26 papers · ranked by Valyu relevance
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…
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…
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…
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
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…
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…
Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth + 3 more
'David Broneske' 'Gunter Saake' 'Robert Heyer'] Title: Abstract The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving…
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…
Chin Ying Liew, Jane Labadin, Woon Chee Kok, Monday Okpoto Eze
The graph-theoretic based studies employing bipartite network approach mostly focus on surveying the statistical properties of the structure and behavior of the network systems under the domain of complex network analysis. They aim to provide the big-picture-view insights of a networked system by looking into the…
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…
David Cole, Víctor M. Zavala
Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and…
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…
Manal Helal
Patents provide a rich source of information about design innovations. Patent mining techniques employ various technologies, such as text mining, machine learning, natural language processing, and ontology-building techniques. An automated graph data modelling method is proposed for extracting functional…
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…
Miguel E. Coimbra, Lucie Svitáková, Alexandre P. Francisco, Luís Veiga
'Luís Veiga'] Graph databases have become essential tools for managing complex and interconnected data, which is common in areas like social networks, bioinformatics, and recommendation systems. Unlike traditional relational databases, graph databases offer a more natural way to model and query intricate relationships…
Nafiseh Jafarzadeh, Jordan M Eizenga, Benedict Paten
In this paper, we present diploid sequence graphs, graphs whose paths encode pairs of haplotypes. We describe an efficient algorithm for creating a diploid graph from a directed acyclic (haploid) sequence graph, such that the diploid graph represents all the possible pairings of haplotypes present in the sequence graph…
Sabrina Benbatata, Bilal Saoud, Ibraheem Shayea, Naif Alsharabi + 5 more
'Abdulraqeb Alhammadi' 'Ali Alferaidi' 'Amr Jadi' 'Yousef Ibrahim Daradkeh' 'José Alberto Benítez-Andrades'] In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the…
Cai Lu, Xinran Xu, Bingbin Zhang
The uncertainty of structural interpretation complicates the practical production and application of data-driven complex geological structure modeling technology. Intelligent structural modeling excavates and extracts structural knowledge from structural interpretation through human-machine collaboration and combines…
Malcolm Crowe, Fritz Laux
Technology Authors: ['Malcolm Crowe' 'Fritz Laux'] Abstract—Recent standardization work for database languages has reflected the growing use of typed graph models (TGM) in application development. Such data models are frequently only used early in the design process, and not reflected directly in underlying physical…
Anthony Sirico, Daniel R. Herber
Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek…
Hanna Krasowski, Eric Palanques-Tost, Calin Belta, Murat Arcak
Modeling dynamical biological systems is key for understanding, predicting, and controlling complex biological behaviors. Traditional methods for identifying governing equations, such as ordinary differential equations (ODEs), typically require extensive quantitative data, which is often scarce in biological systems…
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…