16 papers · ranked by Valyu relevance
Eva Brombacher, Clemens Kreutz, Pier Luigi Martelli
Ordinary differential equation (ODE) models are currently the method of choice in systems biology for mathematically modelling the dynamics of signalling pathways within cells. However, ODEs have limitations, including the lack of analytical solutions and the need for large, complex models that require extensive…
Dilan Pathirana, Frank T. Bergmann, Domagoj Doresic, Polina Lakrisenko + 8 more
A central question in mathematical modeling of biological systems is determining which processes are relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the specification of…
Tim Maiwald, Helge Hass, Bernhard Steiert, Joep Vanlier + 8 more
'Raphael Engesser' 'Andreas Raue' 'Friederike Kipkeew' 'Hans H. Bock' 'Daniel Kaschek' 'Clemens Kreutz' 'Jens Timmer' 'Byung-Jun Yoon'] In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined…
Hui Li, Chunmei Liu
Biology data is increasing exponentially from biological laboratories. It is a complicated problem for further processing the data. Processing computational data and data from biological laboratories manually may lead to potential errors in further analysis. In this paper, we proposed an efficient data-driven framework…
Danial Yazdani, Juergen Branke, Mohammad Sadegh Khorshidi, Mohammad Nabi Omidvar + 3 more
Generation With Heterogeneous Changes Authors: ['Danial Yazdani' 'Juergen Branke' 'Mohammad Sadegh Khorshidi' 'Mohammad Nabi Omidvar' 'Xiaodong Li' 'Amir H. Gandomi' 'Xin Yao'] XIN YAO, Research Institute of Trustworthy Autonomous Systems (RITAS), and Guangdong Provincial Key Laboratory of Brain inspired Intelligent…
Lijia Jia, Yue Shi, Jing Yang, Shangzhe Li + 14 more
The explosive growth of digital data is overwhelming conventional storage media, creating an urgent need for more efficient solutions. DNA offers immense potential for digital data storage, yet most systems remain static and archival. Here, we present a modular DNA storage architecture based on dynamic DNA bytes…
Shantenu Jha, Daniel S. Katz, André Luckow, Omer Rana + 2 more
'Yogesh Simmhan' 'Neil Chue Hong'] A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growing larger and becoming distributed; and their location, availability and properties are often…
Pavel S Novichkov, John-Marc Chandonia, Adam P Arkin
Dynamic data types are most often used for data related to the core types, such as measurements taken on core objects. Dynamic types are defined by combining a limited number of simple, mathematical data structures (e.g., matrices, trees, graphs) with contextons that provide critical context to the data, in order to…
Glenda M. Yenni, Erica M. Christensen, Ellen K. Bledsoe, Sarah R. Supp + 3 more
Data management and publication are core components of the research process. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. “Evolving data” present unique challenges in quality assurance and control, data…
Anna Quaglieri, Joseph Bloom, Aaron Triantafyllidis, Bradley Green + 4 more
Data processing is essential to reliably generate knowledge from proteomics studies. The complexity of the proteomics data, as well as the ability of research teams to adopt complex analysis pipelines, have proven to be an obstacle to effective collaboration and more efficient biological insight generation. Here, we…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Authors not listed
The persistent failure to drug mutant forms of key oncogenes—such as TP53, KRAS, and MYC—is largely due to their lack of stable, visible binding pockets in crystal structures. These proteins are frequently labeled “undruggable,” not because they cannot be modulated, but because their ligandable sites are hidden…
Harley Edwards, Joseph Zavorskas, Walker Huso, Alexander G. Doan + 4 more
Derivative profiling (DP) is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics…
Danielle C. Robinson, Joe A. Hand, Mathias Buus Madsen, Karissa R. McKelvey
'Karissa R. McKelvey'] Today’s scientific data are primarily stored and accessed via centralized Web-based infrastructure. Centralization has advantages but also carries risks such as link rot and content drift, which can hinder scientific progress. It is time to ask whether traditional, centralized Web architecture…
Dimitri Yatsenko, Jacob Reimer, Alexander S. Ecker, Edgar Y. Walker + 6 more
The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse…
Yasith Jayawardana, Vikas Ashok, Sampath Jayarathna
Reusable data/code and reproducible analyses are foundational to quality research. This aspect, however, is often overlooked when designing interactive stream analysis workflows for time-series data (e.g., eye-tracking data). A mechanism to transmit informative metadata alongside data may allow such workflows to…