Gallbladder cancer is relatively uncommon with high incidence in certain geographic locations, including Latin America, East and South Asia and Eastern Europe. Molecular characterization of this disease has been limited and targeted therapy options for advanced disease remain an open area of investigation. In the present study, surgical pathology obtained from resected gallbladder cancer cases (n=72) was examined for the presence of targetable, somatic mutations. All cases were formalin-fixed and paraffin-embedded (FFPE). Two approaches were used: a) mass spectroscopy-based profiling for 159 point (‘hot-spot’) mutations in 33 genes commonly involved in solid tumors and b) next-generation sequencing (NGS) platform that examined the complete coding sequence of in 182 cancer-related genes. Fifty-seven cases were analyzed for hotspot mutations and 15 for NGS. Fourteen hotspot mutations were identified in nine cases. Of these, KRAS mutation was significantly associated with poor survival on multivariate analysis. Other targetable mutations included PIK3CA (N=2) and ALK (N=1). On NGS, 26 mutations were noted in 15 cases. P53 and PI3 kinase pathway (STK11, RICTOR,TSC2) mutations were common. One case had FGF10 amplification while another had FGF3-TACC gene fusion, not previously described in gallbladder cancer. In conclusion, somatic mutation profiling using archival FFPE samples from gallbladder cancer is feasible. NGS, in particular may be a useful platform for identifying novel mutations for targeted therapy.
gallbladder neoplasms; mutational analysis; DNA Sequencing
Cardiovascular diseases are the current leading causes of death and disability
To assess the effects of a basic educational program for cardiovascular prevention
in an unselected outpatient population.
All participants received an educational program to change to a healthy lifestyle.
Assessments were conducted at study enrollment and during follow-up. Symptoms,
habits, ATP III parameters for metabolic syndrome, and American Heart
Association’s 2020 parameters of cardiovascular health were assessed.
A total of 15,073 participants aged ≥ 18 years entered the study. Data
analysis was conducted in 3,009 patients who completed a second assessment. An
improvement in weight (from 76.6 ± 15.3 to 76.4 ± 15.3 kg, p =
0.002), dyspnea on exertion NYHA grade II (from 23.4% to 21.0%) and grade III
(from 15.8% to 14.0%) and a decrease in the proportion of current active smokers
(from 3.6% to 2.9%, p = 0.002) could be documented. The proportion of patients
with levels of triglycerides > 150 mg/dL (from 46.3% to 42.4%, p < 0.001)
and LDL cholesterol > 100 mg/dL (from 69.3% to 65.5%, p < 0.001) improved. A
≥ 20% improvement of AHA 2020 metrics at the level graded as poor was found
for smoking (-21.1%), diet (-29.8%), and cholesterol level (-23.6%). A large
dropout as a surrogate indicator for low patient adherence was documented
throughout the first 5 visits, 80% between the first and second assessments, 55.6%
between the second and third assessments, 43.6% between the third and fourth
assessments, and 38% between the fourth and fifth assessments.
A simple, basic educational program may improve symptoms and modifiable
cardiovascular risk factors, but shows low patient adherence.
Health Behavior; Life Style; Prevention; Obesity; Risk Factors; Health Education
Chromosome 17 abnormalities (C17 abns) are associated with poor outcome in leukemias including acute myeloid leukemia (AML). Recently, Monosomal karyotype (MK) was introduced as independent predictor of dismal outcome in AML. The additional prognostic impact of C-17 abns in patients with MK in a complex karyotype (CK) background is not clear.
Patients and Method
We conducted a retrospective analysis of 1086 patients with newly diagnosed AML treated between January 1998 and December 2007. Patients received treatment with one of the institution's frontline protocols.
Four hundred eighty-three patients had CK. Among them, 370 patients (77%) had MK (CK-MK), and 195 patients (53%) had both MK and C 17 abns (CK-MK-C17 abns). Patients with CK-MK had significantly shorter overall survival (OS) rates compared to patients with CK without MK (4.4 m vs 8 m, respectively, P = 0.002). The median OS for patients with CK-C17 abns was shorter than patients without C17 abns (4 m vs 6.1m, respectively, P = 0.004). In a multivariate analysis, presence of MK among patients with CK was identified as an independent prognostic marker for OS. In addition, presence of C17 abns had significant negative impact on OS among patients with CK-MK (P = 0.04).
Among patients with CK-AML, MK was associated with poor outcomes. Additional presence of C17 abns further worsens the outcome in these particularly poor-risk patients with AML.
chromosome 17; monosomal karyotype; complex cytogenetics; AML
In patients with metastatic renal cell carcinoma (mRCC), the timing of systemic targeted therapy in relation to cytoreductive nephrectomy (CN) is under investigation.
To evaluate postoperative complications after the use of presurgical targeted therapy prior to CN.
Design, setting, and participants
A retrospective review of all patients who underwent a CN at The University of Texas M.D. Anderson Cancer Center from 2004 to 2010 was performed. Inclusion in this study required documented evidence of mRCC, with treatment incorporating CN.
Patients receiving presurgical systemic targeted therapy prior to CN were compared to those undergoing immediate CN.
Complications were assessed using the modified Clavien system for a period of 12 mo postoperatively.
Results and limitations
Presurgical therapy was administered to 70 patients prior to CN (presurgical), while 103 patients had an immediate CN (immediate). A total of 232 complications occurred in 57% of patients (99 of 173). Use of presurgical systemic targeted therapy was predictive of having a complication >90 d postoperatively (p = 0.002) and having multiple complications (p = 0.013), and it was predictive of having a wound complication (p < 0.001). Despite these specific complications, presurgical systemic targeted therapy was not associated with an increased overall complication risk on univariable or multivariate analysis (p = 0.064 and p = 0.237) and was not predictive for severe (Clavien ≥3) complications (p = 0.625). This study is limited by its retrospective nature. As is inherent to any retrospective study reporting on complications, we are limited by reporting bias and the potential for misclassification of specific complications.
Despite an increased risk for specific wound-related complications, overall surgical complications and the risk of severe complications (Clavien ≥3) are not greater after presurgical targeted therapy in comparison to upfront cytoreductive surgery.
Cytoreductive nephrectomy; Metastatic renal cell carcinoma; Targeted therapy; Complications
Clinical records include both coded and free-text fields that interact to reflect complicated patient stories. The information often covers not only the present medical condition and events experienced by the patient, but also refers to relevant events in the past (such as signs, symptoms, tests or treatments). In order to automatically construct a timeline of these events, we first need to extract the temporal relations between pairs of events or time expressions presented in the clinical notes. We designed separate extraction components for different types of temporal relations, utilizing a novel hybrid system that combines machine learning with a graph-based inference mechanism to extract the temporal links. The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues. We generalized the sentences in order to discover patterns that, given the complexities of natural language, might not be directly discoverable in the original sentences. The proposed hybrid system performance reached an F-measure of 0.63, with precision at 0.76 and recall at 0.54 on the 2012 i2b2 Natural Language Processing corpus for the temporal relation (TLink) extraction task, achieving the highest precision and third highest f-measure among participating teams in the TLink track.
Temporal relation extraction; Clinical text mining; Automatic patient timeline; Natural Language Processing; Machine learning; Temporal graph
Recent research has shown that Twitter data analytics can have broad implications on public health research. However, its value for pharmacovigilance has been scantly studied – with health related forums and community support groups preferred for the task. We present a systematic study of tweets collected for 74 drugs to assess their value as sources of potential signals for adverse drug reactions (ADRs). We created an annotated corpus of 10,822 tweets. Each tweet was annotated for the presence or absence of ADR mentions, with the span and Unified Medical Language System (UMLS) concept ID noted for each ADR present. Using Cohen’s kappa1, we calculated the inter-annotator agreement (IAA) for the binary annotations to be 0.69. To demonstrate the utility of the corpus, we attempted a lexicon-based approach for concept extraction, with promising success (54.1% precision, 62.1% recall, and 57.8% F-measure). A subset of the corpus is freely available at: http://diego.asu.edu/downloads.
When attempting to identify a specific epilepsy syndrome, physicians are often unable to make or agree upon a diagnosis. This is further complicated by the fact that the current classification and diagnosis of epilepsy requires specialized training and the use of resources not typically available to the average clinician, such as training to recognize specific seizure types and electroencephalography (EEG)1–4. Even when training and resources are available, expert epileptologists often find it challenging to identify seizure types and to distinguish between specific epilepsy syndromes5. Information relevant to the diagnosis is present in narrative form in the medical record across several visits for an individual patient. Our ultimate goal is to create a system that will assist physicians in the diagnosis of epilepsy. This paper explores, as a baseline, text classification methods that attempt to correlate the narrative text features to the diagnosis of West syndrome (Infantile Spasms), using data from Phoenix Children’s Hospital (PCH). We tested these methods against a dataset containing known (coded) diagnosis of West Syndrome, and found the best performing method to have a precision / recall / f-measure of 76.8 / 66.7 / 71.4 when evaluated with 10-fold cross validation.
Finding gene functions discussed in the literature is an important task of information extraction (IE) from biomedical documents. Automated computational methodologies can significantly reduce the need for manual curation and improve quality of other related IE systems. We propose an open-IE method for the BioCreative IV GO shared task (subtask b), focused on finding gene function terms [Gene Ontology (GO) terms] for different genes in an article. The proposed open-IE approach is based on distributional semantic similarity over the GO terms. The method does not require annotated data for training, which makes it highly generalizable. We achieve an F-measure of 0.26 on the test-set in the official submission for BioCreative-GO shared task, the third highest F-measure among the seven participants in the shared task.
Database URL: https://code.google.com/p/rainbow-nlp/
Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation.
Women with Lynch syndrome have a 40–60% lifetime risk for developing endometrial cancer, a cancer associated with estrogen imbalance. The molecular basis for endometrial-specific tumorigenesis is unclear. Progestins inhibit estrogen-driven proliferation, and epidemiologic studies have demonstrated that progestin-containing oral contraceptives (OCP) reduce the risk of endometrial cancer by 50% in women at general population risk. It is unknown if they are effective in women with Lynch syndrome. Asymptomatic women age 25–50 with Lynch syndrome were randomized to receive the progestin compounds depo-Provera (depoMPA) or OCP for three months. An endometrial biopsy and transvaginal ultrasound were performed before and after treatment. Endometrial proliferation was evaluated as the primary endpoint. Histology and a panel of surrogate endpoint biomarkers were evaluated for each endometrial biopsy as secondary endpoints. A total of 51 women were enrolled, and 46 completed treatment. Two of the 51 women had complex hyperplasia with atypia at the baseline endometrial biopsy and were excluded from the study. Overall, both depoMPA and OCP induced a dramatic decrease in endometrial epithelial proliferation and microscopic changes in the endometrium characteristic of progestin action. Transvaginal ultrasound measurement of endometrial stripe was not a useful measure of endometrial response or baseline hyperplasia. These results demonstrate that women with Lynch syndrome do show an endometrial response to short term exogenous progestins, suggesting that OCP and depoMPA may be reasonable chemopreventive agents in this high-risk patient population.
endometrial cancer; chemoprevention; Lynch syndrome; progestin
Social media postings are rich in information that often remain hidden and inaccessible for automatic extraction due to inherent limitations of the site’s APIs, which mostly limit access via specific keyword-based searches (and limit both the number of keywords and the number of postings that are returned). When mining social media for drug mentions, one of the first problems to solve is how to derive a list of variants of the drug name (common misspellings) that can capture a sufficient number of postings. We present here an approach that filters the potential variants based on the intuition that, faced with the task of writing an unfamiliar, complex word (the drug name), users will tend to revert to phonetic spelling, and we thus give preference to variants that reflect the phonemes of the correct spelling. The algorithm allowed us to capture 50.4 – 56.0 % of the user comments using only about 18% of the variants.
Information Retrieval; Natural Language Processing and Free Text Data Mining; Spelling-Error
Zoonotic viruses represent emerging or re-emerging pathogens that pose significant public health threats throughout the world. It is therefore crucial to advance current surveillance mechanisms for these viruses through outlets such as phylogeography. Despite the abundance of zoonotic viral sequence data in publicly available databases such as GenBank, phylogeographic analysis of these viruses is often limited by the lack of adequate geographic metadata. However, many GenBank records include references to articles with more detailed information and automated systems may help extract this information efficiently and effectively. In this paper, we describe our efforts to determine the proportion of GenBank records with “insufficient” geographic metadata for seven well-studied viruses. We also evaluate the performance of four different Named Entity Recognition (NER) systems for automatically extracting related entities using a manually created gold-standard.
This research seeks to extend the process of novel therapeutic gene target discovery for the treatment of Alzheimer’s disease (AD). Gene-gene and gene-pathway annotation tools as well as human analysis are used to explore likely connections between potential gene targets and biochemical mechanisms of AD and associated genes. Rule-based annotation systems, such as GeneRanker, can be applied to the continuously growing volume of literature to extract relevant gene lists. The subsequent challenge is to abstract biological significance from associated genes to aid in discovery of novel therapeutic gene targets. Automatic annotation of genes deemed significant by data-driven assays and knowledge-driven analysis is limited. Therefore, human analysis is still crucial to exploring novel gene targets and new disease models. This research illustrates a method of analysis of an extracted gene list which lead to the discovery of KNG1 as a possible therapeutic target, suggests a connection between inflammation and AD pathogenesis.
To investigate the dosimetric impact of the heterogeneity dose calculation Acuros, a grid-based Boltzmann equation solver (GBBS), for brachytherapy in a cohort of cervical cancer patients.
Methods and Materials
The impact of heterogeneities was retrospectively assessed in treatment plans for 26 patients who had previously received 192Ir intracavitary brachytherapy for cervical cancer with computed tomography (CT)/magnetic resonance (MR)-compatible tandems and unshielded colpostats. The GBBS models sources, patient boundaries, applicators, and tissue heterogeneities. Multiple GBBS calculations were performed: with and without solid model applicator, with and without overriding the patient contour to 1g/cc muscle, and with and without overriding contrast materials to muscle or 2.25 g/cc bone. Impact of source and boundary modeling, applicator, tissue heterogeneities, and sensitivity of CT-to-material mapping of contrast were derived from the multiple calculations. TG-43 and the GBBS were compared for the following clinical dosimetric parameters: Manchester points A and B, ICRU report #38 rectal and bladder points, three and nine o'clock, and D2cc to the bladder, rectum, and sigmoid.
Points A, B, D2cc bladder, ICRU bladder, and three and nine o'clock were within 5% of TG-43 for all GBBS calculations. The source and boundary and applicator account for most of the differences between the GBBS and TG-43. The D2cc rectum (n=3), D2cc sigmoid (n=1), and ICRU rectum (n=6) had differences > 5% from TG-43 for the worst case incorrect mapping of contrast to bone. Clinical dosimetric parameters were within 5% of TG-43 when rectal and balloon contrast mapped to bone and radiopaque packing was not overridden.
The GBBS has minimal impact on clinical parameters for this cohort of GYN patients with unshielded applicators. The incorrect mapping of rectal and balloon contrast does not have a significant impact on clinical parameters. Rectal parameters may be sensitive to the mapping of radiopaque packing.
brachytherapy; intracavitary; 192Ir; grid-based Boltzmann solver; TG-43
To determine factors which may increase the likelihood of adverse drug events (ADEs) in recurrent endometrial cancer patients treated with pegylated liposomal doxorubicin (PLD) as well as this agent’s impact on clinical outcomes.
The treatment records of endometrial cancer patients who received PLD at The University of Texas, M.D. Anderson Cancer Center from 1996 to 2006 were reviewed. Patient demographics, PLD dose, ADEs, use of supportive care interventions, disease progression and survival were extracted. Logistical regression analysis was used to identify factors which were associated with higher incidence of ADEs and which influenced survival.
A total of 60 recurrent endometrial cancer patients were identified who experienced 122 ADEs. The most commonly reported ADEs were nausea (18.9%), palmar-plantar erythrodysesthesia (PPE) (16.4%), muscle weakness (12.3%), mucositis (10.7%), and peripheral neuropathy (9.8%). Seventeen patients (28%) required a dose reductiondue to ADEs. However, only five (8.3%) patients discontinued therapy because of toxicity. Cooling mechanisms were used in 19 patients to prevent PPE, although nine of these patients still experienced PPE. Treatment with six or more cycles of PLD was associated with increased incidence of neutropenia (p=0.045), peripheral neuropathy (p=0.004), and PPE (p<0.001). No differences in PFS or TTP was found between the doses of PLD, however there was an assessable trend toward increased survival with doses of 40mg/m2.
While there was no association with dose level and ADEs, more cycles received increased the incidence of toxicities, including PPE and neuropathy. There was no association between different doses of PLD and PFS or TTP.
Doxil; endometrial cancer; adverse effects; dose intensity
Resection of certain recurrent malignancies can prolong survival, but resection of recurrent pancreatic ductal adenocarcinoma is typically contraindicated because of poor outcomes.
All patients from 1992 to 2010 with recurrent pancreatic cancer after intended surgical cure were retrospectively evaluated. Clinicopathologic features were compared from patients who did and did not undergo subsequent reoperation with curative intent to identify factors associated with prolonged survival.
Twenty-one of 426 patients (5 %) with recurrent pancreatic cancer underwent potentially curative reoperation for solitary local-regional (n=7) or distant (n=14) recurrence. The median disease-free interval after initial resection among reoperative patients was longer for those with lung or local-regional recurrence (52.4 and 41.1 months, respectively) than for those with liver recurrence (7.6 months, p=0.006). The median interval between reoperation and second recurrence was longer in patients with lung recurrence (median not reached) than with liver or local-regional recurrence (6 and 9 months, respectively, p=0.023). Reoperative patients with an initial disease-free interval >20 months had a longer median survival than those who did not (92.3 versus 31.3 months, respectively; p=0.033).
Patients with a solitary pulmonary recurrence of pancreatic cancer after a prolonged disease-free interval should be considered for reoperation, as they are more likely to benefit from resection versus other sites of solitary recurrence.
Pancreatic ductal adenocarcinoma; Metastasectomy; Reoperation; Locoregional recurrence
Because of privacy concerns and the expense involved in creating an annotated corpus, the existing small-annotated corpora might not have sufficient examples for learning to statistically extract all the named-entities precisely. In this work, we evaluate what value may lie in automatically generated features based on distributional semantics when using machine-learning named entity recognition (NER). The features we generated and experimented with include n-nearest words, support vector machine (SVM)-regions, and term clustering, all of which are considered distributional semantic features. The addition of the n-nearest words feature resulted in a greater increase in F-score than by using a manually constructed lexicon to a baseline system. Although the need for relatively small-annotated corpora for retraining is not obviated, lexicons empirically derived from unannotated text can not only supplement manually created lexicons, but also replace them. This phenomenon is observed in extracting concepts from both biomedical literature and clinical notes.
natural language processing; distributional semantics; concept extraction; named entity recognition; empirical lexical resources
No reliable methods currently exist to predict patient response to intravesical immunotherapy with bacillus Calmette-Guérin (BCG), given after transurethral resection for high-risk non-muscle-invasive bladder cancer. We initiated a prospective clinical trial to determine whether fluorescence in situ hybridization (FISH) results during BCG immunotherapy can predict therapy failure.
Materials and Methods
Candidates for standard of care BCG were offered participation in a clinical trial. FISH was performed prior to BCG and at 6 weeks, 3 months, and 6 months during BCG therapy with maintenance. Cox proportional hazards regression was used to assess the relationship between FISH results and tumor recurrence or progression; the Kaplan-Meier product limit method was used to estimate recurrence- and progression-free survival.
One hundred twenty-six patients participated. At a median follow-up of 24 months, 31% of patients had recurrent tumors and 14% experienced disease progression. Patients who had positive FISH results during BCG therapy were 3-5 times more likely than those who had negative FISH results to develop recurrent tumors and 5-13 times more likely to experience disease progression (p < 0.01). The timing of positive FISH results also affected outcome; for example, patients with a negative FISH result at baseline, 6 weeks, and 3 months demonstrated an 8.3% recurrence rate, compared to 48.1% in those with a positive FISH result at all three time points.
FISH results can identify patients who are at risk of tumor recurrence and progression during BCG immunotherapy. This information may be used to counsel patients about alternative treatment strategies.
bladder cancer; BCG; FISH; response; prediction
Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text.
The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task.
The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged f-measure for exact match increased from 80.3% to 82.3% and the micro-averaged f-measure based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data.
NLP; Information extraction; NER; Distributional Semantics; Clinical Informatics
Phylogeography is a field that focuses on the geographical lineages of species such as vertebrates or viruses. Here, geographical data, such as location of a species or viral host is as important as the sequence information extracted from the species. Together, this information can help illustrate the migration of the species over time within a geographical area, the impact of geography over the evolutionary history, or the expected population of the species within the area. Molecular sequence data from NCBI, specifically GenBank, provide an abundance of available sequence data for phylogeography. However, geographical data is inconsistently represented and sparse across GenBank entries. This can impede analysis and in situations where the geographical information is inferred, and potentially lead to erroneous results. In this paper, we describe the current state of geographical data in GenBank, and illustrate how automated processing techniques such as named entity recognition, can enhance the geographical data available for phylogeographic studies.
Phylogeography; Databases; Nucleic Acid; Geographic Locations; Bioinformatics
Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.
A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%.
The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.
Summary: Identifying mentions of named entities, such as genes or diseases, and normalizing them to database identifiers have become an important step in many text and data mining pipelines. Despite this need, very few entity normalization systems are publicly available as source code or web services for biomedical text mining. Here we present the Gnat Java library for text retrieval, named entity recognition, and normalization of gene and protein mentions in biomedical text. The library can be used as a component to be integrated with other text-mining systems, as a framework to add user-specific extensions, and as an efficient stand-alone application for the identification of gene and protein names for data analysis. On the BioCreative III test data, the current version of Gnat achieves a Tap-20 score of 0.1987.
Availability: The library and web services are implemented in Java and the sources are available from http://gnat.sourceforge.net.
Rapid growth of online health social networks has enabled patients to communicate more easily with each other. This way of exchange of opinions and experiences has provided a rich source of information about drugs and their effectiveness and more importantly, their possible adverse reactions. We developed a system to automatically extract mentions of Adverse Drug Reactions (ADRs) from user reviews about drugs in social network websites by mining a set of language patterns. The system applied association rule mining on a set of annotated comments to extract the underlying patterns of colloquial expressions about adverse effects. The patterns were tested on a set of unseen comments to evaluate their performance. We reached to precision of 70.01% and recall of 66.32% and F-measure of 67.96%.
BioSimplify is an open source tool written in Java that introduces and facilitates the use of a novel model for sentence simplification tuned for automatic discourse analysis and information extraction (as opposed to sentence simplification for improving human readability). The model is based on a “shot-gun” approach that produces many different (simpler) versions of the original sentence by combining variants of its constituent elements. This tool is optimized for processing biomedical scientific literature such as the abstracts indexed in PubMed. We tested our tool on its impact to the task of PPI extraction and it improved the f-score of the PPI tool by around 7%, with an improvement in recall of around 20%. The BioSimplify tool and test corpus can be downloaded from https://biosimplify.sourceforge.net
To evaluate the performance of the Human Papillomavirus High-Risk DNA test in patients 30 years and older.
Materials and Methods
Screening (N=835) and diagnosis (N=518) groups were defined based on prior Papanicolaou smear results as part of a clinical trial for cervical cancer detection. We compared the Hybrid Capture II® (HCII) test result to the worst histological report. We used cervical intraepithelial neoplasia (CIN) 2/3 or worse as the reference of disease. We calculated sensitivities, specificities, positive and negative likelihood ratios (LR+ and LR−), receiver operating characteristic (ROC) curves, and areas under the ROC curves for the HCII test. We also considered alternative strategies, including Papanicolaou smear, a combination of Papanicolaou smear and the HCII test, a sequence of Papanicolaou smear followed by the HCII test, and a sequence of the HCII test followed by Papanicolaou smear.
For the screening group, the sensitivity was 0.69 and the specificity was 0.93; the area under the ROC curve was 0.81. The LR+ and LR− were 10.24 and 0.34, respectively. For the diagnosis group, the sensitivity was 0.88 and the specificity was 0.78; the area under the ROC curve was 0.83. The LR+ and LR− were 4.06 and 0.14, respectively. Sequential testing showed little or no improvement over the combination testing.
The HCII test in the screening group had a greater LR+ for the detection of CIN 2/3 or worse. HCII testing may be an additional screening tool for cervical cancer in women 30 years and older.
cervical intraepithelial neoplasia; cervix neoplasms; DNA probes HPV; sensitivity and specificity