25 papers · ranked by Valyu relevance
Iqbal H. Sarker
Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems…
Reesha J. Patel, Michael L. Blinov, David Nickerson
Rule-based modeling provides a powerful framework for describing and simulating biochemical systems composed of multi-site molecules and multi-molecular species. By encoding molecular interactions as rules rather than enumerating all possible species, this approach naturally accounts for the combinatorial complexity of…
Ryan Suderman, William S. Hlavacek
Rule-based modeling languages, such as the Kappa and BioNetGen languages (BNGL), are powerful frameworks for modeling the dynamics of complex biochemical reaction networks. Each language is distributed with a distinct software suite and modelers may wish to take advantage of both toolsets. This paper introduces a…
Katrin Kolczyk, Regina Samaga, Holger Conzelmann, Sebastian Mirschel + 1 more
'Carsten Conradi'] Background Signaling systems typically involve large, structured molecules each consisting of a large number of subunits called molecule domains. In modeling such systems these domains can be considered as the main players. In order to handle the resulting combinatorial complexity, rule-based…
Carsten Maus, Stefan Rybacki, Adelinde M Uhrmacher
Background Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted…
Dan Vasilescu, James Greene, James C. Schaff, Ion I Moraru + 1 more
Rule-based modeling allows representation and simulation of biological systems where molecular features (such as protein domains) and feature-specific details of molecular interactions are accounted for. The rule-based description is precise and can define very fine molecular details (e.g. how phosphorylation status of…
Till Köster, Philipp Henning, Tom Warnke, Adelinde Uhrmacher
Compartmentalization is vital for cell biological processes. The field of rule-based stochastic simulation has acknowledged this, and many tools and methods have capabilities for compartmentalization. However, mostly, this is limited to a static compartmental hierarchy and does not integrate compartmental changes.…
John A. P. Sekar, James R. Faeder
It is well known that cells process external and internal signals through chemical interactions (Lodish et al., 2008; Nelson and Cox, 2013). Cells that constitute the immune system can be of many different types, such as antigen presenting cell, T-cell, B-cell, mast cell, etc. Each of these cells can have different…
Alexander L.R. Lubbock, Carlos F. Lopez
Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models…
Themistoklis Botsas, Lachlan Mason, Indranil Pan
We introduce a novel rule-based approach for handling regression problems. The new methodology carries elements from two frameworks: (i) it provides information about the uncertainty of the parameters of interest using Bayesian inference, and (ii) it allows the incorporation of expert knowledge through rule-based…
Eyke Hüllermeier
This paper briefly elaborates on a development in (applied) fuzzy logic that has taken place in the last couple of decades, namely, the complementation or even replacement of the traditional knowledge-based approach to fuzzy rule-based systems design by a data-driven one. It is argued that the classical rule-based…
Maryam Kouzehgar, M. A., S. Khanmohammadi
— Regarding the rapid growth of the size and complexity of simulation applications, designing applicable and affordable verification and validation (V&V) structures is an important problem. On the other hand, nowadays human behavior models are principles to make decision in many simulations and in order to have valid…
Saeed Hassanpour, Martin J O’Connor, Amar K Das
Background A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex…
Antonio d'Acierno, Massimo Esposito, Giuseppe De Pietro
Background The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of…
Carl Corea, Matthias Thimm
We investigate the application of inconsistency measures to the problem of analysing business rule bases. Due to some intricacies of the domain of business rule bases, a straightforward application is not feasible. We therefore develop some new rationality postulates for this setting as well as adapt and modify…
Authors not listed
SynTemp is a framework designed to extract and hierarchically cluster reaction templates from large-scale reaction data repositories. Reaction templates are partial Imaginary Transition State graphs representing the reaction center as well as surrounding context. These graphs are equivalent to Double Pushout graph…
Kara Bocan, Nataša Miškov-Živanov
—Computational modeling of a complex system is limited by the parts of the system with the least information. While detailed models and high-resolution data may be available for parts of a system, abstract relationships are often necessary to connect the parts and model the full system. For example, modeling food…
Friedrich Hastedt, Rowan M. Bailey, Klaus Hellgardt, Sophia N. Yaliraki + 2 more
Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box interpretability, obscure model limitations and impede progress in the field. We…
Tieu-Long Phan, Klaus Weinbauer, Thomas Gärtner, Daniel Merkle + 3 more
Purpose: Reaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and…
Pulan Yu
Associative classification mining (ACM) integrating association rule mining and classification has become a significant tool for knowledge discovery, especially in the chemical domain. Its major advantage is providing high accuracy as well as chemically interpretable models. Additionally, it is able to find…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Laura Lema-Perez, Rafael Muñoz-Tamayo, Jose Garcia-Tirado, Hernan Alvarez
Empirical and phenomenological based models are used to represent biological and physiological processes. Phenomenological models are derived from the knowledge of the mechanisms that underlie the behaviour of the system under study, while empirical models are derived from analysis of data to quantify relationships…
Arnaud Poret, Claudio Monteiro Sousa, Jean-Pierre Boissel
Due to the scarcity of quantitative details about biological phenomena, quantitative modeling in systems biology can be compromised, especially at the subcellular scale. One way to get around this is qualitative modeling because it requires few to no quantitative information. One of the most popular qualitative…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…