Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[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 nonliving 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 chemoinformatics 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)

Models for Antitubercular Activity of 5′-O-[(N-Acyl)sulfamoyl]adenosines

The relationship between topological indices and antitubercular activity of 5′-O-[(N-Acyl)sulfamoyl]adenosines has been investigated. A data set consisting of 31 analogues of 5′-O-[(N-Acyl)sulfamoyl]adenosines was selected for the present study. The values of numerous topostructural and topochemical indices for each of 31 differently substituted analogues of the data set were computed using an in-house computer program. Resulting data was analyzed and suitable models were developed through decision tree, random forest and moving average analysis (MAA). The goodness of the models was assessed by calculating overall accuracy of prediction, sensitivity, specificity and Mathews correlation coefficient. Pendentic eccentricity index – a novel highly discriminating, non-correlating pendenticity based topochemical descriptor – was also conceptualized and successfully utilized for the development of a model for antitubercular activity of 5′-O-[(N-Acyl)sulfamoyl]adenosines. The proposed index exhibited not only high sensitivity towards both the presence as well as relative position(s) of pendent/heteroatom(s) but also led to significant reduction in degeneracy. Random forest correctly classified the analogues into active and inactive with an accuracy of 67.74%. A decision tree was also employed for determining the importance of molecular descriptors. The decision tree learned the information from the input data with an accuracy of 100% and correctly predicted the cross-validated (10 fold) data with accuracy up to 77.4%. Statistical significance of proposed models was also investigated using intercorrelation analysis. Accuracy of prediction of proposed MAA models ranged from 90.4 to 91.6%

PRACTICAL APPLICATION OF QSAR TECHNIQUE FOR PREDICTION OF BIOLOGICAL ACTIVITY OF SELECTED HYDRAZONES

One important class of organic compounds is hydrazones which find huge application in many scientific areas. They demonstrate fascinating biological activities like as antioxidant, anti-inflammatory, anticonvulsants, antidepressant, anxiolytic, antihypertensive, anticancer, antimicrobial, anti-tuberculosis, and antifungal activity. This wide palette of the useful medical properties has attracted considerable scientific interest for their synthesis. The biological activity and the changes in this activity depend on the substituents present in the hydrazone molecule. The hydrazones can be used in agriculture as herbicides, insecticides, and plant growth stimulants because of their physiological properties. A series of substituted aromatic hydrazones have been synthesized and evaluated for in vitro antimicrobial activity against: Bacillus subtilis. QSAR study was performed to estimate the quantitative effects of the selected descriptors of derivatives on their antibacterial activity. Topological and physicochemical descriptors were calculated for each molecule and a several two-parametric mathematical models have been selected for further discussion. The statistical significance of each model was measured by a few cross-validation parameters (Q, PRESS/SSY; SPRESS; PSE andQ2). Statistical evaluation of the data used to test the quality of the obtained models, indicated that in statistically significant model both parameters AC (Atom count) and MAPA (Maximal projection area) have opposite input to the modeling of biological activity of the selected hydrazones. Following statistical parameters were obtained for this model: R2 = 0.9444; Sd = 0.0097; F-test = 101.9875; R2adj = 0.9352; Q = 100.1858; PRESS/SSY = 0.0564; SPRESS = 0.0098; PSE = 0.0088 and Q2 = 0.9436

A novel representation of RNA secondary structure based on element-contact graphs

<p>Abstract</p> <p>Background</p> <p>Depending on their specific structures, noncoding RNAs (ncRNAs) play important roles in many biological processes. Interest in developing new topological indices based on RNA graphs has been revived in recent years, as such indices can be used to compare, identify and classify RNAs. Although the topological indices presented before characterize the main topological features of RNA secondary structures, information on RNA structural details is ignored to some degree. Therefore, it is necessity to identify topological features with low degeneracy based on complete and fine-grained RNA graphical representations.</p> <p>Results</p> <p>In this study, we present a complete and fine scheme for RNA graph representation as a new basis for constructing RNA topological indices. We propose a combination of three vertex-weighted element-contact graphs (ECGs) to describe the RNA element details and their adjacent patterns in RNA secondary structure. Both the stem and loop topologies are encoded completely in the ECGs. The relationship among the three typical topological index families defined by their ECGs and RNA secondary structures was investigated from a dataset of 6,305 ncRNAs. The applicability of topological indices is illustrated by three application case studies. Based on the applied small dataset, we find that the topological indices can distinguish true pre-miRNAs from pseudo pre-miRNAs with about 96% accuracy, and can cluster known types of ncRNAs with about 98% accuracy, respectively.</p> <p>Conclusion</p> <p>The results indicate that the topological indices can characterize the details of RNA structures and may have a potential role in identifying and classifying ncRNAs. Moreover, these indices may lead to a new approach for discovering novel ncRNAs. However, further research is needed to fully resolve the challenging problem of predicting and classifying noncoding RNAs.</p

Herramientas informáticas y de inteligencia artificial para el meta-análisis en la frontera entre la bioinformática y las ciencias jurídicas

[Resumen] Los modelos computacionales, conocidos por su acrónimo en idioma Inglés como QSPR (Quantitative Structure-Property Relationships) pueden usarse para predecir propiedades de sistemas complejos. Estas predicciones representan una aplicación importante de las Tecnologías de la Información y la Comunicación (TICs). La mayor relevancia es debido a la reducción de costes de medición experimental en términos de tiempo, recursos humanos, recursos materiales, y/o el uso de animales de laboratorio en ciencias biomoleculares, técnicas, sociales y/o jurídicas. Las Redes Neuronales Artificiales (ANNs) son una de las herramientas informáticas más poderosas para buscar modelos QSPR. Para ello, las ANNs pueden usar como variables de entrada (input) parámetros numéricos que cuantifiquen información sobre la estructura del sistema. Los parámetros conocidos como Índices Topológicos (TIs) se encuentran entre los más versátiles. Los TIs se calculan en Teoría de Grafos a partir de la representación de cualquier sistema como una red de nodos interconectados; desde moléculas a redes biológicas, tecnológicas, y sociales. Esta tesis tiene como primer objetivo realizar una revisión y/o introducir nuevos TIs y software de cálculo de TIs útiles como inputs de ANNs para el desarrollo de modelos QSPR de redes bio-moleculares, biológicas, tecnológico-económicas y socio-jurídicas. En ellas, por una parte, los nodos representan biomoléculas, organismos, poblaciones, leyes tributarias o concausas de delitos. Por otra parte, en la interacción TICs-Ciencias Biomoleculares- Derecho se hace necesario un marco de seguridad jurídica que permita el adecuado desarrollo de las TICs y sus aplicaciones en Ciencias Biomoleculares. Por eso, el segundo objetivo de esta tesis es revisar el marco jurídico-legal de protección de los modelos QSAR/QSPR de sistemas moleculares. El presente trabajo de investigación pretende demostrar la utilidad de estos modelos para predecir características y propiedades de estos sistemas complejos.[Resumo] Os modelos de ordenador coñecidos pola súas iniciais en inglés QSPR (Quantitative Structure-Property Relationships) poden prever as propiedades de sistemas complexos e reducir os custos experimentais en termos de tempo, recursos humanos, materiais e/ou o uso de animais de laboratorio nas ciencias biomoleculares, técnicas, e sociais. As Redes Neurais Artificiais (ANNs) son unha das ferramentas máis poderosas para buscar modelos QSPR. Para iso, as ANNs poden facer uso, coma variables de entrada (input), dos parámetros numéricos da estrutura do sistema chamados Índices Topolóxicos (TIs). Os TI calcúlanse na teoría dos grafos a partir da representación do sistema coma unha rede de nós conectados, incluíndo tanto moléculas coma redes sociais e tecnolóxicas. Esta tese ten como obxectivo principal revisar e/ou desenvolver novos TIs, programas de cálculo de TIs, e/ou modelos QSPR facendo uso de ANNs para predicir redes bio-moleculares, biolóxicas, económicas, e sociais ou xurídicas onde os nós representan moléculas biolóxicas, organismos, poboacións, ou as leis fiscais ou as concausas dun delito. Ademais, a interacción das TIC con as ciencias biolóxicas e xurídicas necesita dun marco de seguridade xurídica que permita o bo desenvolvemento das TIC e as súas aplicacións en Ciencias Biomoleculares. Polo tanto, o segundo obxectivo desta tese é analizar o marco xurídico e legal de protección dos modelos QSPR. O presente traballo de investigación pretende demostrar a utilidade destes modelos para predicir características e propiedades destes sistemas complexos.[Abstract] QSPR (Quantitative Structure-Property Relationships) computer models can predict properties of complex systems reducing experimental costs in terms of time, human resources, material resources, and/or the use of laboratory animals in bio-molecular, technical, and/or social sciences. Artificial Neural Networks (ANNs) are one of the most powerful tools to search QSPR models. For this, the ANNs may use as input variables numerical parameters of the system structure called Topological Indices (TIs). The TIs are calculated in Graph Theory from a representation of any system as a network of interconnected nodes, including molecules or social and technological networks. The first aim of this thesis is to review and/or develop new TIs, TIs calculation software, and QSPR models using ANNs to predict bio-molecular, biological, commercial, social, and legal networks where nodes represent bio-molecules, organisms, populations, products, tax laws, or criminal causes. Moreover, the interaction of ICTs with Biomolecular and law Sciences needs a legal security framework that allows the proper development of ICTs and their applications in Biomolecular Sciences. Therefore, the second objective of this thesis is to review the legal framework and legal protection of QSPR techniques. The present work of investigation tries to demonstrate the usefulness of these models to predict characteristics and properties of these complex systems

Nenad Trinajstić – Pioneer of Chemical Graph Theory

We present a brief overview of many contributions of Nenad Trinajstić to Chemical Graph Theory, an important and fast developing branch of Theoretical Chemistry. In addition, we outline briefly the various activities of Trinajstić within the chemical community of Croatia. As can be seen, his scientific work has been very productive and has not abated despite the hostilities towards the Chemical Graph Theory in certain chemical circles over the past 30 years. On the contrary, Trinajstić continued, widened the areas of his research interest, which started with investigating the close relationship between Graph Theory and HMO, and demonstrated the importance of Chemical Graph theory for chemistry. In more than one way he has proven the opponents of Chemical Graph Theory wrong, though some continue to fail to recognize the importance of Graph Theory in Chemistry

Analisis centrality dan modularity pada graf untuk identifikasi relasi penelitian indeks topologi

INDONESIA: Salah satu cabang matematika yang penting dan jamak dimanfaatkan untuk memecahkan ragam masalah yang terjadi dalam kehidupan adalah teori graf. Visualisasi dengan graf membantu pemecah masalah untuk lebih jelas dan mudah dalam menganalisis masalah yang telah dimodelkan, misalkan pemanfaatan teori graf untuk merepresentasikan keterhubungan antar kata kunci dalam penelitian. Pada penelitian ini menggunakan kata kunci “topological index” dalam pencarian data artikel yang terindeks oleh Scopus serta dilakukan analisis centrality meliputi degree centrality; eigenvector centrality; betweenness centrality; dan closeness centrality dan modularity. Tujuan penelitian ini adalah mengidentifikasi seberapa pengaruh suatu titik yaitu kata kunci terhadap jaringan yang terbentuk dan seberapa kuat jaringan tersebut menggunakan analisis centrality dan modularity. Hasil penelitian menunjukkan bahwa didapatkan pola jaringan dari 2000 data bibliometrik artikel penelitian yang telah dikumpulkan terdapat 225 titik dan 5.109 sisi. Selanjutnya, kata kunci yang memiliki hubungan yang kuat dengan kata kunci lain dan paling berpengaruh sebagai sentral yang signifikan yaitu “wiener index”. Kemudian kata “molecular graphs” merupakan kata kunci sebagai penghubung utama titik dalam jaringan dan efisien dalam penyebaran ide pengetahuan. ENGLISH: One of the important and commonly used branches of mathematics to solve various problems that occur in life is graph theory. Visualization with graphs helps problem solvers to more clearly and easily analyze problems that have been modeled, for example the use of graph theory to represent the relationship between keywords in research. In this study, the keyword “topological index” is used in searching for Scopus indexed articles and centrality analysis is carried out including degree centrality; eigenvector centrality; betweenness centrality; and closeness centrality and modularity. The purpose of this research is to identify how influential a point, namely keywords, is on the network formed and how strong the network is using centrality and modularity analysis. The results showed that the network pattern obtained from 2000 bibliometric data of research articles that have been collected there are 225 vertices and 5.109 edges. Furthermore, the keyword that has a strong relationship with other keywords and the most influential as a significant center is “wiener index”. Then the word “molecular graphs” is a keyword as the main connecting point in the network and efficient in spreading knowledge ideas. ARABIC: تعتبر نظرية المخطاط أحد فروع الرياضيات المهمة والمستخدمة بشكل شائع لحل المشكلات المختلفة التي تحدث في الحياة. يساعد التصور مع المخطاط من يحلون المشكلات على أن يكونوا أكثر وضوحًا وأسهل في تحليل المشكلات التي تم تصميمها، على سبيل المثال استخدام نظرية المخطاط الرسم لتمثيل العلاقة بين الكلمات الرئيسية في البحث. في هذه الدراسة، تم استخدام الكلمة الأساسية "الفهرس الطوبولوجي" للبحث عن بيانات المقالة المفهرسة على Scopus وتم إجراء تحليل المركزية بما في ذلك مركزية الدرجة ؛ مركزية المتجهات الذاتية بين مركزية وقرب مركزية ونمطية. الغرض من هذه الدراسة هو تحديد مدى تأثير نقطة ما ، أي كلمة رئيسية لها على الشبكة التي تم تشكيلها ومدى قوة الشبكة التي تستخدم تحليل المركزية والنمطية. أظهرت النتائج أن نمط الشبكة الذي تم الحصول عليه من البيانات الببليومترية لعام 2000 للمقالات البحثية التي تم جمعها يحتوي على 225 رؤوس و 5109 أضلاع. علاوة على ذلك ، فإن الكلمات الرئيسية التي لها علاقة قوية بالكلمات الرئيسية الأخرى ولها التأثير الأكبر كمركزية مهمة هيwiener indeks. ثم كلمة “molecular graphs” هي كلمة رئيسية كنقطة الاتصال الرئيسية في الشبكة وفعالة في نشر الأفكار المعرفية

Doctor of Philosophy

dissertationNanoinformatics is a relatively young field of study that is important due to its implications in the field of nanomedicine, specifically toward the development of nanoparticle drug delivery systems. As more structural, biochemical, and physiochemical data become available regarding nanoparticles, the greater the knowledge-gain from using nanoinformatics methods will become. While there are challenges that exist with nanoparticle data, including heterogeneity of data and complexity of the particles, nanoinformatics will be at the forefront of processing these data and aid in the design of nanoparticles for biomedical applications. In this dissertation, a review of data mining and machine learning studies performed in the field of nanomedicine is presented. Next, the use of natural language processing methods to extract numeric values of biomedical property terms of poly(amido amine) (PAMAM) dendrimers from nanomedicine literature is demonstrated, along with successful extraction results. Following this is an implementation and its results of data mining techniques used for the development of predictive models of cytotoxicity of PAMAM dendrimers using their chemical and structural properties. Finally, a method and its results for using molecular dynamics simulations to test the ability of EDTA, as a gold standard, and generation 3.5 (G3.5) PAMAM dendrimers to chelate calcium

BIOINFORMATICS TOOL AND MODEL DEVELOPMENT FOR STUDYING BIOLOGICAL NETWORKS AND PROTEIN-PROTEIN INTERACTIONS

Ph.DDOCTOR OF PHILOSOPH