Systematic feature evaluation for gene name recognition

In task 1A of the BioCreAtIvE evaluation, systems had to be devised that recognize words and phrases forming gene or protein names in natural language sentences. We approach this problem by building a word classification system based on a sliding window approach with a Support Vector Machine, combined with a pattern-based post-processing for the recognition of phrases. The performance of such a system crucially depends on the type of features chosen for consideration by the classification method, such as pre- or postfixes, character n-grams, patterns of capitalization, or classification of preceding or following words. We present a systematic approach to evaluate the performance of different feature sets based on recursive feature elimination, RFE. Based on a systematic reduction of the number of features used by the system, we can quantify the impact of different feature sets on the results of the word classification problem. This helps us to identify descriptive features, to learn about the structure of the problem, and to design systems that are faster and easier to understand. We observe that the SVM is robust to redundant features. RFE improves the performance by 0.7%, compared to using the complete set of attributes. Moreover, a performance that is only 2.3% below this maximum can be obtained using fewer than 5% of the features

Feasibility and effectiveness of second-line chemotherapy with mitomycin C in patients with advanced penile cancer

BackgroundTriple-drug cisplatin- and taxane-based chemotherapy is the standard treatment for metastatic penile squamous cell cancer (PeSCC), with a moderate response rate of 30% to 38%. Relapse after first-line chemotherapy has a poor prognosis and there is no established second-line treatment. Mitomycin C (MMC) is used as an effective chemotherapy in squamous cell carcinoma of other localities. We therefore used MMC as a single agent for the second-line treatment for patients with advanced PeSCC.MethodsNine patients [median age 63 years (range 31 years–81 years)], who, after inguinal and pelvic lymphadenectomy and progression after first-line chemotherapy, received second-line treatment with 20 mg of MMC administered intravenously and weekly, were included in this study. The median number of cycles of MMC was 6 (range 2–12 cycles) and the median cumulative dose was 120 mg absolute (range 40 mg absolute–240 mg absolute). The patients’ toxicity and treatment responses were evaluated, with the latter evaluated using 18F-FDG-PET/CT.ResultsCommon Terminology Criteria for Adverse Events (CTCAE) grades 3 or 4 thrombocytopenia and grades 2 or 3 leukopenia occurred in all patients, as did anemia. In seven patients, the application interval had to be extended due to thrombocytopenia. Stable disease was achieved in two patients, and all others progressed under treatment. Seven patients died of the disease, with most patients dying 6 months after starting MMC therapy. Of the two patients who responded with disease stabilization, one died of progressive disease 14 months after MMC treatment. The other responding patient has been stable for over 1 year and is still receiving treatment, which he tolerates well, and has a good quality of life.ConclusionMMC has only moderate efficacy as a second-line treatment in patients with metastatic PeSCC. With MMC treatment, hematological toxicity is marked

Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy

<p>Abstract</p> <p>Background</p> <p>Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively.</p> <p>Results</p> <p>The 'Closest Sense' method assumes that the ontology defines multiple senses of the term. It computes the shortest path of co-occurring terms in the document to one of these senses. The 'Term Cooc' method defines a log-odds ratio for co-occurring terms including co-occurrences inferred from the ontology structure. The 'MetaData' approach trains a classifier on metadata. It does not require any ontology, but requires training data, which the other methods do not. To evaluate these approaches we defined a manually curated training corpus of 2600 documents for seven ambiguous terms from the Gene Ontology and MeSH. All approaches over all conditions achieve 80% success rate on average. The 'MetaData' approach performed best with 96%, when trained on high-quality data. Its performance deteriorates as quality of the training data decreases. The 'Term Cooc' approach performs better on Gene Ontology (92% success) than on MeSH (73% success) as MeSH is not a strict is-a/part-of, but rather a loose is-related-to hierarchy. The 'Closest Sense' approach achieves on average 80% success rate.</p> <p>Conclusion</p> <p>Metadata is valuable for disambiguation, but requires high quality training data. Closest Sense requires no training, but a large, consistently modelled ontology, which are two opposing conditions. Term Cooc achieves greater 90% success given a consistently modelled ontology. Overall, the results show that well structured ontologies can play a very important role to improve disambiguation.</p> <p>Availability</p> <p>The three benchmark datasets created for the purpose of disambiguation are available in Additional file <supplr sid="S1">1</supplr>.</p> <suppl id="S1"> <title> <p>Additional file 1</p> </title> <text> <p><b>Benchmark datasets used in the experiments.</b> The three corpora (High quality/Low quantity corpus; Medium quality/Medium quantity corpus; Low quality/High quantity corpus) are given in the form of PubMed identifiers (PMID) for True/False cases for the 7 ambiguous terms examined (GO/MeSH/UMLS identifiers are also given).</p> </text> <file name="1471-2105-10-28-S1.txt"> <p>Click here for file</p> </file> </suppl

Factors Affecting the Improvement of the Initial Peak Urinary Flow Rate after Transurethral Resection of the Prostate or Photoselective Vaporization of the Prostate for Treating Benign Prostatic Hyperplasia

Purpose We evaluated the factors that affect the improvement of the initial peak flow rate after transurethral resection of the prostate (TURP) or photoselective vaporization of the prostate (PVP) for benign prostatic hyperplasia (BPH) patients by using noninvasive tools. Methods One hundred and twenty seven BPH patients who had undergone TURP or PVP between January 2005 and May 2009 were evaluated. They were divided into 2 groups: the postoperative initial peak urinary flow rate (Qmax) was less than 10 mL/sec (Group 1; n=37, TURP=11, PVP=26) and more than 10 mL/sec (Group 2; n=90, TURP=41, PVP=49). We confirmed the patients' preoperative check lists. The check list were the international prostate symptom score (IPSS), the quality of life score, a past history of acute urinary retention (AUR), body mass index and/or pyuria, the serum prostate-specific antigen (PSA) level and the prostate volume, the prostate transitional zone volume and prostatic calcification. The initial Qmax was measured at the outpatient clinic one week after discharge. Results The improvement rate was not significant difference between the TURP group (78.8%) and the PVP group (65.3%). The efficacy parameters were the IPSS-storage symptom score, the prostate volume, the PSA level and a past history of AUR. The IPSS-storage symptom scores of Group 1 (12.3±3.3) was higher than those of Group 2 (10.5±1.7). The prostate volume of Group 2 (42.3±16.6 g) was bigger than that of Group 1 (36.6±7.8 g). The PSA level of Group 2 (3.8±2.6 ng/mL) was higher than that of Group 1 (2.6±2.6 ng/mL). A past history of AUR in Group 1 (35.1%) was more prevalent than that of Group 2 (15.6%). Conclusions The non-invasive factors affecting the initial Qmax after TURP or PVP were the IPSS-storage symptom score, the prostate volume and a past history of AUR. Accordingly, in patients who have a higher IPSS-storage symptom score, a smaller prostate volume and a history of AUR, there might be a detrimental effect on the initial Qmax after TURP or PVP. These factors might also be used as long-term prognostic factors