scholarly journals An artificial-intelligence technique for qualitatively deriving enzyme kinetic mechanisms from initial-velocity measurements and its application to hexokinase

1989 ◽  
Vol 264 (1) ◽  
pp. 175-184 ◽  
Author(s):  
L Garfinkel ◽  
D M Cohen ◽  
V W Soo ◽  
D Garfinkel ◽  
C A Kulikowski
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Ionic Strength ◽  
Initial Velocity ◽  
Large Data ◽  
Data Sets ◽  
Enzyme Kinetic ◽  
Experimental Conditions ◽  
Enzyme Preparations ◽  
Kinetic Mechanisms

We have developed a computer method based on artificial-intelligence techniques for qualitatively analysing steady-state initial-velocity enzyme kinetic data. We have applied our system to experiments on hexokinase from a variety of sources: yeast, ascites and muscle. Our system accepts qualitative stylized descriptions of experimental data, infers constraints from the observed data behaviour and then compares the experimentally inferred constraints with corresponding theoretical model-based constraints. It is desirable to have large data sets which include the results of a variety of experiments. Human intervention is needed to interpret non-kinetic information, differences in conditions, etc. Different strategies were used by the several experimenters whose data was studied to formulate mechanisms for their enzyme preparations, including different methods (product inhibitors or alternate substrates), different experimental protocols (monitoring enzyme activity differently), or different experimental conditions (temperature, pH or ionic strength). The different ordered and rapid-equilibrium mechanisms proposed by these experimenters were generally consistent with their data. On comparing the constraints derived from the several experimental data sets, they are found to be in much less disagreement than the mechanisms published, and some of the disagreement can be ascribed to different experimental conditions (especially ionic strength).

scholarly journals Spatial and temporal tools for building a human cell atlas

2019 ◽  
Vol 30 (19) ◽  
pp. 2435-2438 ◽  
Author(s):  
Jonah Cool ◽  
Richard S. Conroy ◽  
Sean E. Hanlon ◽  
Shannon K. Hughes ◽  
Ananda L. Roy
Keyword(s):  
Scientific Community ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Experimental Conditions ◽  
Modeling Tools ◽  
Holistic View ◽  
Multiple Timescales ◽  
Single Cell Sequencing ◽  
Coordination And Collaboration

Improvements in the sensitivity, content, and throughput of microscopy, in the depth and throughput of single-cell sequencing approaches, and in computational and modeling tools for data integration have created a portfolio of methods for building spatiotemporal cell atlases. Challenges in this fast-moving field include optimizing experimental conditions to allow a holistic view of tissues, extending molecular analysis across multiple timescales, and developing new tools for 1) managing large data sets, 2) extracting patterns and correlation from these data, and 3) integrating and visualizing data and derived results in an informative way. The utility of these tools and atlases for the broader scientific community will be accelerated through a commitment to findable, accessible, interoperable, and reusable data and tool sharing principles that can be facilitated through coordination and collaboration between programs working in this space.

scholarly journals Compartment and hub definitions tune metabolic networks for metabolomic interpretations

GigaScience ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
T Cameron Waller ◽  
Jordan A Berg ◽  
Alexander Lex ◽  
Brian E Chapman ◽  
Jared Rutter
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Shortest Paths ◽  
Large Data ◽  
Differential Regulation ◽  
Large Data Sets ◽  
Data Sets ◽  
Human Metabolism ◽  
Experimental Conditions ◽  
Systemic Model

Abstract Background Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism’s cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. Results We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. Conclusions Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

Emerging Pharmacotherapy and Health Care Needs of Patients in the Age of Artificial Intelligence and Digitalization

2020 ◽  
Vol 54 (10) ◽  
pp. 1038-1046
Author(s):  
Barbara J. Zarowitz
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Ethnic Diversity ◽  
Health Care Professionals ◽  
Large Data ◽  
The United States ◽  
Wearable Devices ◽  
Data Sets ◽  
Care Needs ◽  
Clinician Training

Advances in the application of artificial intelligence, digitization, technology, iCloud computing, and wearable devices in health care predict an exciting future for health care professionals and our patients. Projections suggest an older, generally healthier, better-informed but financially less secure patient population of wider cultural and ethnic diversity that live throughout the United States. A pragmatic yet structured approach is recommended to prepare health care professionals and patients for emerging pharmacotherapy needs. Clinician training should include genomics, cloud computing, use of large data sets, implementation science, and cultural competence. Patients will need support for wearable devices and reassurance regarding digital medicine.

scholarly journals Tweaking Business Planning With Artificial Intelligence

2021 ◽  
Vol 2 (4) ◽  
pp. 1-22
Author(s):  
Jing Rui Chen ◽  
P. S. Joseph Ng
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Learning Experience ◽  
Compulsory Education ◽  
Large Data ◽  
Data Sets ◽  
Chinese Market ◽  
Artificial Intelligence Technology ◽  
Education Support

Griffith AI&BD is a technology company that uses big data platform and artificial intelligence technology to produce products for schools. The company focuses on primary and secondary school education support and data analysis assistance system and campus ARTIFICIAL intelligence products for the compulsory education stage in the Chinese market. Through big data, machine learning and data mining, scattered on campus and distributed systems enable anyone to sign up to join the huge data processing grid, and access learning support big data analysis and matching after helping students expand their knowledge in a variety of disciplines and learning and promotion. Improve the learning process based on large data sets of students, and combine ai technology to develop AI electronic devices. To provide schools with the best learning experience to survive in a competitive world.

scholarly journals Artificial Intelligence Explained for Nonexperts

2020 ◽  
Vol 24 (01) ◽  
pp. 003-011 ◽  
Author(s):  
Narges Razavian ◽  
Florian Knoll ◽  
Krzysztof J. Geras
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Medical Imaging ◽  
Deep Neural Networks ◽  
Large Data ◽  
Large Data Sets ◽  
Natural Images ◽  
Data Sets ◽  
Current State

AbstractArtificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.

With a Little Help From AI

2021 ◽  
Vol 2 (2) ◽  
pp. 19-33
Author(s):  
Adam Urban ◽  
David Hick ◽  
Joerg Rainer Noennig ◽  
Dietrich Kammer
Keyword(s):  
Artificial Intelligence ◽  
Urban Design ◽  
Large Data ◽  
User Participation ◽  
Large Data Sets ◽  
Data Sets ◽  
Design Decision ◽  
Analysis And Synthesis ◽  
Algorithmic Analysis ◽  
Future Potential

Exploring the phenomenon of artificial intelligence (AI) applications in urban planning and governance, this article reviews most current smart city developments and outlines the future potential of AI, especially in the context of participatory urban design. It concludes that especially the algorithmic analysis and synthesis of large data sets generated by massive user participation projects present a beneficial field of application that enables better design decision making, project validation, and evaluation.

scholarly journals ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

Artificial intelligence: predictive analytics of perinatal risks

2020 ◽  
Vol 19 (6) ◽  
pp. 133-144
Author(s):  
A.A. Ivshin ◽  
◽  
A.V. Gusev ◽  
R.E. Novitskiy ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Congenital Abnormalities ◽  
Predictive Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Fetal Hypoxia ◽  
Perinatal Medicine ◽  
Processing Power

Artificial intelligence (AI) has recently become an object of interest for specialists from various fields of science and technology, including healthcare professionals. Significantly increased funding for the development of AI models confirms this fact. Advances in machine learning (ML), availability of large data sets, and increasing processing power of computers promote the implementation of AI in many areas of human activity. Being a type of AI, machine learning allows automatic development of mathematical models using large data sets. These models can be used to address multiple problems, such as prediction of various events in obstetrics and neonatology. Further integration of artificial intelligence in perinatology will facilitate the development of this important area in the future. This review covers the main aspects of artificial intelligence and machine learning, their possible application in healthcare, potential limitations and problems, as well as outlooks in the context of AI integration into perinatal medicine. Key words: artificial intelligence, cardiotocography, neonatal asphyxia, fetal congenital abnormalities, fetal hypoxia, machine learning, neural networks, prediction, prognosis, perinatal risk, prenatal diagnosis

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