Aliuska Duardo-Sanchez

University of A Coruña, La Corogne, Galicia, Spain

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Publications (28)48.7 Total impact

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    Aliuska Duardo-Sánchez · Humberto González-Díaz · Alejandro Pazos
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    ABSTRACT: Complex systems and networks appear in almost all areas of reality. We find then from proteins residue networks to Protein Interaction Networks (PINs). Chemical reactions form Metabolic Reactions Networks (MRNs) in living beings or Atmospheric reaction networks in planets and moons. Network of neurons appear in the worm C. elegans, in Human brain connectome, or in Artificial Neural Networks (ANNs). Infection spreading networks exist for contagious outbreaks networks in humans and in malware epidemiology for infection with viral software in internet or wireless networks. Social-legal networks with different rules evolved from swarm intelligence, to hunter-gathered societies, or citation networks of U.S. Supreme Court. In all these cases, we can see the same question. Can we predict the links based on structural information? We propose to solve the problem using Quantitative Structure-Property Relationship (QSPR) techniques commonly used in chemo-informatics. In so doing, we need software able to transform all types of networks/graphs like drug structure, drug-target interactions, protein structure, protein interactions, metabolic reactions, brain connectome, or social networks into numerical parameters. Consequently, we need to process in alignment-free mode multitarget, multiscale, and multiplexing, information. Later, we have to seek the QSPR model with Machine Learning techniques. MI-NODES is this type of software. Here we review the evolution of the software from chemoinformatics to bioinformatics and systems biology. This is an effort to develop a universal tool to study structure-property relationships in complex systems.
    Full-text · Article · Nov 2015 · Current Bioinformatics
  • Aliuska Duardo-Sanchez · Humberto Gonzalez-Diaz · Alejandro Pazos

    No preview · Article · Nov 2015
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    ABSTRACT: This work is aimed at describing the workflow for a methodology that combines Chemoinformatics and Pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs. ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.
    No preview · Article · Feb 2014 · Journal of Chemical Information and Modeling
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    ABSTRACT: The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic non-living systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs) can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors), can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in Chemo-informatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers / W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity / Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation).
    No preview · Article · Dec 2013 · Journal of Chemical Information and Modeling
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    ABSTRACT: The Randić index is a well known topological index (TI) used in QSAR/QSPR studies to quantify the molecular structure represented by a graph. In this work we review some aspects of this TI with special emphasis on the generalizations introduced by Kier & Hall and more recently by Estrada. Next, we introduced a new generalization using a Markov chain in order to obtain a new family of TIs called the Markov-Randić indices of order k-th (1χk). Later, we applied these new indices to seek models useful to calculate numerical quality scores S(Lij) for network links Lij (connectivity) in known complex networks. The linear models obtained produced the following results in terms of overall accuracy for network re-construction: Metabolic networks (70.48%), Parasite-Host networks (90.86%), CoCoMac brain cortex co-activation network (81.59%), NW Spain Fasciolosis spreading network (99.04%). Spanish financial law network (71.83%). This work opens a new door to the computational re-evaluation of network connectivity quality (collation) in different complex systems.
    No preview · Article · Aug 2013 · Current Bioinformatics
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    ABSTRACT: In recent times, there has been an increased use of Computer-Aided Drug Discovery (CADD) techniques in Medicinal Chemistry as auxiliary tools in drug discovery. Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the creation of new computational tools. This process is often accompanied by a lack of understanding of the legal aspects related to software and model use, that is, the copyright protection of new medicinal chemistry software and software-mediated discovered products. In the center of picture, which lies in the frontiers of legal, chemistry, and biosciences, we found computational modeling-based drug discovery patents. This article aims to review prominent cases of patents of bio-active organic compounds that involved/protect also computational techniques. We put special emphasis on patents based on Quantitative Structure-Activity Relationships (QSAR) models but we include other techniques too. An overview of relevant international issues on drug patenting is also presented.
    No preview · Article · Jan 2013 · Frontiers in bioscience (Elite edition)
  • Aliuska Duardo-Sanchez · Humberto Gonzalez-Diaz
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    ABSTRACT: Chem-Bioinformatic models connect the chemical structure of drugs and/or targets (protein, gen, RNA, microorganism, tissue, disease...) with drug biological activity over this target. On the other hand, a systematic judicial framework is needed to provide appropriate and relevant guidance for addressing various computing techniques as applied to scientific research in biosciences frontiers. This article reviews both: the use of the predictions made with models for regulatory purposes and how to protect (in legal terms) the models of molecular systems per se, and the software used to seek them. First we review: i) models as a tool for regulatory purposes, ii) Organizations Involved with Validation of models, iii) Regulatory Guidelines and Documents for models, iv) Models for Human Health and Environmental Endpoint, and v) Difficulties to Validation of models, and other issues. Next, we focused on the legal protection of models and software; including: a short summary of topics, and methods for legal protection of computer software. We close the review with a section that treats the taxes in software use.
    No preview · Article · Jan 2013 · Frontiers in bioscience (Elite edition)
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    ABSTRACT: Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. In second instance, we use for the first time a Markov chain model to generalize Spectral moments to higher order analogues coined here as the Stochastic Spectral Moments TIs of order k (πk). Lastly, we report for the first time different QSAR/QSPR models for different classes of networks found in drug research, nature, technology, and social-legal sciences using πk values. This work updates our previous reviews Gonzalez-Diaz et al. Curr Top Med Chem. 2007; 7(10): 1015-29 and Gonzalez-Diaz et al. Curr Top Med Chem. 2008; 8(18):1676-90. It has been prepared in response to the kind invitation of the editor Prof. AB Reitz in commemoration of the 10th anniversary of this journal in 2010.
    Full-text · Article · Apr 2012 · Current Topics in Medicinal Chemistry
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    ABSTRACT: In recent times, there has been an increased use of Computer-Aided Drug Discovery (CADD) techniques in Medicinal Chemistry as auxiliary tools in drug discovery. Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the creation of new computational tools. The adoption of computational tools by medicinal chemists is sadly, and all too often accompanied, by a lack of understanding of the legal aspects related to software and model use, that is, the copyright protection of new medicinal chemistry software and software-mediated discovered products. In the center of picture, which lies in the frontiers of legal, chemistry, and biosciences, we found computational modeling-based drug discovery patents. We refer here to patents that claims new drug candidate compounds and recognized/claim as well computational techniques used in the discovery process. This article aims to review prominent cases of patents of bio-active organic compounds that involved/protect also computational techniques. We put special emphasis on patents based on Quantitative Structure-Activity Relationships (QSAR) models but we include other techniques too. An overview of relevant international issues on drug patenting is also presented. TABLE OF CONTENTS 1. Abstract 2. Introduction 3. CADD Bio-Active compounds patents 3.1. Compounds & Computational method 3.1.1. Aziridinyl quinone antitumor agents 3.1.2 Chalcones for neoplastic disorders 3.1.3. Steroid acting over DNA replication. 3.1.4. Drugs suppressing appetite 3.1.5. Anti-microbial peptidomimetic compounds 3.1.6. Biologically active chalcones 3.1.7. Predictive method for polymers. 3.2. Computational method alone 3.2.1. One-dimensional QSAR models. 3.2.2. Comparative molecular field analysis 3.2.3. Molecular hologram QSAR. 3.2.4. Predictive method for polymers 3.2.5. Molecule Fragmentation Scheme 3.2.6. Method for predicting organic pollutants 3.2.7. Searching compound databases 3.2.8. A 3D-QSAR method 3.2.9. QSAR & pharmacophore fingerprinting 3.2.10. Fast computer data segmenting techniques 4. Notes on Legal issues related to CADD 4.1. Some general remarks 4.2. Short notes on Copyright protection 4.3. A short note on patent protection 5. Conclusions 6. Acknowledgements 7. References
    No preview · Article · Mar 2012 · Frontiers in Bioscience
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    ABSTRACT: Several graph representations have been introduced for different data in theoretical biology. For instance, Complex Networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning (sequence pseudo-folding rules) in 2D space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. In fact, we can use this technique to create string pseudo-folding lattice representations for any kind of string data. However, despite the proved efficacy of new Lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. In this work, we review both classic and generalized types of lattice graphs as well as examples that illustrate how to use it in order to represent and compare biological data from different sources. The examples reviewed include the following cases: Protein sequence; Mass Spectra (MS) of protein Peptide Mass Fingerprints (PMF); Molecular Dynamic Trajectory (MDTs) from structural studies; mRNA Microarray data; Single Nucleotide Polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein Polymorphisms and Protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of graph theory in theoretical biology and other areas of biomedical sciences. In addition, we carried out the statistical analysis of 50,000+ cases to seek and validate a new QSAR-like predictor for enzyme sub-classes. The model use as inputs spectral moments of pseudo-folding lattice graphs. Last we illustrated the use of this model to study 4,000+ ESTs of protein sequences present on the parasite Trichinella spiralis.
    No preview · Article · Mar 2012 · Current Bioinformatics
  • Duardo-Sanchez A · González-Díaz H
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    ABSTRACT: A very useful class of models describing structure-property relationships of systems may play an important role to reduce costs in terms of time, human resources, material resources, as well as allow certain laboratory animals replacement in biomedical sciences. Many of these models are in essence Quantitative Structure-Activity or Property Relationships (QSARs/QSPRs). In other words, QSPRs are models that connect the structure of a system with external properties of these systems that are not self-evident after direct inspection of the structure. In particular, QSARs are models that connect the chemical structure of drugs, target (protein, gen, RNA, microorganism, tissue, disease…), or both (drug and target at the same time) with drug biological activity over this target. On the other hand, a systematic judicial framework is needed to provide appropriate and relevant guidance for addressing various computing techniques as applied to scientific research in biosciences frontiers. Bioinformatics and computational biology are two areas within the field of biosciences that require more attention from the legal operators. Taking all the previous aspects into consideration, this article reviews both: the use of the predictions made with models for regulatory purposes in legal and how to legally protect models of molecular sytems systems per se, and the software used to seek them. In this sense, the issues reviewed here are the following. In the first part we review: i) QSAR models as a tool for regulatory purposes, ii) Organizations Involved with Validation of QSARs, iii) Regulatory Guidelines and Documents for QSARs Developing and Application, iv) QSARs for Human Health and Environmental Endpoint, and v) Difficulties to Validation of QSARs. We also discuss in this part: vi) OECD’s Database on Chemical Risk Assessment Models, vii) QSARs Based on Metabolism and In Vitro Data, and viii) Perspectives in QSAR Modeling for Regulatory Use. In the second part we focused on the legal protection of QSAR-like models and software; including: a short summary of topics, and methods for legal protection of computer software like: copyright protection, patent protection, trade secret protection, and trademark protection. We close the review with a section that treat the taxes in software use. TABLE OF CONTENTS 1. Introduction 2. Discussion 2.1. QSAR models as a tool for regulatory purposes 2.1.1 Organizations Involved with Validation of QSARs 2.1.2 Regulatory Guidelines and Documents for QSARs Developing and Application 2.1.3 QSARs for Human Health and Environmental Endpoint 2.1.4 Difficulties to Validation of QSARs 2.1.5 OECD’s Database on Chemical Risk Assessment Models 2.1.6 QSARs Based on Metabolism and In Vitro Data 2.1.7 Perspectives in QSAR Modeling for Regulatory Use 2.2. Legal protection of QSAR-like models and software 2.2.1. Short summary of topics 2.2.2. Methods for legal protection of computer software 2.2.2.1. Copyright protection 2.2.2.2. Patent protection 2.2.2.3. Trade Secret Protection 2.2.2.4. Trademark Protection 2.2.2.5. Copyright and contractual issues. 2.3. Taxes in software use 3. Conclusions
    No preview · Article · Mar 2012 · Frontiers in Bioscience
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    ABSTRACT: Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. Consequently, the goal of this work, in first instance, is offering a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. In second instance, we use for the first time a Markov chain model to generalize Spectral moments to higher order analogues coined here as the Stochastic Spectral Moments TIs of order k (pik). Last, we report by the first time different QSAR/QSPR models for different classes of networks found in drug research, nature, technology, and social-legal sciences using pik values. This work updates our previous reviews Gonzalez-Diaz et al. Curr Top Med Chem. 2007; 7(10): 1015-29 and Gonzalez-Diaz et al. Curr Top Med Chem. 2008; 8(18):1676-90. It has been prepared after kindly invitation of editor Prof. AB Reitz in commemoration of the 10th anniversary of this journal in 2010.
    No preview · Article · Feb 2012 · Current topics in medicinal chemistry

  • No preview · Article · Feb 2012 · Current Topics in Medicinal Chemistry
  • [Show abstract] [Hide abstract]
    ABSTRACT: Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. Consequently, the goal of this work, in first instance, is offering a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. In second instance, we use for the first time a Markov chain model to generalize Spectral moments to higher order analogues coined here as the Stochastic Spectral Moments TIs of order k (πk). Last, we report by the first time different QSAR/QSPR models for different classes of networks found in drug research, nature, technology, and social-legal sciences using πk values. This work updates our previous reviews González-Díaz et al. Curr Top Med Chem. 2007; 7(10): 1015-29 and González-Díaz et al. Curr Top Med Chem. 2008; 8(18):1676-90. It has been prepared after kindly invitation of editor Prof. AB Reitz in commemoration of the 10th anniversary of this journal in 2010.
    No preview · Article · Feb 2012 · Current Topics in Medicinal Chemistry

  • No preview · Article · Feb 2012 · Current topics in experimental endocrinology
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    ABSTRACT: Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction: Metabolic networks (72.3%), Parasite-Host networks (93.3%), CoCoMac brain cortex co-activation network (89.6%), NW Spain fasciolosis spreading network (97.2%), Spanish financial law network (89.9%) and World trade network for Intelligent & Active Food Packaging (92.8%). In order to seek these models, we studied an average of 55,388 pairs of nodes in each model and a total of 332,326 pairs of nodes in all models. Finally, this method was used to solve a more complicated problem. A model was developed to score the connectivity quality in the Drug-Target network of US FDA approved drugs. In this last model the θ(k) values were calculated for three types of molecular networks representing different levels of organization: drug molecular graphs (atom-atom bonds), protein residue networks (amino acid interactions), and drug-target network (compound-protein binding). The overall accuracy of this model was 76.3%. This work opens a new door to the computational reevaluation of network connectivity quality (collation) for complex systems in molecular, biomedical, technological, and legal-social sciences as well as in world trade and industry.
    No preview · Article · Jan 2012 · Journal of Theoretical Biology
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    ABSTRACT: Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures to large systems. We can cite for instance, drug-target protein interaction networks, drug policy legislation networks, or drug treatment in large geographical disease spreading networks. In any case, all these networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and edges (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent. The main reason for this success of TIs is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. Another important reason for the success of TIs is that using these parameters as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for different kind of problems in Computer-Aided Drug Design (CADD). Taking into account all the above-mentioned aspects, the present work is aimed at offering a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most common types of complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. Next, we use for the first time a Markov chain model to generalize Galvez TIs to higher order analogues coined here as the Markov-Galvez TIs of order k (MGk). Lastly, we illustrate the calculation of MGk values for different classes of networks found in drug research, nature, technology, and social-legal sciences.
    Full-text · Article · Oct 2011 · Current Computer - Aided Drug Design
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    ABSTRACT: In this chapter, we propose the study of multiple systems using node centrality or connectedness information measures derived from a Graph or Complex Network. The information is quantified in terms of the Entropy centrality k C θ(j) of the jth parts or states (nodes) of a Markov Chain associated with the system, represented by a network graph. The procedure is standard for all systems despite the complexity of the system. First, we define the phenomena to study, ranging from molecular systems composed by single molecules (drug activity, drug toxicity), multiple molecules (networks of chemical reactions), and macromolecules (DNA–drug interaction, protein function), to ecological systems (bacterial co-aggregation), or social systems (criminal causation, legislative productivity). Second, we collect several cases from literature (drugs, chemical reactions, proteins, bacterial species, or criminal cases). Next, we classify the cases in at least two different groups (active/nonactive drugs, enantioselective/non-enantioselective reactions, functional/nonfunctional proteins, co-aggregating/non-co-aggregating bacteria, or crime/noncrime cause, efficient/nonefficient law). After that, we represent the interconnectivity of the discrete parts of the system (atoms, amino acids, reactants, bacteria species, or people) as a graph or network. The Markov Chain theory is used to calculate the entropy of the system for nodes placed at different distances. Finally, we aim to both derive and validate a classification model using the entropy values as input variables and the classification of cases as the output variables. The model is used to predict the probability with which a case presents the studied property. The present work proposes the entropy of a Markov Chain associated with a network or graph to be used as a universal quantity in pattern recognition regardless the chemical, biological, social, or other nature of the systems under study.
    No preview · Chapter · Aug 2011
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    ABSTRACT: Graph and Complex Network theories are applied to different levels of matter organization such as genome networks, protein-protein networks, sexual disease transmission networks, linguistic networks, law and social networks, power electric networks or Internet. A very important fact is that we can use the numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent. The main reason for this success of TIs is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. Another important reason for the success of TIs is that using these parameters as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems, at least in principle. In any case, there is a lack of manuscripts or issues focused on QSAR-like models with TIs and of networks more focused on Bioinformatics. In this sense, the present issue provides state-of-the-art reviews of some of these new computational approaches in this rapidly expanding area of Bioinformatics. Taking into account all the above-mentioned aspects, the present work intends to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of classic TIs and Databases of Biology, Parasitology, Technology, and Social-Legal Networks. After that, we report a definition of a new class of TIs, coined here as Markov-Harary invariants. We also present the calculation of this class of TIs for different classes of networks. Next, we carry out a comparative study of these networks using the values of the new Markov-Harary TIs. Finally, we compare these new indices with another new class of TIs called Markov entropy values, which has been previously developed.
    No preview · Article · Mar 2011 · Current Bioinformatics
  • Aliuska Duardo-Sanchez · Grace Patlewicz · Humberto Gonzalez-Diaz
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    ABSTRACT: Bionformatics models describe that structure-property relationships of systems may play an important role to reduce costs in terms of time, human resources, material resources, as well as allow certain laboratory animals replacement in biomedical sciences. Many of these models are in essence Quantitative Structure-Activity or Property Relationships (QSARs/QSPRs). In other words, QSPRs are models that connect the structure of a system with external properties of these systems that are not self-evident after direct inspection of the structure. In particular, QSARs are models that connect the chemical structure of drugs, target (protein, gen, RNA, microorganism, tissue, disease…), or both (drug and target at the same time) with drug biological activity over this target. Many of these QSAR techniques are based on the use of structural parameters, which are numerical series that codify useful structural information and enable correlations between strcture and biological properties. In parallel, graph theory and Complex Network analysis tools are expanding to new potential fields of application of Information Sciences at different levels from molecules to populations, social or technological networks such as genome, protein-protein networks, sexual disease transmission networks, power electric power network or internet. In all these cases, we can calculate parameters called Topological Indices (TIs) that numerically described the connectedness patterns (structure) between the nodes or actors in a network. Consequently, TIs are very useful as inputs for QSPR models at all structural levels. In fact, even legal systems may be approached using computing and information techniques like networks. So we can construct a complex network of legal systems connecting laws (nodes) that regulate common biological topics for instance. On the other hand, a systematic judicial framework is needed to provide appropriate and relevant guidance for addressing various computing techniques as applied to scientific research. Bioinformatics and computational biology are two areas within the field of biosciences that require more attention from the legal operators. Taking all the previous aspects into consideration, this article reviews both: the use of legal sciences to regulate and protect QSAR models of molecular sytems and the application of QSPR-like models to study legal systems per se. Consequently, as stated in a title, in this review we are going to travel from Cheminformatics, Bioinformatics, and Networks to Legal Sciences and later go back. In the first direction, we review the various legal procedures that are available to protect QSAR software, the acceptance and legal treatment of scientific results and techniques derived from such software, as well as some of the specific tax issues from the computer programs field. In the second direction, we review the representation of legal systems using complex networks, the description of legal social networks with TIs, the development of QSPR models to predict legal and social phenomena. In this sense, the issues reviewed here are: 1- Networks, Topological Indices and QSPR models: Theoretical Background. 2- Notes on reviews of legal issues related to QSAR models. 3- Complex Network Representations in Legal Sciences. 4- Topological Indices of Legal networks. 5. QSPR models to predict causality in crime law networks. DOI: 10.2174/157489311795222347
    No preview · Article · Mar 2011 · Current Bioinformatics