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Volume 8 Issue 3 - Special Issue: Selected extended papers of the 5th International Conference on Practical Applications of Computational Biology and Bioinformatics, Salamanca, Spain, 2011 / Guest Editors: Juan M. Corchado, Miguel Rocha, Florentino Fdez-Riverola and Alfonso Valencia

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Open Access October 18, 2016

Modelling Proteolytic Enzymes With Support Vector Machines

Lionel Morgado, Carlos Pereira, Paula Veríssimo, António Dourado

Page range: 1-15

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Abstract

The strong activity felt in proteomics during the last decade created huge amounts of data, for which the knowledge is limited. Retrieving information from these proteins is the next step. For that, computational techniques are indispensable. Although there is not yet a silver bullet approach to solve the problem of enzyme detection and classification, machine learning formulations such as the state-of-the-art Support Vector Machine (SVM) appear among the most reliable options. A SVM based framework for peptidase analysis, that recognizes the hierarchies demarked in the MEROPS database is presented. Feature selection with SVM-RFE is used to improve the discriminative models and build classifiers computationally more efficient than alignment based techniques.

Open Access October 18, 2016

Rapid development of proteomic applications with the AIBench framework

Hugo López-Fernández, Miguel Reboiro-Jato, Daniel Glez-Peña, José R. Méndez Reboredo, Hugo M. Santos, Ricardo J. Carreira, José L. Capelo-Martínez, Florentino Fdez-Riverola

Page range: 16-30

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Abstract

In this paper we present two case studies of Proteomics applications development using the AIBench framework, a Java desktop application framework mainly focused in scientific software development. The applications presented in this work are Decision Peptide- Driven, for rapid and accurate protein quantification, and Bacterial Identification, for Tuberculosis biomarker search and diagnosis. Both tools work with mass spectrometry data, specifically with MALDI-TOF spectra, minimizing the time required to process and analyze the experimental data.

Open Access October 18, 2016

Inter-dinucleotide distances in the human genome: an analysis of the whole-genome and protein-coding distributions

Carlos A. C. Bastos, Vera Afreixo, Armando J. Pinho, Sara P. Garcia, João M. O. S. Rodrigues, Paulo J. S. G. Ferreira

Page range: 31-42

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Abstract

We study the inter-dinucleotide distance distributions in the human genome, both in the whole-genome and protein-coding regions. The inter-dinucleotide distance is defined as the distance to the next occurrence of the same dinucleotide. We consider the 16 sequences of inter-dinucleotide distances and two reading frames. Our results show a period-3 oscillation in the protein-coding inter-dinucleotide distance distributions that is absent from the whole-genome distributions. We also compare the distance distribution of each dinucleotide to a reference distribution, that of a random sequence generated with the same dinucleotide abundances, revealing the CG dinucleotide as the one with the highest cumulative relative error for the first 60 distances. Moreover, the distance distribution of each dinucleotide is compared to the distance distribution of all other dinucleotides using the Kullback-Leibler divergence. We find that the distance distribution of a dinucleotide and that of its reversed complement are very similar, hence, the divergence between them is very small. This is an interesting finding that may give evidence of a stronger parity rule than Chargaff’s second parity rule.

Open Access October 18, 2016

Model Reference Adaptive Scheme for Multi-drug Infusion for Blood Pressure Control

S. Enbiya, F. Mahieddine, A. Hossain

Page range: 43-56

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Abstract

Using multiple interacting drugs to control both the mean arterial pressure (MAP) and cardiac output (CO) of patients with different sensitivity to drugs is a challenging task which this paper attempts to address. A multivariable model reference adaptive control (MRAC) algorithm is developed using a two-input, two-output patient model. The control objective is to maintain the homodynamic variables MAP and CO at the normal values by simultaneously administering two drugs; sodium nitroprusside (SNP) and dopamine (DPM). Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters.

Open Access October 18, 2016

Parallel Niche Pareto AlineaGA – an Evolutionary Multiobjective approach on Multiple Sequence Alignment

Fernando José Mateus da Silva, Juan Manuel Sánchez Pérez, Juan Antonio Gómez Pulido, Miguel A. Vega Rodríguez

Page range: 57-72

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Abstract

Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, presenting results that are better than those provided by the sum of several sequential Genetic Algorithms. Although these methods are often used to optimize a single objective, they can also be used in multidimensional domains, finding all possible tradeoffs among multiple conflicting objectives. Parallel AlineaGA is an Evolutionary Algorithm which uses a Parallel Genetic Algorithm for performing multiple sequence alignment. We now present the Parallel Niche Pareto AlineaGA, a multiobjective version of Parallel AlineaGA.We compare the performance of both versions using eight BAliBASE datasets. We also measure up the quality of the obtained solutions with the ones achieved by T-Coffee and ClustalW2, allowing us to observe that our algorithm reaches for better solutions in the majority of the datasets.

Open Access October 18, 2016

Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series

André V. Carreiro, Orlando Anunciação, João A. Carriço, Sara C. Madeira

Page range: 73-89

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Abstract

The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perspective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient’s response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series.

Open Access October 18, 2016

Improving imbalanced scientific text classification using sampling strategies and dictionaries

L. Borrajo, R. Romero, E. L. Iglesias, C. M. Redondo Marey

Page range: 90-104

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Abstract

Many real applications have the imbalanced class distribution problem, where one of the classes is represented by a very small number of cases compared to the other classes. One of the systems affected are those related to the recovery and classification of scientific documentation. Sampling strategies such as Oversampling and Subsampling are popular in tackling the problem of class imbalance. In this work, we study their effects on three types of classifiers (Knn, SVM and Naive-Bayes) when they are applied to search on the PubMed scientific database. Another purpose of this paper is to study the use of dictionaries in the classification of biomedical texts. Experiments are conducted with three different dictionaries (BioCreative, NLPBA, and an ad-hoc subset of the UniProt database named Protein) using the mentioned classifiers and sampling strategies. Best results were obtained with NLPBA and Protein dictionaries and the SVM classifier using the Subsampling balancing technique. These results were compared with those ob- tained by other authors using the TREC Genomics 2005 public corpus.

Open Access October 18, 2016

Evaluating the effect of unbalanced data in biomedical document classification

Rosalía Laza, Reyes Pavón, Miguel Reboiro-Jato, Florentino Fdez-Riverola

Page range: 105-117

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Abstract

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.

Open Access October 18, 2016

Classification methods for finding articles describing protein-protein interactions in PubMed

Sérgio Matos, José Luis Oliveira

Page range: 118-129

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Abstract

With the rapid expansion in the number of published papers in the biomedical field, finding relevant articles has become a demanding task for researchers. This has led to increasing interest in the use of text mining tools that help search the literature and identify the most relevant documents or information. One specific topic of interest is related to the identification of articles that might be used for extracting protein-protein interactions. Using the BioCreative III Article Classification Task dataset, composed of PubMed abstracts classified as relevant or non-relevant for describing protein-protein interactions, we compare different classification methods with different sets of features. The best results - area under the interpolated precision-recall curve of 0.654 - indicate that the proposed classification strategy could be incorporated in the database curation workflows in order to prioritize articles for extraction of protein-protein interactions. Furthermore, we also analysed the use of this method for ranking documents resulting from general PubMed queries, and propose that this approach could be useful for general researchers looking for publications describing protein-protein interactions within a particular topic of interest.

Open Access October 18, 2016

Bioinformatic tools help molecular characterization of Perkinsus olseni differentially expressed genes

Rita M. T. Ascenso

Page range: 130-140

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Abstract

In the 80ies, in Southern Europe and in particular in Ria Formosa there was an episode of heavy mortality of the economically relevant clam Ruditapes (R.) decussatus associated with a debilitating disease (Perkinsosis) caused by Perkinsus olseni. This protozoan parasite was poorly known concerning its’ differential transcriptome in response to its host, R. decussatus. This laboratory available protozoan system was used to identify parasite genes related to host interaction. Beyond the application of molecular biology technologies and methodologies, only the help of Bioinformatics tools allowed to analyze the results of the study. The strategy started with SSH technique, allowing the identification of parasite up-regulated genes in response to its natural host, then a macroarray was constructed and hybridized to characterize the parasite genes expression when exposed to bivalves hemolymph from permissive host (R. decussatus), resistant host (R. philippinarum) and non permissive bivalve (Donax trunculus) that cohabit in the same or adjacent habitats in Southern Portugal. Genes and respective peptides full molecular characterization depended on several Bioinformatic tools application. Also a new Bioinformatic tool was developed.

Open Access October 18, 2016

Tracking T and B cells from two-photon microscopy imaging using constrained SMC clusters

D. Olivieri, J. Faro, I. Gomez-Conde, C. E. Tadokoro

Page range: 141-157

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Abstract

This paper describes a novel software algorithm, called constrained Sequential Monte Carlo (SMC) clusters, for tracking a large collection of individual cells from intra-vital two-photon microscopy image sequences. We show how our method and software tool, implemented in python, is useful for quantifying the motility of T and B lymphocytes involved in an immune response vs lymphocytes under non immune conditions. We describe the theory behind our algorithm and briefly discuss the architecture of our software. Finally, we demonstrate both the functionality and utility of software by applying it to two practical examples from videos displaying lymphocyte motility in B cell zones (follicles) and T cell zones of lymph nodes.

Open Access October 18, 2016

Predicting protein distance maps according to physicochemical properties

Gualberto Asencio Cortés, Jesús A. Aguilar-Ruiz

Page range: 158-175

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Abstract

The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein con- sists in determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, we used a set of 30 physicochemical amino acid properties selected from the AAindex database. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. We performed several preliminary analysis of the protein physicochemical data distributions using 3D surfaces. Three main pattern types were found in 3D surfaces, thus it is possible to extract rules in order to predict distances between amino acids according to their physicochemical properties. We performed an experimental validation of our method using five non-homologous protein sets and we showed the generality of this method and its prediction quality using the amino acid properties considered. Finally, we included a study of the algorithm efficiency according to the number of most similar fragments considered and we notably improved the precision with the studied proteins sets.

Open Access October 18, 2016

A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies

Rui Camacho, Max Pereira, Vítor Santos Costa, Nuno A. Fonseca, Carlos Adriano, Carlos J. V. Simões, Rui M. M. Brito

Page range: 176-194

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Abstract

It has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current “target-rich, lead-poor” scenario. Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal - iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms.

Open Access October 18, 2016

A Study of the Short and Long-term Regulation of E. coli Metabolic Pathways

Anália Lourenço, Sónia Carneiro, José P. Pinto, Miguel Rocha, Eugénio C. Ferreira, Isabel Rocha

Page range: 195-209

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Abstract

The present study addresses the regulatory network of Escherichia coli and offers a global view of the short- and long-term regulation of its metabolic pathways. The regulatory mechanisms responsible for key metabolic activities and the structure behind such mechanisms are detailed. Most metabolic functions are dependent on the activity of transcriptional regulators over gene expression - the so-called long-term regulation. However, enzymatic regulation - the so-called short-term regulation - often overlays transcriptional regulation and even, in particular metabolic pathways, enzymatic regulation may prevail. As such, understanding the balance between these two types of regulation is necessary to be able to predict and control cell responses, specifically cell responses to the various environmental stresses.

About this journal

Objective
Journal of Integrative Bioinformatics (JIB) is an international open access journal publishing original peer-reviewed research articles in all aspects of integrative bioinformatics.

Molecular biology produces huge amounts of data in the post-genomic era. This includes data describing metabolic mechanisms and pathways, structural genomic organization, patterns of regulatory regions; proteomics, transcriptomics, and metabolomics. On the one hand, analysis of this data uses essentially the methods and concepts of computer science; on the other hand, the range of biological tasks solved by researchers determines the range and scope of the data. Currently, there are about 1,000 database systems and various analytical tools available via the Internet which are directed at solving various biological tasks.

The challenge we have is to integrate these list-parts and relationships from genomics and proteomics at novel levels of understanding. Integrative Bioinformatics is a new area of research using the tools of computer science and electronic infrastructure applied to Biotechnology. These tools will also represent the backbone of the concept of a virtual cell.

Topics

Software applications/tools and databases covering the following topics:

Molecular Databases, Information Systems and Data Warehouses
Integration of Data, Methods and Tools
Metabolic and Regulatory Network Modeling and Simulation
Signal Pathways and Cell Control
Network Analysis
Medical Informatics, Biomedicine and Biotechnology
Integrative Approaches for Drug Design
Integrative Data and Text Mining Approaches
Integrative, whole cell and molecular modeling
Visualization and animation

Review papers are also welcome with regard to JIB.tools.

Article formats
Research articles, Review papers, Workshop contributions (if peer-reviewed)

Article processing charges (APCs)

Effective 1^st February, 2025, each unsolicited article, which is accepted for publication in the Journal of Integrative Bioinformatics is subject to an Article Processing Charge of 1,050€.
The Open Access publication of invited articles for Special Issues is sponsored by the editors.

Inquiries concerning APCs should be addressed to the Editorial Office at De Gruyter (see contact details below).