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Rajan S, Schwarz E. Network-based artificial intelligence approaches for advancing personalized psychiatry. Am J Med Genet B Neuropsychiatr Genet 2024:e32997. [PMID: 39031613 DOI: 10.1002/ajmg.b.32997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/24/2024] [Accepted: 06/06/2024] [Indexed: 07/22/2024]
Abstract
Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.
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Affiliation(s)
- Sivanesan Rajan
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Emanuel Schwarz
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Mannheim, Germany
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2
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Abubakar MS, Aremu KO, Aphane M, Amusa LB. A QSPR analysis of physical properties of antituberculosis drugs using neighbourhood degree-based topological indices and support vector regression. Heliyon 2024; 10:e28260. [PMID: 38571658 PMCID: PMC10987931 DOI: 10.1016/j.heliyon.2024.e28260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 03/05/2024] [Accepted: 03/14/2024] [Indexed: 04/05/2024] Open
Abstract
Topological indices are molecular descriptors used in QSPR modelling to predict the physicochemical properties of molecules. Topological indices are used in numerous applications in drug design. In this work, we compute the neighbourhood degree-based topological indices of 15 antituberculosis drugs, we studied the QSPR analysis of these drugs using support vector regression. The efficiency of support vector regression is determined by comparing it with the classical linear regression. Our QSPR model further shows the superiority of the SVR model as a better predictive model in QSPR analysis of the physical properties of antituberculosis drugs. The findings in this study are a further contribution to the field of chemical graph theory and drug design, providing a deeper understanding of neighbourhood degree-based topological indices and their predictive capabilities in QSPR model.
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Affiliation(s)
- Muhammad Shafii Abubakar
- Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, P.O. Box 60, 0204, Pretoria, South Africa
| | - Kazeem Olalekan Aremu
- Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, P.O. Box 60, 0204, Pretoria, South Africa
- Department of Mathematics, Usmanu Danfodiyo University Sokoto, P.M.B. 2346, Sokoto State, Nigeria
| | - Maggie Aphane
- Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, P.O. Box 60, 0204, Pretoria, South Africa
| | - Lateef Babatunde Amusa
- Department of Statistics, University of Ilorin, P.M.B. 1515, Ilorin, Kwara State, Nigeria
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3
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Predicting prediction: A systematic workflow to analyze factors affecting the classification performance in genomic biomarker discovery. PLoS One 2022; 17:e0276607. [DOI: 10.1371/journal.pone.0276607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022] Open
Abstract
High throughput technologies in genomics enable the analysis of small alterations in gene expression levels. Patterns of such deviations are an important starting point for the discovery and verification of new biomarker candidates. Identifying such patterns is a challenging task that requires sophisticated machine learning approaches. Currently, there are a variety of classification models, and a common approach is to compare the performance and select the best one for a given classification problem. Since the association between the features of a data set and the performance of a particular classification method is still not fully understood, the main contribution of this work is to provide a new methodology for predicting the prediction results of different classifiers in the field of biomarker discovery. We propose here a three-steps computational workflow that includes an analysis of the data set characteristics, the calculation of the classification accuracy and, finally, the prediction of the resulting classification error. The experiments were carried out on synthetic and microarray datasets. Using this method, we showed that the predictability strongly depends on the discriminatory ability of the features, e.g., sets of genes, in two or multi-class datasets. If a dataset has a certain discriminatory ability, this method enables prediction of the classification performance before applying a learning model. Thus, our results contribute to a better understanding of the relationship between dataset characteristics and the corresponding performance of a machine learning method, and suggest the optimal classification method for a given dataset based on its discriminatory ability.
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4
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Sedykh AY, Shah RR, Kleinstreuer NC, Auerbach SS, Gombar VK. Saagar-A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions. Chem Res Toxicol 2020; 34:634-640. [PMID: 33356152 DOI: 10.1021/acs.chemrestox.0c00464] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of being black boxes. Interpretable models require interpretable descriptors to provide chemistry-backed predictive reasoning and facilitate intelligent molecular design. We developed a novel set of extensible chemistry-aware substructures, Saagar, to support interpretable predictive models and read-across protocols. Performance of Saagar in chemical characterization and search for structurally similar actives for read-across applications was compared with four publicly available fingerprint sets (MACCS (166), PubChem (881), ECFP4 (1024), ToxPrint (729)) in three benchmark sets (MUV, ULS, and Tox21) spanning ∼145 000 compounds and 78 molecular targets at 1%, 2%, 5%, and 10% false discovery rates. In 18 of the 20 comparisons, interpretable Saagar features performed better than the publicly available, but less interpretable and fixed-bit length, fingerprints. Examples are provided to show the enhanced capability of Saagar in extracting compounds with higher scaffold similarity. Saagar features are interpretable and efficiently characterize diverse chemical collections, thus making them a better choice for building interpretable predictive in silico models and read-across protocols.
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Affiliation(s)
| | - Ruchir R Shah
- Sciome LLC, Research Triangle Park, North Carolina 27709, United States
| | - Nicole C Kleinstreuer
- National Institute of Environmental Health Sciences (NIEHS), National Toxicology Program (NTP), Research Triangle Park, North Carolina 27709, United States
| | - Scott S Auerbach
- National Institute of Environmental Health Sciences (NIEHS), National Toxicology Program (NTP), Research Triangle Park, North Carolina 27709, United States
| | - Vijay K Gombar
- Sciome LLC, Research Triangle Park, North Carolina 27709, United States
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5
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Abstract
A fullerene is a cubic three-connected graph whose faces are entirely composed of pentagons and hexagons. Entropy applied to graphs is one of the significant approaches to measuring the complexity of relational structures. Recently, the research on complex networks has received great attention, because many complex systems can be modelled as networks consisting of components as well as relations among these components. Information—theoretic measures have been used to analyze chemical structures possessing bond types and hetero-atoms. In the present article, we reviewed various entropy-based measures on fullerene graphs. In particular, we surveyed results on the topological information content of a graph, namely the orbit-entropy Ia(G), the symmetry index, a degree-based entropy measure Iλ(G), the eccentric-entropy Ifσ(G) and the Hosoya entropy H(G).
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6
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Zenil H, Hernández-Orozco S, Kiani NA, Soler-Toscano F, Rueda-Toicen A, Tegnér J. A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity. ENTROPY 2018; 20:e20080605. [PMID: 33265694 PMCID: PMC7513128 DOI: 10.3390/e20080605] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 06/18/2018] [Accepted: 07/31/2018] [Indexed: 12/28/2022]
Abstract
We investigate the properties of a Block Decomposition Method (BDM), which extends the power of a Coding Theorem Method (CTM) that approximates local estimations of algorithmic complexity based on Solomonoff–Levin’s theory of algorithmic probability providing a closer connection to algorithmic complexity than previous attempts based on statistical regularities such as popular lossless compression schemes. The strategy behind BDM is to find small computer programs that produce the components of a larger, decomposed object. The set of short computer programs can then be artfully arranged in sequence so as to produce the original object. We show that the method provides efficient estimations of algorithmic complexity but that it performs like Shannon entropy when it loses accuracy. We estimate errors and study the behaviour of BDM for different boundary conditions, all of which are compared and assessed in detail. The measure may be adapted for use with more multi-dimensional objects than strings, objects such as arrays and tensors. To test the measure we demonstrate the power of CTM on low algorithmic-randomness objects that are assigned maximal entropy (e.g., π) but whose numerical approximations are closer to the theoretical low algorithmic-randomness expectation. We also test the measure on larger objects including dual, isomorphic and cospectral graphs for which we know that algorithmic randomness is low. We also release implementations of the methods in most major programming languages—Wolfram Language (Mathematica), Matlab, R, Perl, Python, Pascal, C++, and Haskell—and an online algorithmic complexity calculator.
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Affiliation(s)
- Hector Zenil
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
- Correspondence:
| | - Santiago Hernández-Orozco
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
- Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico
| | - Narsis A. Kiani
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
| | | | - Antonio Rueda-Toicen
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
- Instituto Nacional de Bioingeniería, Universidad Central de Venezuela, Caracas 1051, Venezuela
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, SciLifeLab and Karolinska Institute, Stockholm SE-171 77, Sweden
- Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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7
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Chen J, Gao Y, Hu X, Qin D, Lu X. Descriptor selection based on variable stability for predicting inhibitor activity. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2017. [DOI: 10.1142/s0219633617500742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Quantitative structure-activity relationship (QSAR) has been a technique to study the relationship between chemical structures and properties, and variable selection is an important problem for finding the informative variables and building reliable models. A variable selection method based on variable stability is proposed and used for selecting the informative descriptors in the QSAR model of inhibitors. In the method, a series of models are built by leave-one-out cross validation (LOOCV), and variable stability is defined as the ratio of the absolute mean value and standard deviation of the regression coefficients in the models for a descriptor. Therefore, the descriptors with larger stabilities are more informative to the model. To further enhance the difference among the descriptors, an exponential parameter is used to modify the standard deviation. The results show that 13 descriptors are selected as informative ones from 1217 descriptors for the QSAR model of inhibitors. An effective prediction model can be constructed by them.
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Affiliation(s)
- Jing Chen
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou 730070, P. R. China
| | - Yunjing Gao
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou 730070, P. R. China
| | - Xiaoyan Hu
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou 730070, P. R. China
| | - Dongdong Qin
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou 730070, P. R. China
| | - Xiaoquan Lu
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou 730070, P. R. China
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8
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Shakibian H, Moghadam Charkari N. Mutual information model for link prediction in heterogeneous complex networks. Sci Rep 2017; 7:44981. [PMID: 28344326 PMCID: PMC5366872 DOI: 10.1038/srep44981] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 01/23/2017] [Indexed: 11/21/2022] Open
Abstract
Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices.
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Affiliation(s)
- Hadi Shakibian
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
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9
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Generalized Degree-Based Graph Entropies. Symmetry (Basel) 2017. [DOI: 10.3390/sym9030029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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10
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Using Graph and Vertex Entropy to Compare Empirical Graphs with Theoretical Graph Models. ENTROPY 2016. [DOI: 10.3390/e18090320] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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11
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12
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13
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14
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Li C, Wang XF, Chen Z, Zhang Z, Song J. Computational characterization of parallel dimeric and trimeric coiled-coils using effective amino acid indices. MOLECULAR BIOSYSTEMS 2015; 11:354-60. [DOI: 10.1039/c4mb00569d] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
RFCoil is a novel predictor for parallel coiled-coil dimer and trimer.
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Affiliation(s)
- Chen Li
- Department of Biochemistry and Molecular Biology
- Faculty of Medicine
- Monash University
- Melbourne
- Australia
| | - Xiao-Feng Wang
- State Key Laboratory of Agrobiotechnology
- College of Biological Sciences
- China Agricultural University
- Beijing 100193
- China
| | - Zhen Chen
- State Key Laboratory of Agrobiotechnology
- College of Biological Sciences
- China Agricultural University
- Beijing 100193
- China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology
- College of Biological Sciences
- China Agricultural University
- Beijing 100193
- China
| | - Jiangning Song
- Department of Biochemistry and Molecular Biology
- Faculty of Medicine
- Monash University
- Melbourne
- Australia
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15
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González-Díaz H, Herrera-Ibatá DM, Duardo-Sánchez A, Munteanu CR, Orbegozo-Medina RA, Pazos A. ANN Multiscale Model of Anti-HIV Drugs Activity vs AIDS Prevalence in the US at County Level Based on Information Indices of Molecular Graphs and Social Networks. J Chem Inf Model 2014; 54:744-55. [DOI: 10.1021/ci400716y] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Humberto González-Díaz
- Department
of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country UPV/EHU, 48940, Leioa, Vizcaya, Spain
- IKERBASQUE, Basque
Foundation for Science, 48011, Bilbao, Vizcaya, Spain
| | - Diana María Herrera-Ibatá
- Department of Information and Communication Technologies, University of A Coruña UDC, 15071, A Coruña, A Coruña, Spain
| | - Aliuska Duardo-Sánchez
- Department of Information and Communication Technologies, University of A Coruña UDC, 15071, A Coruña, A Coruña, Spain
| | - Cristian R. Munteanu
- Department of Information and Communication Technologies, University of A Coruña UDC, 15071, A Coruña, A Coruña, Spain
| | - Ricardo Alfredo Orbegozo-Medina
- Department
of Microbiology and Parasitology, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, A Coruña, Spain
| | - Alejandro Pazos
- Department of Information and Communication Technologies, University of A Coruña UDC, 15071, A Coruña, A Coruña, Spain
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16
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Tasdighian S, Di Paola L, De Ruvo M, Paci P, Santoni D, Palumbo P, Mei G, Di Venere A, Giuliani A. Modules Identification in Protein Structures: The Topological and Geometrical Solutions. J Chem Inf Model 2013; 54:159-68. [DOI: 10.1021/ci400218v] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Setareh Tasdighian
- Department
of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology, Ghent University, K.L. Ledeganckstraat 35, B-9000 Ghent, Belgium
| | - Luisa Di Paola
- Faculty
of Engineering, Università CAMPUS BioMedico, Via A. del
Portillo, 21, 00128 Roma, Italy
| | - Micol De Ruvo
- CNR-Institute of Systems Analysis and Computer Science (IASI), viale Manzoni 30, 00185 Roma, Italy
| | - Paola Paci
- CNR-Institute of Systems Analysis and Computer Science (IASI), viale Manzoni 30, 00185 Roma, Italy
| | - Daniele Santoni
- Department
of Experimental Medicine and Surgery, University of Rome “Tor Vergata”, via Montpellier 1, 00133 Rome, Italy
| | - Pasquale Palumbo
- CNR-Institute of Systems Analysis and Computer Science (IASI), viale Manzoni 30, 00185 Roma, Italy
- Department
of Experimental Medicine and Surgery, University of Rome “Tor Vergata”, via Montpellier 1, 00133 Rome, Italy
| | - Giampiero Mei
- Department
of Experimental Medicine and Surgery, University of Rome “Tor Vergata”, via Montpellier 1, 00133 Rome, Italy
| | - Almerinda Di Venere
- Environment
and Health Department, Istituto Superiore di Sanità, Viale
Regina Elena 299, 00161, Roma, Italy
| | - Alessandro Giuliani
- Environment
and Health Department, Istituto Superiore di Sanità, Viale
Regina Elena 299, 00161, Roma, Italy
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Dehmer M, Mowshowitz A. The discrimination power of structural SuperIndices. PLoS One 2013; 8:e70551. [PMID: 23936227 PMCID: PMC3723667 DOI: 10.1371/journal.pone.0070551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2013] [Accepted: 06/25/2013] [Indexed: 11/29/2022] Open
Abstract
In this paper, we evaluate the discrimination power of structural superindices. Superindices for graphs represent measures composed of other structural indices. In particular, we compare the discrimination power of the superindices with those of individual graph descriptors. In addition, we perform a statistical analysis to generalize our findings to large graphs.
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Affiliation(s)
- Matthias Dehmer
- Institute for Bioinformatics and Translational Research, Hall in Tyrol, Austria.
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18
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Grabner M, Varmuza K, Dehmer M. RMol: a toolset for transforming SD/Molfile structure information into R objects. SOURCE CODE FOR BIOLOGY AND MEDICINE 2012; 7:12. [PMID: 23151338 PMCID: PMC3599689 DOI: 10.1186/1751-0473-7-12] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2012] [Accepted: 10/30/2012] [Indexed: 11/28/2022]
Abstract
Background The graph-theoretical analysis of molecular networks has a long tradition in chemoinformatics. As demonstrated frequently, a well designed format to encode chemical structures and structure-related information of organic compounds is the Molfile format. But when it comes to use modern programming languages for statistical data analysis in Bio- and Chemoinformatics, R as one of the most powerful free languages lacks tools to process Molfile data collections and import molecular network data into R. Results We design an R object which allows a lossless information mapping of structural information from Molfiles into R objects. This provides the basis to use the RMol object as an anchor for connecting Molfile data collections with R libraries for analyzing graphs. Associated with the RMol objects, a set of R functions completes the toolset to organize, describe and manipulate the converted data sets. Further, we bypass R-typical limits for manipulating large data sets by storing R objects in bz-compressed serialized files instead of employing RData files. Conclusions By design, RMol is a R toolset without dependencies to other libraries or programming languages. It is useful to integrate into pipelines for serialized batch analysis by using network data and, therefore, helps to process sdf-data sets in R efficiently. It is freely available under the BSD licence. The script source can be downloaded from
http://sourceforge.net/p/rmol-toolset.
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Affiliation(s)
- Martin Grabner
- Department of Biomedical Sciences and Engineering, Institute for Bioinformatics and Translational Research, University for Health Sciences, Medical Informatics and Technology (UMIT), Eduard Wallnöfer Zentrum 1, Hall in Tyrol, A-6060, Austria.
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19
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Netzer M, Kugler KG, Müller LAJ, Weinberger KM, Graber A, Baumgartner C, Dehmer M. A network-based feature selection approach to identify metabolic signatures in disease. J Theor Biol 2012; 310:216-22. [PMID: 22771628 DOI: 10.1016/j.jtbi.2012.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2011] [Revised: 04/16/2012] [Accepted: 06/03/2012] [Indexed: 12/17/2022]
Abstract
The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.
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Affiliation(s)
- Michael Netzer
- Research Group for Clinical Bioinformatics, Institute of Electrical and Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tyrol, Austria.
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21
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Dehmer M, Grabner M, Varmuza K. Information indices with high discriminative power for graphs. PLoS One 2012; 7:e31214. [PMID: 22393358 PMCID: PMC3290601 DOI: 10.1371/journal.pone.0031214] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2011] [Accepted: 01/04/2012] [Indexed: 11/18/2022] Open
Abstract
In this paper, we evaluate the uniqueness of several information-theoretic measures for graphs based on so-called information functionals and compare the results with other information indices and non-information-theoretic measures such as the well-known Balaban J index. We show that, by employing an information functional based on degree-degree associations, the resulting information index outperforms the Balaban J index tremendously. These results have been obtained by using nearly 12 million exhaustively generated, non-isomorphic and unweighted graphs. Also, we obtain deeper insights on these and other topological descriptors when exploring their uniqueness by using exhaustively generated sets of alkane trees representing connected and acyclic graphs in which the degree of a vertex is at most four.
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Affiliation(s)
- Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT, Hall in Tyrol, Austria.
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22
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Netzer M, Weinberger KM, Handler M, Seger M, Fang X, Kugler KG, Graber A, Baumgartner C. Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers. J Clin Bioinforma 2011; 1:34. [PMID: 22182709 PMCID: PMC3320562 DOI: 10.1186/2043-9113-1-34] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2011] [Accepted: 12/19/2011] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND In metabolomics, biomarker discovery is a highly data driven process and requires sophisticated computational methods for the search and prioritization of novel and unforeseen biomarkers in data, typically gathered in preclinical or clinical studies. In particular, the discovery of biomarker candidates from longitudinal cohort studies is crucial for kinetic analysis to better understand complex metabolic processes in the organism during physical activity. FINDINGS In this work we introduce a novel computational strategy that allows to identify and study kinetic changes of putative biomarkers using targeted MS/MS profiling data from time series cohort studies or other cross-over designs. We propose a prioritization model with the objective of classifying biomarker candidates according to their discriminatory ability and couple this discovery step with a novel network-based approach to visualize, review and interpret key metabolites and their dynamic interactions within the network. The application of our method on longitudinal stress test data revealed a panel of metabolic signatures, i.e., lactate, alanine, glycine and the short-chain fatty acids C2 and C3 in trained and physically fit persons during bicycle exercise. CONCLUSIONS We propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Many of them could be verified and confirmed by literature. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/.
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Affiliation(s)
- Michael Netzer
- Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, UMIT, 6060 Hall in Tirol, Austria.
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Mueller LAJ, Kugler KG, Netzer M, Graber A, Dehmer M. A network-based approach to classify the three domains of life. Biol Direct 2011; 6:53. [PMID: 21995640 PMCID: PMC3226542 DOI: 10.1186/1745-6150-6-53] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2011] [Accepted: 10/13/2011] [Indexed: 11/22/2022] Open
Abstract
Background Identifying group-specific characteristics in metabolic networks can provide better insight into evolutionary developments. Here, we present an approach to classify the three domains of life using topological information about the underlying metabolic networks. These networks have been shown to share domain-independent structural similarities, which pose a special challenge for our endeavour. We quantify specific structural information by using topological network descriptors to classify this set of metabolic networks. Such measures quantify the structural complexity of the underlying networks. In this study, we use such measures to capture domain-specific structural features of the metabolic networks to classify the data set. So far, it has been a challenging undertaking to examine what kind of structural complexity such measures do detect. In this paper, we apply two groups of topological network descriptors to metabolic networks and evaluate their classification performance. Moreover, we combine the two groups to perform a feature selection to estimate the structural features with the highest classification ability in order to optimize the classification performance. Results By combining the two groups, we can identify seven topological network descriptors that show a group-specific characteristic by ANOVA. A multivariate analysis using feature selection and supervised machine learning leads to a reasonable classification performance with a weighted F-score of 83.7% and an accuracy of 83.9%. We further demonstrate that our approach outperforms alternative methods. Also, our results reveal that entropy-based descriptors show the highest classification ability for this set of networks. Conclusions Our results show that these particular topological network descriptors are able to capture domain-specific structural characteristics for classifying metabolic networks between the three domains of life.
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Affiliation(s)
- Laurin A J Mueller
- Institute for Bioinformatics and Translational Research, Department of Biomedical Sciences and Engineering, University for Health Sciences, Medical Informatics and Technology (UMIT), Austria
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