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A Comparative Study of Proteomics Maps Using Graph Theoretical Biodescriptors
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Optimal molecular connectivity descriptors for nitrogen-containing molecules
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On characterization of physical properties of amino acids
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A COMPARATIVE STUDY OF MOLECULAR SIMILARITY, STATISTICAL, AND NEURAL METHODS FOR PREDICTING TOXIC MODES OF ACTION
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On computation of optimal parameters for multivariate analysis of structure‐property relationship
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Spectrofluorometric Determination of Bufuralol in Blood and Urine
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Encyclopedia of Computational Chemistry
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On a graph theoretical basis for ordering of structures
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Determining structural similarity of chemicals using graph-theoretic indices
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Novel graph theoretical approach to heteroatoms in quantitative structure—activity relationships
inferredcites →https://doi.org/10.1021/j100474a032openalex-snapshot
Graph theoretical ordering of structures as a basis for systematic searches for regularities in molecular data
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Pattern recognition. Powerful approach to interpreting chemical data
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Role of ring torsion in the electrocyclic transformation between cyclobutene and butadiene. Theoretical study
inferredcites →https://doi.org/10.1021/ja00856a001openalex-snapshot
Characterization of molecular branching
inferredcites →https://doi.org/10.1021/pr0100117openalex-snapshot
On Characterization of Dose Variations of 2-D Proteomics Maps by Matrix Invariants
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Characterization of Toxicoproteomics Maps for Chemical Mixtures Using Information Theoretic Approach
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Proteomics in biomarker discovery and drug development
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Quantitative Characterization of Proteomics Maps by Matrix Invariants
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On numerical characterization of proteomics maps based on partitioning of 2-D maps into Voronoi regions
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Quantitative Proteome–Property Relationships (QPPRs). Part 1: Finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra
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Graphical representations of DNA as 2-D map
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Study of proteome maps using partial ordering
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Shannon’s entropy of proteomic 2D-gel maps
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Novel Characterization of Proteomics Maps by Sequential Neighborhoods of Protein Spots
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On the Dependence of a Characterization of Proteomics Maps on the Number of Protein Spots Considered
inferred← citeshttps://doi.org/10.1021/cr0780006openalex-snapshot
Some New Trends in Chemical Graph Theory
inferred← citeshttps://doi.org/10.1021/cr800198jopenalex-snapshot
Graphical Representation of Proteins
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Aromaticity of Polycyclic Conjugated Hydrocarbons
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On Invariants of a 2-D Proteome Map Derived from Neighborhood Graphs
inferred← citeshttps://doi.org/10.1021/pr050049+openalex-snapshot
Canonical Labeling of Proteome Maps
inferred← citeshttps://doi.org/10.1021/pr050463+openalex-snapshot
Quantitative Characterizations of Proteome: Dependence on the Number of Proteins Considered
inferred← citeshttps://doi.org/10.1039/9781782623656-00307openalex-snapshot
Chemometrical Analysis of Proteomics Data
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Novel matrix invariants for characterization of changes of proteomics maps
inferred← citeshttps://doi.org/10.1080/10629360410001697744openalex-snapshot
2-D Graphical representation of proteins based on virtual genetic code
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Highly compact 2D graphical representation of DNA sequences
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The Power of Graph Convolutional Networks to Distinguish Random Graph Models
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Fundamental Limits of Deep Graph Convolutional Networks for Graph Classification
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Quantitative Structure–Toxicity Relationships Using Chemodescriptors and Biodescriptors
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Use of Biodescriptors and Chemodescriptors in Predictive Toxicology: A Mathematical/Computational Approach
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QSAR, Complex Networks, Principal Components and Partial Order Analysis of Drug Cardiotoxicity with Proteome Mass- Spectra Topological Indices
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Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs
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Fundamental Limits of Deep Graph Convolutional Networks
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The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version
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