terms as potential categories. We also find that there is a significant agreement between the two sets of terms. The overlap between the two methods improves our confidence regarding categories predicted by these independent methods.ConclusionsThis study is an initial attempt to extract categories that are discussed in Medline. Rather than imposing external ontologies on Medline, our methods allow categories to emerge from the text.

Brief communication: Hidden Markov models and optimized sequence alignments

We present a formulation of the Needleman-Wunsch type algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a hidden Markov process. The fully trainable model is applied to two problems in bioinformatics: the recognition of related gene/protein names and the alignment and scoring of homologous proteins.