Dahlquist bosc 20160709
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Talk given at the Bioinformatics Open Source Conference (BOSC) on July 9, 2016 in Orlando, Florida, entitled: GRNmap and GRNsight: open source software for dynamical systems modeling and visualization of medium-scale gene regulatory networks. Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Over a period of several years, our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. The large number of developers and time span of development led to a code base that was difficult to revise and adjust. We therefore brought the code under version control in a GitHub repository and refactored the script-based software with global variables into a function-based package that uses an object to carry relevant information from function to function. This modular approach allows for cleaner, less ambiguous code and increased maintainability. We standardized the format of the input and output Excel workbooks, making them more readable. We also added an optimization diagnostics output worksheet which includes both the actual and theoretical minimum least squared error overall, and the mean squared errors for the individual genes. The MATLAB compiler was used to create an executable that can be run on any Windows machine without the need of a MATLAB license, increasing the accessibility of our program. Finally, we have implemented test-driven development, creating unit tests for all new features to speed up debugging and to prevent future code regressions. We are improving the test coverage of previous code. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. It is written in JavaScript, with diagrams facilitated by D3.js. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized. GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. Visualizations can be modified through manual node dragging and sliders that adjust the force graph parameters. Truncated...
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Anguiano varshneya sccur-poster_20141122
Poster presented at the Southern California Conference for Undergraduate Research on November 22, 2014 by Nicole Anguiano and Anindita Varshneya entitled "Improvements to GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks."
Anguiano varshneya lmu_symposium_2015
Poster presented at the 7th Annual Loyola Marymount University Undergraduate Research Symposium on March 21, 2015. Title: Improvements to GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks.
Authors: Nicole A. Anguiano, Anindita Varshneya, Kam D. Dahlquist, John David N. Dionisio, and Ben G. Fitzpatrick.
Abstract: GRNsight is a web application and service for visualizing models of gene regulatory networks (GRN). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNs can be mathematically modeled by GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN. The general graph visualization tools that exist for displaying GRNs did not fulfill our needs, so we created GRNsight. GRNsight automatically lays out the network graph based on spreadsheets uploaded from GRNmap. It is written in JavaScript, with diagrams facilitated by D3.js. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized to support the specific needs of GRN visualization. GRNsight colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. GRNsight uses pointed and blunt arrowheads to indicate activation and repression, respectively. Visualizations can be modified through manual node dragging and sliders that adjust D3.js’s force layout parameters. Since version 1.0, GRNsight has acquired significant user interface improvements, bug fixes, extended robustness to errors in file formats, and aesthetic refinements, particularly for edge markers. Demo spreadsheets are now available for users who do not have their own models. GRNsight's current release is version 1.8, with beta version 1.9 available for testing new features. GRNsight is available at http://dondi.github.io/GRNsight/.
Varshneya samdarshi lmu_symposium_2016
Poster presented at the Loyola Marymount University 8th Annual Undergraduate Research Symposium on March 19, 2016 and at the Experimental Biology Conference held in San Diego, California on April 4, 2016. Abstract: GRNsight is an open source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN, can mathematically model the dynamics of GRNs. GRNsight automatically lays out the network graph based on GRNmap output spreadsheets. GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. Visualizations can be modified through manual node dragging and sliders that adjust the force graph parameters. We have now implemented an exhaustive unit testing framework using Mocha and the Chai assertion library to perform test-driven development where unit tests are written before new functionality is coded. This framework consists of over 160 automated unit tests that examine over 450 test files to ensure that the program is running as expected. Error and warning messages inform the user what happened, the source of the problem, and possible solutions. The completion of the testing framework marks the close of development for version 1 (the current release stands at version 1.12). In version 2.0 of GRNsight, a new feature will be implemented that colors the nodes (genes) based on expression data provided by GRNmap. GRNsight is available at http://dondi.github.io/GRNsight/.
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Presentation given by Britain Southwick for his CMSI 402 senior project at the Loyola Marymount University on May 8, 2014. It describes GRNsight: A Web Application for Visualizing Models of Gene Regulatory Networks, found on the web at http://dondi.github.io/GRNsight/index.html.
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Talk given at the Southern California Systems Biology Conference on January 31, 2015 entitled, "GRNmap and GRNsight: Open Source Software for Dynamical Systems Modeling and Visualization of Medium-Scale Gene Regulatory Networks".
Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. Parameters for a 21-gene network were optimized against DNA microarray data measuring the transcriptional response to cold shock in wild type and four transcription factor deletion strains of budding yeast, Saccharomyces cerevisiae. Model predictions fit experimental data well, within the 95% confidence interval. Open source code and a compiled executable that can run without a MATLAB license are available from <http: />. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. The GRNsight code and application are available from <http: />. This work was partially supported by NSF award 0921038.
Dahlquist experimental biology_20160404
Talk and poster presented at the American Society for Biochemistry and Molecular Biology/Experimental Biology Conference on April 4, 2016. Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. Parameters for a series of related networks, ranging in size from 15 to 35 genes, were optimized against DNA microarray data measuring the transcriptional response to cold shock in wild type and five strains individually deleted for the transcription factors, Cin5, Gln3, Hap4, Hmo1, Zap1, of budding yeast, Saccharomyces cerevisiae BY4741. Model predictions fit the experimental data well, within the 95% confidence interval. Open source code and a compiled executable that can run without a MATLAB license are available from http://kdahlquist.github.io/GRNmap/. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. The GRNsight open source code and application are available from http://dondi.github.io/GRNsight/index.html.
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Anguiano varshneya sccur-poster_20141122
Poster presented at the Southern California Conference for Undergraduate Research on November 22, 2014 by Nicole Anguiano and Anindita Varshneya entitled "Improvements to GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks."
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Poster presented at the 7th Annual Loyola Marymount University Undergraduate Research Symposium on March 21, 2015. Title: Improvements to GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks.
Authors: Nicole A. Anguiano, Anindita Varshneya, Kam D. Dahlquist, John David N. Dionisio, and Ben G. Fitzpatrick.
Abstract: GRNsight is a web application and service for visualizing models of gene regulatory networks (GRN). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNs can be mathematically modeled by GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN. The general graph visualization tools that exist for displaying GRNs did not fulfill our needs, so we created GRNsight. GRNsight automatically lays out the network graph based on spreadsheets uploaded from GRNmap. It is written in JavaScript, with diagrams facilitated by D3.js. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized to support the specific needs of GRN visualization. GRNsight colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. GRNsight uses pointed and blunt arrowheads to indicate activation and repression, respectively. Visualizations can be modified through manual node dragging and sliders that adjust D3.js’s force layout parameters. Since version 1.0, GRNsight has acquired significant user interface improvements, bug fixes, extended robustness to errors in file formats, and aesthetic refinements, particularly for edge markers. Demo spreadsheets are now available for users who do not have their own models. GRNsight's current release is version 1.8, with beta version 1.9 available for testing new features. GRNsight is available at http://dondi.github.io/GRNsight/.
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Presentation given at the Loyola Marymount University Undergraduate Research Symposium on March 29, 2014 by Britain Southwick and Nicole Anguiano. It describes GRNsight: A Web Application for Visualizing Models of Gene Regulatory Networks, found on the web at http://dondi.github.io/GRNsight/index.html.
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Talk given at the Southern California Systems Biology Conference on January 31, 2015 entitled, "GRNmap and GRNsight: Open Source Software for Dynamical Systems Modeling and Visualization of Medium-Scale Gene Regulatory Networks".
Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. Parameters for a 21-gene network were optimized against DNA microarray data measuring the transcriptional response to cold shock in wild type and four transcription factor deletion strains of budding yeast, Saccharomyces cerevisiae. Model predictions fit experimental data well, within the 95% confidence interval. Open source code and a compiled executable that can run without a MATLAB license are available from <http: />. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. The GRNsight code and application are available from <http: />. This work was partially supported by NSF award 0921038.
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An Improved PageRank Algorithm for Multilayer Networks
Highlighted notes on An Improved PageRank Algorithm for Multilayer Networks.
While doing research work under Prof. Kishore Kothapalli.
The authors use a layer-layer weight to provide an additional factor to PR which they then use in the 1st step of SpreadMax algorithm to show that their modified pagerank is better than standard PR in selecting key nodes. I didnt properly understand this.
Applications of networks:
Social networks, Air traffic systems, Transportation networks, Citation networks, Biological systems (infection spreading).
Multilayer networks can be thought of as a large graph subdivided into multiple groups (layers). This probably makes it easier to assign common weights to a particular group of nodes, edges.
SPARJA: a Distributed Social Graph Partitioning and Replication Middleware
This document describes SPARJA, a distributed social graph partitioning and replication middleware. SPARJA is based on SPAR but improves it in two key ways: 1) It uses a distributed partitioning algorithm that does not require a global view of the social graph; and 2) It eliminates the single point of failure of the central partition manager in SPAR. The document evaluates SPARJA against SPAR on both synthetic and real social graph datasets, finding that SPARJA performs on par with or better than SPAR depending on the graph structure and level of clustering.
Consistency in Distributed Systems
This document provides an overview of key concepts in distributed systems including:
1) The CAP theorem which states that a distributed system cannot simultaneously provide consistency, availability, and partition tolerance.
2) Consistency models such as strong, weak, and eventual consistency. Eventual consistency guarantees that if no new writes are made, reads will return the last updated value.
3) Consensus protocols like Paxos which allow distributed systems to agree on a value despite potential failures. Vector clocks are used to order events in a distributed system.
C055011012
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Analysis of grid log data with Affinity Propagation
In this paper we present an unsupervised learning approach to detect meaningful job traffic patterns in Grid log data. Manual anomaly detection on modern Grid environments is troublesome given their in- creasing complexity, the distributed, dynamic topology of the network and heterogeneity of the jobs being executed. The ability to automat- ically detect meaningful events with little or no human intervention is therefore desirable. We evaluate our method on a set of log data col- lected on the Grid. Since we lack a priori knowledge of patterns that can be detected and no labelled data is available, an unsupervised learning method is followed. We cluster jobs executed on the Grid using Affinity Propagation. We try to explain discovered clusters using representative features and we label them with the help of domain experts. Finally, as a further validation step, we construct a classifier for five of the detected clusters and we use it to predict the termination status of unseen jobs.
Lec 3.pptx
The document discusses different types of data sets that can be analyzed in data science. It describes record, graph and network, ordered, spatial, image and multimedia data. It then discusses key concepts related to data sets including data objects, attributes, attribute types (nominal, binary, numeric), and characteristics of data sets like dimensionality and sparsity. The document also lists some repositories for finding publicly available data sets and outlines strategies for getting data like being provided data, downloading data, or scraping data from the web. Finally, it introduces the topic of data visualization.
Extending Complex Event Processing to Graph-structured Information
Complex Event Processing (CEP) is a powerful technology in realtime distributed environments for analyzing fast and distributed streams of data, and deriving conclusions from them. CEP permits defining complex events based on the events produced by the incoming sources in order to identify complex meaningful circumstances and to respond to them as quickly as possible. However, in many situations the information that needs to be analyzed is not structured as a mere sequence of events, but as graphs of interconnected data that evolve over time. This paper proposes an extension of CEP systems that permits dealing with graph-structured information. Two case studies are used to validate the proposal and to compare its performance with traditional CEP systems. We discuss the benefits and limitations of the CEP extensions presented.
IEEE 2014 JAVA DATA MINING PROJECTS A similarity measure for text classificat...
IEEE 2014 JAVA DATA MINING PROJECTS A similarity measure for text classificat...IEEEFINALYEARSTUDENTPROJECTS
The document proposes a new similarity measure for text classification and clustering that considers three cases: when a feature appears in both documents, in one document, or in none. It evaluates the effectiveness of this measure on real-world data sets, finding it performs better than other measures. It also describes an existing system for document clustering that has disadvantages like dependency on initial random assignments and local rather than global minimum variance. The proposed system develops a hierarchical algorithm for more efficient and high-performing document clustering using a novel way to evaluate similarity between documents.What's hot (8)
An Improved PageRank Algorithm for Multilayer Networks
An Improved PageRank Algorithm for Multilayer Networks
SPARJA: a Distributed Social Graph Partitioning and Replication Middleware
SPARJA: a Distributed Social Graph Partitioning and Replication Middleware
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Analysis of grid log data with Affinity Propagation
Extending Complex Event Processing to Graph-structured Information
Extending Complex Event Processing to Graph-structured Information
IEEE 2014 JAVA DATA MINING PROJECTS A similarity measure for text classificat...
IEEE 2014 JAVA DATA MINING PROJECTS A similarity measure for text classificat...
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This document introduces GEOSHARE's cyberinfrastructure for sharing and analyzing geospatial data through HUBzero. GEOSHARE aims to provide a global, spatially explicit database on agriculture, land use, and the environment, as well as analysis tools for scientists, decision makers, and practitioners. Currently, GEOSHARE has developed a pilot network utilizing HUBzero technology to allow researchers to share models, data, and computational resources without additional software or learning new platforms. Examples include sharing a crop model and processing climate data through interactive web applications. Recent funding will expand HUBzero's spatial capabilities to enable more geospatial modeling and analysis to be conducted online.
2015 genome-center
This document discusses the challenges and opportunities biology faces with increasing data generation. It outlines four key points:
1) Research approaches for analyzing infinite genomic data streams, such as digital normalization which compresses data while retaining information.
2) The need for usable software and decentralized infrastructure to perform real-time, streaming data analysis.
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4) The lack of data analysis training in biology and efforts at UC Davis to address this through workshops and community building.
Better science through superior software
Presentation given to the BEACON 2013 Congress during the "Collaborating with Industry" sandbox
Original w/ slide notes at: https://docs.google.com/presentation/d/1mmvD0R3fLIl11TmFHij5fGcMDb9qJxy_nwENO2Rt-YI/edit?usp=sharing
NRNB Annual Report 2018
This document summarizes the accomplishments of the National Resource for Network Biology over a reporting period. It lists numerous quantitative metrics of success, including over 100 publications citing their grants, thousands of daily downloads and uses of their software tools, and training over 100 users. It also provides details on improvements and developments made to several of their modeling frameworks, algorithms, and software applications. Finally, it outlines the formation of a new working group on single-cell RNA-seq analysis and visualization, and improvements made to their computing infrastructure.
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
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The document provides an overview of the Global Research Platform (GRP), an international collaborative partnership creating a distributed environment for data-intensive global science. The GRP facilitates high-performance data gathering, analytics, transport up to terabits per second, computing, and storage to support large-scale global science cyberinfrastructure ecosystems. It aims to orchestrate research across multiple domains using international testbeds for investigating new technologies related to data-intensive science. Examples of instruments generating exabytes of data that would benefit include the Korea Superconducting Tokamak, the High Luminosity LHC, genomics, the SKA radio telescope, and the Vera Rubin Observatory.
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This document summarizes a workshop on teaching computational skills in the sciences using MATLAB. The workshop included strategies for teaching data analysis, modeling, and computation through domain-focused courses. Presenters provided teaching activities and resources for conveying these skills with MATLAB. Three professors demonstrated representative activities involving geophone layout simulation, building modular visualization tools, and principal component analysis. The workshop aimed to provide a community for peer educators to share resources and approaches for effectively teaching computational skills in science fields using MATLAB.
CI4CC sustainability-panel
This document summarizes a presentation about Globus Genomics, a service that provides genomic data analysis tools and workflows through a web interface. It allows users to securely transfer data, run standardized analysis pipelines, access computational resources on demand through Amazon Web Services, and collaborate on shared data and workflows. The service aims to make genomic analysis more accessible, reproducible, and sustainable through various pricing models and support for individual labs and bioinformatics cores.
NRNB Annual Report 2017
The document summarizes the accomplishments of the National Resource for Network Biology (NRNB) over the past year, including:
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Omics Logic - Bioinformatics 2.0
Excited to share our vision for bioinformatics education available for students and researchers that want to apply advanced multi-omics integration and machine learning to large biomedical datasets. Practice and learn from real-life projects.
EarthCube Monthly Community Webinar- Nov. 22, 2013
This webinar features project overviews of all EarthCube Awards (Building Blocks, Research Coordination Networks, Conceptual Designs, and Test Governance), followed by a call for involvement, and a Q&A session.
Agenda:
EarthCube Awards – Project Overviews
1.. EarthCube Web Services (Building Block)
2. EC3: Earth-Centered Community for Cyberinfrastructure (RCN)
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4. Specifying and Implementing ODSIP (Building Block)
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9. Earth System Bridge (Building Block)
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14. Enterprise Architecture for Transformative Research and Collaboration (CD)
15. EC Test Enterprise Governance: An Agile Approach (Test Governance)
A Call for Involvement!
Mohan C R CV
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Pine education-platform
This document discusses using the T-BioInfo platform to provide practical education in bioinformatics. It describes how the platform can integrate different types of omics data and analysis into intuitive, visual pipelines. This allows non-experts to analyze and interpret complex datasets. Example projects are provided, such as using RNA-seq data to identify genes involved in a disease. The goal is to teach bioinformatics through collaborative, project-based learning without requiring programming skills. Learners would reconstruct simulated biological processes and contribute to ongoing analysis of real scientific datasets.
Open Science
This document discusses open science and how NASA can accelerate and expand its scientific impact through more open practices. It defines open science as collaborative sharing of data, information and knowledge to accelerate research. It advocates for making the scientific process, data, software, and findings more accessible and transparent through practices like open data, open source software, open cyberinfrastructure, open access publications, and public communication. It acknowledges open science can be difficult but provides examples of how different groups are implementing more open approaches.
A
The document provides information on various genetic algorithm and evolutionary computation software tools that are available as shareware or freeware. It lists over 30 different programs, including their descriptions and developers. The tools cover a wide range of programming languages and applications, including genetic programming, genetic algorithms, and evolutionary strategies.
A study on cloud computing ppt n_24-12-2017
The document discusses how advances in high-throughput sequencing technologies and the exponential growth of biological data have posed challenges for data storage and analysis in bioinformatics. This has resulted in a growing gap between sequencing throughput and computer capabilities for handling large datasets. Cloud computing promises to address issues of big data storage and analysis in the bioinformatics field by providing vast computing resources and the ability to parallelize processing. The integration of computers, databases, and software tools in bioinformatics approaches allows for analysis of genomes and proteomes through approaches like genomics and proteomics.
Turning data into knowledge the impacts of bioinformatics
Scientists in ICRISAT’s Global Theme on Harnessing Biotechnology for the Poor employ a range of modern genomic technologies in their efforts to enhance the efficiency and effectiveness of crop improvement. A critical rate-limiting step in genomics is no longer data generation, but rather the speed at which data is captured, validated, analyzed and turned into useful knowledge.
Automated Fake News Detection -1.pptx
Logistic regression is proposed as a novel technique for automated fake news detection. The technique uses NLP processing on a fake news dataset to extract text features before applying logistic regression. The proposed system achieves 97.5% accuracy on test data, outperforming existing methods. It can detect fake news across different datasets and is deployed as a web application using Django and Heroku for online use.
Kuchinsky_Cytoscape_BOSC2009
This document discusses plans for a major re-architecture of the Cytoscape software platform in version 3.0. The goals are to make it easier for developers to write plugins by adopting modern frameworks like OSGi and Spring, and to support different deployment contexts. This will address current issues like a monolithic architecture that is difficult to extend. The re-architecture aims to improve modularity, enable better plugin interoperability, and facilitate use of Cytoscape in new environments like web applications. Challenges include balancing simplicity of the new API with plugin extensibility.
Usability, Reusability and Reproducibility of Bioinformatic Applications
Novel biotechnologies allow creating data in exascale dimension with relatively minor effort of human and laboratory and thus monetary resources compared to capabilities only a decade ago. While the availability of this data salvage to find answers for research questions, which would not have been feasible before, maybe even not feasible to ask before, the amount of data creates new challenges, which obviously need new software and data management systems. Such new solutions have to consider integrative approaches, which are not only considering the effectiveness and efficiency of data processing but improve usability, reusability and reproducibility especially tailored to the target user communities of biological data. Science gateways address such challenges and are intuitive graphical user interfaces offering a single point of entry to distributed job, workflow and/or data management across organizational boundaries. Their overall goal is to increase the usability of applications allowing users to focus on their specific research question instead of becoming acquainted with command line tools and diverse access mechanisms to infrastructures. The talk will give an overview on existing technologies, on current issues regarding reusability and reproducibility as well as on results of two user surveys. It will especially highlight key challenges and the characteristics cutting-edge developments should possess for fulfilling the needs of the user communities to allow for seamless data analysis on a large scale.
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The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
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With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
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Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
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Dahlquist bosc 20160709
- 1. GRNmap and GRNsight: Open Source Software for Dynamical Systems Modeling and Visualization of Medium-Scale Gene Regulatory Networks Kam D. Dahlquist, Ph.D. Department of Biology Loyola Marymount University July 9, 2016 Bioinformatics Open Source Conference (BOSC)
- 2. Open Science (open process) Citizen Science Open Source Code Open Access (creative commons) Reproducible Research Research Integrity Open Science Ecosystem Open DataOpen Pedagogy With thanks to John Jungck
- 3. HHMI Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Science Program http://seaphages.org/ BioQUEST Curriculum Consortium 30th Anniversary this year! http://www.bioquest.org The Genome Consortium for Active Teaching NextGen Sequencing Group http://gcat-seek.weebly.com/ Students Benefit from Open Source and Open Data
- 4. GRNsight: http://dondi.github.io/GRNsight/ GRNmap: http://kdahlquist.github.io/GRNmap/ Back row (left to right) Brandon Klein Mihir Samdarshi Kevin McGee Kevin Wyllie K. Grace Johnson Kristen Horstmann Tessa Morris Front row (left to right) Maggie O’Neil Monica Hong Kam Dahlquist Anindita Varshneya Kayla Jackson Not pictured John David N. Dionisio Ben G. Fitzpatrick Nicole Anguiano Juan Carrillo Trixie Anne Roque Chukwuemeka Azinge Funding: NSF RUI, Kadner-Pitts Research Grant, LMU SURP, LMU Honors Program, LMU Rains Research Assistant Program
- 5. Cold shock microarray data from wt and TF deletion strains Systems Biology Approach to Understanding the Regulation of the Cold Shock Response in Yeast Normalization, statistical analysis, clustering Derivation of gene regulatory networks from YEASTRACT Dynamical systems modeling using GRNmap Visualization of modeling results using GRNsight Interpretation, new questions, new experiments 0 0.5 1 Activation 1/w 0 0.5 1 Repression 1/w Dash1 15°C wt
- 6. GRNmap: Gene Regulatory Network Modeling and Parameter Estimation Weight parameter, w, gives the direction (activation or repression) and magnitude of regulatory relationship. 0 0.5 1 Activation 1/w 0 0.5 1 Repression 1/w )( )(exp1 )( txd btxw P dt tdx ii j ijij ii
- 7. GRNsight is written in JavaScript, customizing the D3.js library. Node.js and the Express framework handle server-side functions.
- 8. Users Click and Drag to Customize Layout, Mouse-over Edge Displays Value of Weight Parameter
- 9. A Tale of Two Open Source Projects. . . GRNmap • Developed in MATLAB by successive math students over nearly a decade • Shifted to open development on GitHub in the last two years • Free executable now available • Still in the midst of software refactoring and “paying off our technical debt” • New features arise through interplay between student “coding” and “data analysis” teams • Goal is reproducible research GRNsight • Why “yet another graph layout tool”? • Specific use-case of displaying output from GRNmap directly in a web application • Reduced learning curve for student users • Do one thing well • Teach software engineering best practices while creating a useful tool • New features arise through interplay with GRNmap team, leverage other open source tools
- 10. Summary • GRNmap models the dynamics of “medium-scale” gene regulatory networks using differential equations. • A penalized least squares approach was used successfully to estimate parameters from yeast cold shock microarray data. • Can be used with time course gene expression data from any species. • GRNsight automatically generates weighted network graphs from the spreadsheets produced by GRNmap. • This facilitates visualization of the relative influence of each transcription factor in controlling the cold shock response. • Can be used to visualize any small- to medium-scale network in adjacency matrix format (< 35 nodes, < 70 edges). • Challenges in bridging the cultures of mathematics and computing were overcome. • Shifting a longstanding project to open development and software engineering best practices, versus... • Building an open source project with test-driven development, “standing on the shoulders of giants”.