An enhanced algorithm for semantic-based feature reduction in spam filtering.

With the advent and improvement of ontological dictionaries (WordNet, Babelnet), the use of synsets-based text representations is gaining popularity in classification tasks. More recently, ontological dictionaries were used for reducing dimensionality in this kind of representation (e.g., Semantic Dimensionality Reduction System (SDRS) (Vélez de Mendizabal et al., 2020)). These approaches are based on the combination of semantically related columns by taking advantage of semantic information extracted from ontological dictionaries. Their main advantage is that they not only eliminate features but can also combine them, minimizing (low-loss) or avoiding (lossless) the loss of information. The most recent (and accurate) techniques included in this group are based on using evolutionary algorithms to find how many features can be grouped to reduce false positive (FP) and false negative (FN) errors obtained. The main limitation of these evolutionary-based schemes is the computational requirements derived from the use of optimization algorithms. The contribution of this study is a new lossless feature reduction scheme exploiting information from ontological dictionaries, which achieves slightly better accuracy (specially in FP errors) than optimization-based approaches but using far fewer computational resources. Instead of using computationally expensive evolutionary algorithms, our proposal determines whether two columns (synsets) can be combined by observing whether the instances included in a dataset (e.g., training dataset) containing these synsets are mostly of the same class. The study includes experiments using three datasets and a detailed comparison with two previous optimization-based approaches.

El concepto de umbral craneofacial y su código de lectura ABC / The concept of craniofacial threshold and its ABC reading code

Durante el crecimiento y desarrollo de la cabeza, ésta lo hace en diferentes direcciones y proporciones, habiendo un límite entre la armonía /desarmonía conocido como umbral. Se hace referencia a este concepto, la forma de escribirlo y leerlo por medio de un código que lo simboliza. Objetivo: Poner al alcance de la comunidad médica un código de lectura e identificación de fenotipos craneofaciales sindrómicos y no sindrómicos. Conclusiones: Se considera que este concepto de umbral craneofacial y su código de lectura pueden ser usados en la enseñanza e investigación de la armonía-desarmonía durante el crecimiento y desarrollo de la cabeza, resultando ser de gran utilidad en la comprensión rápida y sencilla de la lectura del fenotipo craneofacial (AU)

During the growth and development of the head, it does so in different directions and proportions, there being a limit between the harmony / disharmony known as threshold. Reference is made to this concept, the way of writing it and reading it by means of a code that symbolizes it. Objective: To put within reach of the medical community, a code of reading and identification of syndromic and non-syndromic craniofacial phenotypes. Conclusions: It is considered that this concept of a craniofacial threshold and its reading code can be used in the teaching and research of harmony / disharmony during the growth and development of the head, being very useful in the quick and easy comprehension of the reading of the craniofacial phenotype (AU)

GC4S: A bioinformatics-oriented Java software library of reusable graphical user interface components.

Modern bioinformatics and computational biology are fields of study driven by the availability of effective software required for conducting appropriate research tasks. Apart from providing reliable and fast implementations of different data analysis algorithms, these software applications should also be clear and easy to use through proper user interfaces, providing appropriate data management and visualization capabilities. In this regard, the user experience obtained by interacting with these applications via their Graphical User Interfaces (GUI) is a key factor for their final success and real utility for researchers. Despite the existence of different packages and applications focused on advanced data visualization, there is a lack of specific libraries providing pertinent GUI components able to help scientific bioinformatics software developers. To that end, this paper introduces GC4S, a bioinformatics-oriented collection of high-level, extensible, and reusable Java GUI elements specifically designed to speed up bioinformatics software development. Within GC4S, developers of new applications can focus on the specific GUI requirements of their projects, relying on GC4S for generalities and abstractions. GC4S is free software distributed under the terms of GNU Lesser General Public License and both source code and documentation are publicly available at http://www.sing-group.org/gc4s.

WARCProcessor: An Integrative Tool for Building and Management of Web Spam Corpora.

In this work we present the design and implementation of WARCProcessor, a novel multiplatform integrative tool aimed to build scientific datasets to facilitate experimentation in web spam research. The developed application allows the user to specify multiple criteria that change the way in which new corpora are generated whilst reducing the number of repetitive and error prone tasks related with existing corpus maintenance. For this goal, WARCProcessor supports up to six commonly used data sources for web spam research, being able to store output corpus in standard WARC format together with complementary metadata files. Additionally, the application facilitates the automatic and concurrent download of web sites from Internet, giving the possibility of configuring the deep of the links to be followed as well as the behaviour when redirected URLs appear. WARCProcessor supports both an interactive GUI interface and a command line utility for being executed in background.

Cell Culture of Bursera linanoe in a Stirred Tank Bioreactor for Production of Linalool and Linalyl Acetate.

Bursera linanoe cell suspension cultures were initiated from callus grown in Murashige and Skoog medium supplemented with naphthalene acetic acid (3.0 mg L⁻¹) and 6-benzylaminopurine (0.5 mg L⁻¹). In flasks, B. linanoe cell cultures grew over a 9 day period, reaching a maximum biomass of 11.16 g DW L⁻¹. Throughout the growth phase, cell viability was constant at 60 - 70%. In contrast, B. linanoe cells growing in a bioreactor achieved a maximum biomass of 22.26 g DW L⁻¹ (after 7 days), and cell viability was constant at 75 - 85%. Production of linalool and linalyl acetate in the bioreactor (3.02 and 2.40 mg g⁻¹ DW, respectively) was significantly greater than that achieved from cells in flask cultures (1.05 and 0.97 mg g⁻¹ DW, respectively). B. linanoe cell suspension culture has potential as an alternative method for the production of essential oils.

Evaluating the effect of unbalanced data in biomedical document classification.

Nowadays, document classification has become an interesting research field. Partly, this is due to the increasing availability of biomedical information in digital form which is necessary to catalogue and organize. In this context, machine learning techniques are usually applied to text classification by using a general inductive process that automatically builds a text classifier from a set of pre-classified documents. Related with this domain, imbalanced data is a well-known problem in many practical applications of knowledge discovery and its effects on the performance of standard classifiers are remarkable. In this paper, we investigate the application of a Bayesian Network (BN) model for the triage of documents, which are represented by the association of different MeSH terms. Our results show that BNs are adequate for describing conditional independencies between MeSH terms and that MeSH ontology is a valuable resource for representing Medline documents at different abstraction levels. Moreover, we perform an extensive experimental evaluation to investigate if the classification of Medline documents using a BN classifier poses additional challenges when dealing with class-imbalanced prediction. The evaluation involves two methods, under-sampling and cost-sensitive learning. We conclude that BN classifier is sensitive to both balancing strategies and existing techniques can improve its overall performance.