Florentino Fdez-Riverola

ALTER: program-oriented conversion of DNA and protein alignments

ALTER is an open web-based tool to transform between different multiple sequence alignment formats. The originality of ALTER lies in the fact that it focuses on the specifications of mainstream alignment and analysis programs rather than on the conversion among more or less specific formats. In addition, ALTER is capable of identify and remove identical sequences during the transformation process. Besides its user-friendly environment, ALTER allows access to its functionalities in a programmatic way through a Representational State Transfer web service. ALTER’s front-end and its API are freely available at http://sing.ei.uvigo.es/ALTER/ and http://sing.ei.uvigo.es/ALTER/api/, respectively.

Applying lazy learning algorithms to tackle concept drift in spam filtering

A great amount of machine learning techniques have been applied to problems where data is collected over an extended period of time. However, the disadvantage with many real-world applications is that the distribution underlying the data is likely to change over time. In these situations, a problem that many global eager learners face is their inability to adapt to local concept drift. Concept drift in spam is particularly difficult as the spammers actively change the nature of their messages to elude spam filters. Algorithms that track concept drift must be able to identify a change in the target concept (spam or legitimate e-mails) without direct knowledge of the underlying shift in distribution. In this paper we show how a previously successful instance-based reasoning e-mail filtering model can be improved in order to better track concept drift in spam domain. Our proposal is based on the definition of two complementary techniques able to select both terms and e-mails representative of the current situation. The enhanced system is evaluated against other well-known successful lazy learning approaches in two scenarios, all within a cost-sensitive framework. The results obtained from the experiments carried out are very promising and back up the idea that instance-based reasoning systems can offer a number of advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain.

SpamHunting: An instance-based reasoning system for spam labelling and filtering

In this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable enhanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the target case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules.

gene-CBR: A CASE-BASED REASONIG TOOL FOR CANCER DIAGNOSIS USING MICROARRAY DATA SETS

Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well‐known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state‐of‐the‐art algorithms is crucial to the development of successful applications. This paper presents gene‐CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case‐based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty‐three adult patients with cancer plus a group of six cases corresponding to healthy persons.

FSfRT: Forecasting System for Red Tides

A hybrid neuro-symbolic problem-solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation, it has been found that a hybrid case-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The system employs a case-based reasoning model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used at a different stage of the reasoning cycle of the case-based reasoning system to retrieve historical data, to adapt it to the present problem and to review the proposed solution. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments, in which the system operated in a real environment, are presented.

CBR based system for forecasting red tides

A hybrid neuro-symbolic problem solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. The system employs a case-based reasoning model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used in a different stage of the reasoning cycle of the case-based reasoning system to retrieve, adapt and review the proposed solution to the present problem. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments are presented.

AIBench: A rapid application development framework for translational research in biomedicine

Applied research in both biomedical discovery and translational medicine today often requires the rapid development of fully featured applications containing both advanced and specific functionalities, for real use in practice. In this context, new tools are demanded that allow for efficient generation, deployment and reutilization of such biomedical applications as well as their associated functionalities. In this context this paper presents AIBench, an open-source Java desktop application framework for scientific software development with the goal of providing support to both fundamental and applied research in the domain of translational biomedicine. AIBench incorporates a powerful plug-in engine, a flexible scripting platform and takes advantage of Java annotations, reflection and various design principles in order to make it easy to use, lightweight and non-intrusive. By following a basic input-processing-output life cycle, it is possible to fully develop multiplatform applications using only three types of concepts: operations, data-types and views. The framework automatically provides functionalities that are present in a typical scientific application including user parameter definition, logging facilities, multi-threading execution, experiment repeatability and user interface workflow management, among others. The proposed framework architecture defines a reusable component model which also allows assembling new applications by the reuse of libraries from past projects or third-party software.

Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification

Nowadays, spam represents an extensive subset of the information delivered through Internet involving all unsolicited and disturbing communications received while using different services including e-mail, weblogs and forums. In this context, this paper reviews and brings together previous approaches and novel alternatives for applying rough set (RS) theory to the spam filtering domain by defining three different rule execution schemes: MFD (most frequent decision), LNO (largest number of objects) and LTS (largest total strength). With the goal of correctly assessing the suitability of the proposed algorithms, we specifically address and analyse significant questions for appropriate model validation like corpus selection, preprocessing and representational issues, as well as different specific benchmarking measures. From the experiments carried out using several execution schemes for selecting appropriate decision rules generated by rough sets, we conclude that the proposed algorithms can outperform other well-known anti-spam filtering techniques such as support vector machines (SVM), Adaboost and different types of Bayes classifiers.

WhichGenes: a web-based tool for gathering, building, storing and exporting gene sets with application in gene set enrichment analysis

WhichGenes is a web-based interactive gene set building tool offering a very simple interface to extract always-updated gene lists from multiple databases and unstructured biological data sources. While the user can specify new gene sets of interest by following a simple four-step wizard, the tool is able to run several queries in parallel. Every time a new set is generated, it is automatically added to the private gene-set cart and the user is notified by an e-mail containing a direct link to the new set stored in the server. WhichGenes provides functionalities to edit, delete and rename existing sets as well as the capability of generating new ones by combining previous existing sets (intersection, union and difference operators). The user can export his sets configuring the output format and selecting among multiple gene identifiers. In addition to the user-friendly environment, WhichGenes allows programmers to access its functionalities in a programmatic way through a Representational State Transfer web service. WhichGenes front-end is freely available at http://www.whichgenes.org/, WhichGenes API is accessible at http://www.whichgenes.org/api/.

A comparative performance study of feature selection methods for the anti-spam filtering domain

In this paper we analyse the strengths and weaknesses of the mainly used feature selection methods in text categorization when they are applied to the spam problem domain. Several experiments with different feature selection methods and content-based filtering techniques are carried out and discussed. Information Gain, χ2-text, Mutual Information and Document Frequency feature selection methods have been analysed in conjunction with Naive Bayes, boosting trees, Support Vector Machines and ECUE models in different scenarios. From the experiments carried out the underlying ideas behind feature selection methods are identified and applied for improving the feature selection process of SpamHunting, a novel anti-spam filtering software able to accurate classify suspicious e-mails.