Performance measurement framework for hierarchical text classification

Hierarchical text classification or simply hierarchical classification refers to assigning a document to one or more suitable categories from a hierarchical category space. In our literature survey, we have found that the existing hierarchical classification experiments used a variety of measures to evaluate performance. These performance measures often assume independence between categories and do not consider documents misclassified into categories that are similar or not far from the correct categories in the category tree. In this paper, we therefore propose new performance measures for hierarchical classification. The proposed performance measures consist of category similarity measures and distance based measures that consider the contributions of misclassified documents. Our experiments on hierarchical classification methods based on SVM classifiers and binary Nave Bayes classifiers showed that SVM classifiers perform better than Nave Bayes classifiers on Reuters21578 collection according to the extended measures. A new classifier-centric measure called blocking measure is also defined to examine the performance of subtree classifiers in a top-down level-based hierarchical classification method

Biochemical and Structural Characterization of Selective Allosteric Inhibitors of the Plasmodium falciparum Drug Target, Prolyl-tRNA-synthetase

Plasmodium falciparum (<i>Pf</i>) prolyl-tRNA synthetase (ProRS) is one of the few chemical-genetically validated drug targets for malaria, yet highly selective inhibitors have not been described. In this paper, approximately 40,000 compounds were screened to identify compounds that selectively inhibit <i>Pf</i>ProRS enzyme activity versus Homo sapiens (<i>Hs</i>) ProRS. X-ray crystallography structures were solved for apo, as well as substrate- and inhibitor-bound forms of <i>Pf</i>ProRS. We identified two new inhibitors of <i>Pf</i>ProRS that bind outside the active site. These two allosteric inhibitors showed >100 times specificity for <i>Pf</i>ProRS compared to <i>Hs</i>ProRS, demonstrating this class of compounds could overcome the toxicity related to <i>Hs</i>ProRS inhibition by halofuginone and its analogues. Initial medicinal chemistry was performed on one of the two compounds, guided by the cocrystallography of the compound with <i>Pf</i>ProRS, and the results can instruct future medicinal chemistry work to optimize these promising new leads for drug development against malaria

Disambiguating identity web references using Web 2.0 data and semantics

As web users disseminate more of their personal information on the web, the possibility of these users becoming victims of lateral surveillance and identity theft increases. Therefore web resources containing this personal information, which we refer to as identity web references must be found and disambiguated to produce a unary set of web resources which refer to a given person. Such is the scale of the web that forcing web users to monitor their identity web references is not feasible, therefore automated approaches are required. However, automated approaches require background knowledge about the person whose identity web references are to be disambiguated. Within this paper we present a detailed approach to monitor the web presence of a given individual by obtaining background knowledge from Web 2.0 platforms to support automated disambiguation processes. We present a methodology for generating this background knowledge by exporting data from multiple Web 2.0 platforms as RDF data models and combining these models together for use as seed data. We present two disambiguation techniques; the first using a semi-supervised machine learning technique known as Self-training and the second using a graph-based technique known as Random Walks, we explain how the semantics of data supports the intrinsic functionalities of these techniques. We compare the performance of our presented disambiguation techniques against several baseline measures including human processing of the same data. We achieve an average precision level of 0.935 for Self-training and an average f-measure level of 0.705 for Random Walks in both cases outperforming several baselines measures

Negated bio-events: Analysis and identification

Background: Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations.Results: We have conducted a detailed analysis of three open access bio-event corpora containing negation information (i.e., GENIA Event, BioInfer and BioNLP'09 ST), and have identified the main types of negated bio-events. We have analysed the key aspects of a machine learning solution to the problem of detecting negated events, including selection of negation cues, feature engineering and the choice of learning algorithm. Combining the best solutions for each aspect of the problem, we propose a novel framework for the identification of negated bio-events. We have evaluated our system on each of the three open access corpora mentioned above. The performance of the system significantly surpasses the best results previously reported on the BioNLP'09 ST corpus, and achieves even better results on the GENIA Event and BioInfer corpora, both of which contain more varied and complex events.Conclusions: Recently, in the field of biomedical text mining, the development and enhancement of event-based systems has received significant interest. The ability to identify negated events is a key performance element for these systems. We have conducted the first detailed study on the analysis and identification of negated bio-events. Our proposed framework can be integrated with state-of-the-art event extraction systems. The resulting systems will be able to extract bio-events with attached polarities from textual documents, which can serve as the foundation for more elaborate systems that are able to detect mutually contradicting bio-events. © 2013 Nawaz et al.; licensee BioMed Central Ltd

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Toto, we’re not in Kansas anymore: First reported case of M. persicum septic arthritis

In this report, we describe a case of septic arthritis caused by the newly described Mycobacterium persicum (formerly Mycobacterium kansasii complex). The patient's only significant exposure was home gardening. To our knowledge, this represents the first documented case of M. persicum infection in the United States and first septic arthritis