Machine learning applied to the h index of colombian authors with publications in scopus

Our research aims to establish how to predict the H index of Colombian authors with publications in Scopus until 2016. The selection of the date was because, as mentioned earlier, the number of documents indexed per year exceeded 10,000 and they obtained the highest number of documents cited. To accomplish this purpose, a quantitative, nonexperimental, cross-sectional, descriptive, explanatory, and predictive research was designed using supervised learning algorithms. These were applied to information from 8,840 Colombian authors. Among the findings we can highlight that: (i) Colombia is in the fifth position in the scope of countries of South America and the Caribbean, in terms of the number of products and citations; (ii) the largest number of Colombian authors with products in Scopus until 2016, belonged mainly to the area of natural sciences, followed by medical sciences and health; (iii) most of the Colombian authors were men (64.2%, or 5,442) and they have higher H index rates than women; (iv) using random cross validation for 10 iterations, the methods with the best predictive value using R2 and the minimization of mean absolute error (MAE) correspond to: AdaBoost (96.6% and 0.397, respectively); Random Forest (96.8% and 0.431, respectively); KNN (94.4% and 0.525, respectively); Tree (94.9% and 0.53, respectively); and Neural Network (93.3% and 0.7, respectively); and (v) the variables that help predict the H index in the case of the Colombian authors, in addition to the citations, correspond to: the quantity of products, number of products in Q1, and international collaboratio

Recognizing speculative language in biomedical research articles: a linguistically motivated perspective

We explore a linguistically motivated approach to the problem of recognizing speculative language (“hedging”) in biomedical research articles. We describe a method, which draws on prior linguistic work as well as existing lexical resources and extends them by introducing syntactic patterns and a simple weighting scheme to estimate the speculation level of the sentences. We show that speculative language can be recognized successfully with such an approach, discuss some shortcomings of the method and point out future research possibilities.

Constructing a semantic predication gold standard from the biomedical literature

<p>Abstract</p> <p>Background</p> <p>Semantic relations increasingly underpin biomedical text mining and knowledge discovery applications. The success of such practical applications crucially depends on the quality of extracted relations, which can be assessed against a gold standard reference. Most such references in biomedical text mining focus on narrow subdomains and adopt different semantic representations, rendering them difficult to use for benchmarking independently developed relation extraction systems. In this article, we present a multi-phase gold standard annotation study, in which we annotated 500 sentences randomly selected from MEDLINE abstracts on a wide range of biomedical topics with 1371 semantic predications. The UMLS Metathesaurus served as the main source for conceptual information and the UMLS Semantic Network for relational information. We measured interannotator agreement and analyzed the annotations closely to identify some of the challenges in annotating biomedical text with relations based on an ontology or a terminology.</p> <p>Results</p> <p>We obtain fair to moderate interannotator agreement in the practice phase (0.378-0.475). With improved guidelines and additional semantic equivalence criteria, the agreement increases by 12% (0.415 to 0.536) in the main annotation phase. In addition, we find that agreement increases to 0.688 when the agreement calculation is limited to those predications that are based only on the explicitly provided UMLS concepts and relations.</p> <p>Conclusions</p> <p>While interannotator agreement in the practice phase confirms that conceptual annotation is a challenging task, the increasing agreement in the main annotation phase points out that an acceptable level of agreement can be achieved in multiple iterations, by setting stricter guidelines and establishing semantic equivalence criteria. Mapping text to ontological concepts emerges as the main challenge in conceptual annotation. Annotating predications involving biomolecular entities and processes is particularly challenging. While the resulting gold standard is mainly intended to serve as a test collection for our semantic interpreter, we believe that the lessons learned are applicable generally.</p

Detecting modification of biomedical events using a deep parsing approach

<p>Abstract</p> <p>Background</p> <p>This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. <it>analysis of IkappaBalpha phosphorylation</it>, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. <it>inhibition of IkappaBalpha phosphorylation</it>, where phosphorylation did <it>not </it>occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser.</p> <p>Method</p> <p>To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the <it>RASP </it>parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm.</p> <p>Results</p> <p>Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features.</p> <p>Conclusions</p> <p>Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.</p

Caipirini: using gene sets to rank literature

<p>Abstract</p> <p>Background</p> <p>Keeping up-to-date with bioscience literature is becoming increasingly challenging. Several recent methods help meet this challenge by allowing literature search to be launched based on lists of abstracts that the user judges to be 'interesting'. Some methods go further by allowing the user to provide a second input set of 'uninteresting' abstracts; these two input sets are then used to search and rank literature by relevance. In this work we present the service 'Caipirini' (<url>http://caipirini.org</url>) that also allows two input sets, but takes the novel approach of allowing ranking of literature based on one or more sets of genes.</p> <p>Results</p> <p>To evaluate the usefulness of Caipirini, we used two test cases, one related to the human cell cycle, and a second related to disease defense mechanisms in <it>Arabidopsis thaliana</it>. In both cases, the new method achieved high precision in finding literature related to the biological mechanisms underlying the input data sets.</p> <p>Conclusions</p> <p>To our knowledge Caipirini is the first service enabling literature search directly based on biological relevance to gene sets; thus, Caipirini gives the research community a new way to unlock hidden knowledge from gene sets derived via high-throughput experiments.</p

Leukotriene biosynthesis inhibition ameliorates acute lung injury following hemorrhagic shock in rats

<p>Abstract</p> <p>Background</p> <p>Hemorrhagic shock followed by resuscitation is conceived as an insult frequently induces a systemic inflammatory response syndrome and oxidative stress that results in multiple-organ dysfunction syndrome including acute lung injury. MK-886 is a leukotriene biosynthesis inhibitor exerts an anti inflammatory and antioxidant activity.</p> <p>Objectives</p> <p>The objective of present study was to assess the possible protective effect of MK-886 against hemorrhagic shock-induced acute lung injury via interfering with inflammatory and oxidative pathways.</p> <p>Materials and methods</p> <p>Eighteen adult Albino rats were assigned to three groups each containing six rats: group I, sham group, rats underwent all surgical instrumentation but neither hemorrhagic shock nor resuscitation was done; group II, Rats underwent hemorrhagic shock (HS) for 1 hr then resuscitated with Ringer's lactate (1 hr) (induced untreated group, HS); group III, HS + MK-886 (0.6 mg/kg i.p. injection 30 min before the induction of HS, and the same dose was repeated just before reperfusion period). At the end of experiment (2 hr after completion of resuscitation), blood samples were collected for measurement of serum tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). The trachea was then isolated and bronchoalveolar lavage fluid (BALF) was carried out for measurement of leukotriene B₄(LTB₄), leukotriene C₄(LTC₄) and total protein. The lungs were harvested, excised and the left lung was homogenized for measurement of malondialdehyde (MDA) and reduced glutathione (GSH) and the right lung was fixed in 10% formalin for histological examination.</p> <p>Results</p> <p>MK-886 treatment significantly reduced the total lung injury score compared with the HS group (<it>P </it>< 0.05). MK-886 also significantly decreased serum TNF-α & IL-6; lung MDA; BALF LTB₄, LTC₄& total protein compared with the HS group (<it>P </it>< 0.05). MK-886 treatment significantly prevented the decrease in the lung GSH levels compared with the HS group (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>The results of the present study reveal that MK-886 may ameliorate lung injury in shocked rats via interfering with inflammatory and oxidative pathways implicating the role of leukotrienes in the pathogenesis of hemorrhagic shock-induced lung inflammation.</p