Stacking classifiers for anti-spam filtering of e-mail

We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications

The use of fondaparinux for the treatment of venous thromboembolism in a patient with heparin-induced thombocytopenia and thrombosis caused by heparin flushes

Heparin-induced thrombocytopenia (HIT) is an immunologic drug reaction characterized by paradoxical association with venous and arterial thrombosis. The syndrome is caused by IgG antibodies that are reactive against complexes of platelet factor 4 and heparin. Fondparinux does not bind to platelet factor 4, is structurally too short to induce an antibody response, and could in theory be a useful agent to treat HIT. A 69-year-old white female presented with a lower extremity extensive iliofemoral deep vein thrombosis after a right total knee arthroplasty and was subsequently found to have a pulmonary embolism. The patient was noted to have heparin flushes during her operation. Her platelet drop decreased >50% from baseline during initiation of antithrombotic therapy. She was started on subcutaneous fondaparinux 7.5 mg once daily injection. Her serotonin release assay and enzyme-linked immunosorbent assay for heparin antibodies were positive for HIT. Her platelet count nadir was 60 × 03/mm3 on day 5 and the platelet count rebounded after 8 days of fondaparinux therapy. No recurrent thrombotic or bleeding events were noted throughout her therapy. Anecdotal reports have shown that fondaparinux can be a useful agent to treat HIT with or without thrombosis

A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins

BACKGROUND: Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. RESULTS: The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. CONCLUSION: Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: , and it is the only freely available HMM-based predictor for β-barrel outer membrane protein topology

Comparative effectiveness, safety, and costs of rivaroxaban and warfarin among morbidly obese patients with atrial fibrillation.

BACKGROUND: There are limited data regarding clinical outcomes and healthcare resource utilization of direct oral anticoagulants (DOACs) in patients with atrial fibrillation (AF) who are morbidly obese (body mass index \u3e40 kg/m METHODS: Using data from 2 US healthcare claims databases, we identified patients initiating rivaroxaban or warfarin who had ≥1 medical claim with an AF diagnosis, a diagnostic code for morbid obesity (ICD-9: 278.01, V85.4%; ICD-10: E66.01%, E66.2%, Z68.4%), and a minimum continuous enrollment of 12 months before and 3 months after treatment initiation. Patients were excluded if they had mitral stenosis, a mechanical heart valve procedure, an organ/tissue transplant, or an oral anticoagulant prescription prior to the index date. Rivaroxaban and warfarin patients were 1:1 propensity score matched. Conditional logistic regression was used to compare ischemic stroke/systemic embolism and major bleeding risk. Generalized linear models were used to compare healthcare resource utilization and costs. RESULTS: A total of 3563 matched pairs of morbidly obese AF patients treated with rivaroxaban or warfarin were identified. The majority (81.4%) of patients in the rivaroxaban cohort were receiving the 20 mg dose. The rivaroxaban and warfarin cohorts were well balanced after propensity score matching. The risks of ischemic stroke/systemic embolism and major bleeding were similar for rivaroxaban and warfarin users (stroke/systemic embolism: 1.5% vs 1.7%; odds ratio [OR]: 0.88; 95% confidence interval [CI]: 0.60, 1.28; P = .5028; major bleeding: 2.2% vs 2.7%; OR: 0.80; 95% CI: 0.59, 1.08; P = .1447). Total healthcare costs including medication costs per patient per year (PPPY) were significantly lower with rivaroxaban versus warfarin ($48,552 vs$52,418; P = .0025), which was primarily driven by lower hospitalization rate (50.2% vs 54.1%; P = .0008), shorter length of stay (7.5 vs 9.1 days; P = .0010), and less outpatient service utilization (86 vs 115 visits PPPY; P \u3c .0001). CONCLUSIONS: Morbidly obese AF patients treated with rivaroxaban had comparable risk of ischemic stroke/systemic embolism and major bleeding as those treated with warfarin, but lower healthcare resource utilization and costs

Rivaroxaban versus warfarin treatment among morbidly obese patients with venous thromboembolism: Comparative effectiveness, safety, and costs.

INTRODUCTION: Limited data exist on direct-acting oral anticoagulants in morbidly obese patients with venous thromboembolism (VTE). We compared clinical and health/economic outcomes with rivaroxaban versus warfarin for VTE treatment in morbidly obese patients. MATERIALS AND METHODS: This retrospective 1:1 propensity score matched cohort study analyzed data from 2 US claims databases. VTE patients initiating rivaroxaban or warfarin were identified who had diagnosis codes for morbid obesity (ICD-9:278.01,V85.4; ICD-10:E66.01,E66.2,Z68.4) 12 months pre- or 3 months post-initiation and followed ≥3 months. Intent-to-treat (ITT) and on-treatment (OT) analyses were conducted using conditional logistic regression and generalized linear models to compare recurrent VTE and major bleeding risks, healthcare resource utilization (HRU), and per patient per year (PPPY) costs. RESULTS: In total, 2890 matched pairs of morbidly obese VTE patients initiating rivaroxaban or warfarin were identified. Risks of recurrent VTE (ITT: OR: 0.99; 95% CI: 0.85-1.14) and major bleeding (OT: OR: 0.75; 95% CI: 0.47-1.19) were similar for cohorts. Anti-Factor Xa laboratory measurement was performed on CONCLUSIONS: Morbidly obese VTE patients receiving rivaroxaban had similar risks of recurrent VTE and major bleeding versus warfarin. Rivaroxaban treatment yielded significantly less HRU and total medical costs, with similar total healthcare costs between groups

A database for G proteins and their interaction with GPCRs

BACKGROUND: G protein-coupled receptors (GPCRs) transduce signals from extracellular space into the cell, through their interaction with G proteins, which act as switches forming hetero-trimers composed of different subunits (α,β,γ). The α subunit of the G protein is responsible for the recognition of a given GPCR. Whereas specialised resources for GPCRs, and other groups of receptors, are already available, currently, there is no publicly available database focusing on G Proteins and containing information about their coupling specificity with their respective receptors. DESCRIPTION: gpDB is a publicly accessible G proteins/GPCRs relational database. Including species homologs, the database contains detailed information for 418 G protein monomers (272 Gα, 87 Gβ and 59 Gγ) and 2782 GPCRs sequences belonging to families with known coupling to G proteins. The GPCRs and the G proteins are classified according to a hierarchy of different classes, families and sub-families, based on extensive literature searchs. The main innovation besides the classification of both G proteins and GPCRs is the relational model of the database, describing the known coupling specificity of the GPCRs to their respective α subunit of G proteins, a unique feature not available in any other database. There is full sequence information with cross-references to publicly available databases, references to the literature concerning the coupling specificity and the dimerization of GPCRs and the user may submit advanced queries for text search. Furthermore, we provide a pattern search tool, an interface for running BLAST against the database and interconnectivity with PRED-TMR, PRED-GPCR and TMRPres2D. CONCLUSIONS: The database will be very useful, for both experimentalists and bioinformaticians, for the study of G protein/GPCR interactions and for future development of predictive algorithms. It is available for academics, via a web browser at the URL