25 papers · ranked by Valyu relevance
Arafat Abdulgader Mohammed Elhag, Radziah Mohamad, Muhammad Waqar Aziz, Furkh Zeshan + 1 more
'Muhammad Waqar Aziz' 'Furkh Zeshan' 'Cheng-Yi Xia'] The composite service design modeling is an essential process of the service-oriented software development life cycle, where the candidate services, composite services, operations and their dependencies are required to be identified and specified before their design.…
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu + 1 more
'Peter Battaglia'] Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs…
Chen Fang, Zhilong Hu, Shaole Chang, Qingqing Long + 11 more
Inspired by the advancements in pre-trained Large Language Models, there has been a surge of studies in the Life Sciences focusing on constructing foundation models with large scale single-cell RNA-seq data. These studies typically involve pre-training a transformer model on large-scale single-cell sequencing data…
Amine Ghrab, Oscar Romero, Sabri Skhiri, Alejandro Vaisman + 1 more
'Esteban Zimányi'] Abstract. Graph databases have emerged as the fundamental technology underpinning trendy application domains where traditional databases are not well-equipped to handle complex graph data. However, current graph databases support basic graph structures and integrity constraints with no standard…
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
Santiago Timón-Reina, Mariano Rincón, Rafael Martínez-Tomás
Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because…
Authors not listed
Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture…
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…
Regina Samaga, Steffen Klamt
A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is…
Teddy Lazebnik, Hanna Weitman, Gal A. Kaminka
Pharmaceutical nanoparticles (NPs) carrying molecular payloads are used for medical purposes such as diagnosis and medical treatment. They are designed to modify the pharmacokinetics-pharmacodynamics (PKPD) of their associated payloads, to obtain better clinical results. Currently, the research process of discovering…
Daniel M. Gonçalves, André Patrício, Rafael S. Costa, Rui Henriques
The growing availability and complexity of omics data have driven the development of specialized algorithms for modeling molecular systems. Although graph-based learning methods effectively represent biological interactions, they often neglect the statistical information embedded in node and edge annotations. To…
Sabah Al‐Fedaghi
- In software system design, one of the purposes of diagrammatic modeling is to explain something (e.g., data tables) to others. Very often, syntax of diagrams is specified while the intended meaning of diagrammatic constructs remains intuitive and approximate. Conceptual modeling has been developed to capture concepts…
Sumit Purohit, Nhuy Van, George Chin
—Graphs are a natural and fundamental representation of describing the activities, relationships, and evolution of various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource…
Authors not listed
Graph Neural Networks (GNNs) have emerged as a powerful tool in predicting molecular properties based on structural data. While GNNs excel in identifying local patterns within molecules, their ability to capture global properties remains limited due to inherent structural challenges such as oversmoothing and their…
Marta Pasquini, Marco Stenta
Background. The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction…
Ezio Bartocci, Pietro Lió, Marta Zofia Kwiatkowska
As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable…
Charalampos P. Triantafyllidis, Ricardo Aguas
We employ a computational framework that integrates mathematical programming and graph neural networks to elucidate functional phenotypic heterogeneity in disease by classifying entire pathways under various conditions of interest. Our approach combines two distinct, yet seamlessly integrated, modeling schemes. First…
Liying Chen, Satwik Acharyya, Chunyu Luo, Yang Ni + 1 more
Probabilistic graphical models are powerful and widely used tools to quantify, visualize and interpret dependencies in complex biological systems such as highthroughput genomics and proteomics. However, most existing graphical modeling methods assume homogeneity within and across samples which restricts their broad…
Renzo Angles, Claudio Gutiérrez
A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graphoriented operations and type constructors. In this article we present the basic notions of graph databases, give an…
Jens Schrodt, Aleksei Dudchenko, Petra Knaup-Gregori, Matthias Ganzinger
'Matthias Ganzinger'] Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data…
Ron Henkel, Olaf Wolkenhauer, Dagmar Waltemath
Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article…
Joshua Levy, Carly Bobak, Brock Christensen, Louis Vaickus + 1 more
Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While…
Michael Statt, Brian Rohr, Dan Guevarra, Ja'Nya Breeden + 2 more
Materials knowledge is inherently hierarchical. While high-level descriptors such as composition and structure are valuable for contextualizing materials data, the data must ultimately be considered in the context of its low-level acquisition details. Graph databases offer an opportunity to represent hierarchical…
Authors not listed
Graph theory provides a framework for clearly representing relationships between objects [1,2]. In the fields of chemistry and biology, graph-based concepts are widely applied. Hypergraphs generalize classical graphs by allowing hyperedges to connect any nonempty subset of vertices [3]. Superhypergraphs extend this…
Marco Fariñas, Eirini Tsirvouli, John Zobolas, Tero Aittokallio + 2 more
Boolean models are widely used for studying dynamic processes of biological systems. However, their inherent discrete nature limits their ability to capture continuous aspects of signal transduction, such as signal strength or protein activation levels. Although existing tools provide some path exploration capabilities…