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In a new Nature Communications article, researchers describe a genomic language model (gLM) capable of predicting protein co-regulation and function. The gLM, which was trained on millions of metagenomic scaffolds, has learned to interpret the language of genes, revealing the intricate dance of protein regulation and enzymatic functions. This approach is a leap forward in our understanding of the genomic blueprint and its regulatory networks.
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https://Proteopedia.Org needs a major upgrade. Proteopedia.org (free, open source) was created in 2007 as a wiki encyclopedia of protein molecular structure incorporating JSmol (http://Jmol.Org). It is by far the easiest place to create customized interactive molecular scenes, which are then immediately online for anyone to see. "Green links" illustrate statements in text with interactive molecular scenes in JSmol. Proteopedia's pages have been visited >230 million times. Users have authored thousands of articles, >100 of which have been visited >50,000 times each. Professors and students are major users, as well as researchers. Over 5,000 people have set up free accounts. A brief introductory overview of Proteopedia is here:
https://proteopedia.org/w/Proteopedia:Overview
Rejuvenation will involve upgrading the obsolete versions of linux, MySQL, and Wikimedia that it now runs with on Amazon Web Services, and making all its customized features work with the new infrastructure. Those features include support to run JSmol within Wikimedia, the Scene Authoring Tools (https://www.youtube.com/watch?v=90jonYOzzCY), green links, etc. Then the content in the present implementation could be imported into the new system using an automated script that would also need to be developed. Jaime Prilusky, who created most of the present system and has maintained it until now, would very much like help from someone familiar with the components of the infrastructure, especially current MySQL. Limited funding may be available.
Thanks from the Proteopedia Team, https://proteopedia.org/w/Proteopedia:Team
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The ARC Opportunity is seeking innovative approaches to tackle the challenge of the "Unknome" – a collection of genes that have been overlooked in research for over 20 years. These genes are difficult to study and often lack funding, leading to a research gap. The complexity of cellular processes and the labor-intensive nature of generating relevant datasets have hindered the development of models that can predict how genes affect cell behavior. The initiative is calling for high-throughput methods to accurately annotate these unknown genes, which could significantly advance our understanding of genetics.
https://www.darpa.mil/ARC/DUF
https://www.youtube.com/watch?v=AFOPZw8qAJc
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September 16-18, 2024
Dipartimento di Informatica, Pisa, Italy
https://biomedinfo.di.unipi.it/cmsb2024/
CMSB 2024 solicits original research articles, tool papers, posters, and highlight talks on the modelling and analysis of biological systems and networks, as well as the analysis of biological data. The conference brings together researchers from across biological, mathematical, computational, and physical sciences who are interested in the modelling, simulation, analysis, inference, design, and control of biological systems. It covers the broad field of computational methods and tools in systems and synthetic biology and their applications.
TOPICS
Topics of interest include, but are not limited to:
- Formalisms for modelling biological processes
- Methods and tools for biological system analysis, modelling and simulation
- Frameworks for model verification, validation, analysis, and simulation of biological systems
- High-performance methods for computational systems biology
- Identification of biological systems
- Applications of machine learning and data analytics in biology
- Network modelling, analysis, inference
- Automated parameter and model synthesis
- Model integration and biological databases
- Multi-scale modelling and analysis methods
- Design, analysis, and verification methods for synthetic biology
- Methods for biomolecular computing and engineered molecular devices
- Data-based approaches for systems and synthetic biology
- Optimality and control of biological systems
- Modelling, analysis and control of microbial communities
The CMSB 2024 proceedings will be published in the Springer LNCS/LNBI series and indexed by ISI Web of Science, Scopus, ACM Digital Library, DBLP, and Google Scholar.
Conference organizers:
Roberta Gori, University of Pisa (Italy)
Paolo Milazzo, University of Pisa (Italy)
Mirco Tribastone, IMT School for Advanced Studies Lucca (Italy)
Invited speakers:
Juliana Bowles, University of St Andrews, (UK)
Madalena Chaves, INRIA, Centre Inria d'Université Côte d'Azur (France)
Karoline Faust, KU Leuven (Belgium)
Corrado Priami, University of Pisa (Italy)
IMPORTANT DATES
Abstract submission (regular/tool papers): April 14, 2024
Paper submission (regular/tool papers): April 21, 2024
Notification: June 9, 2024
Camera ready: June 23, 2024
Poster/highlight talk: July 9, 2024
Conference: September 16-18, 2024
CONTACT
All questions about the conference should be emailed to cmsb2024[at]easychair.org.
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March 4-8, 2024
Live online (synchronous), max 18 participants
Sessions from Monday to Friday, 13:00 to 17:00 (Madrid time zone)
https://www.transmittingscience.com/courses/statistics-and-bioinformatics/introduction-bayesian-inference-practice/
INSTRUCTORS
Dr. Daniele Silvestro (University of Gothenburg, Sweden) and Tobias Andermann (University of Gothenburg, Sweden)
COURSE OVERVIEW
This course is based on the assumption that the easiest way to understand the principles of Bayesian inference and the functioning of the main algorithms is to implement these methods yourself.
The instructors will outline the relevant concepts and basic theory, but the focus of the course will be to learn how to do Bayesian inference in practice. He will show how to implement the most common algorithms to estimate parameters based on posterior probabilities, such as Markov Chain Monte Carlo samplers, and how to build hierarchical models.
He will also touch upon hypothesis testing using Bayes factors and Bayesian variable selection.
The course will take a learn-by-doing approach, in which participants will implement their own MCMCs using R or Python (templates for both languages will be provided).
After completion of the course, the participants will have gained a better understanding of how the main Bayesian methods are implemented in many programs used in biological research work. Participants will also learn how to model at least basic problems using Bayesian statistics and how to implement the necessary algorithms to solve them.
Participants are expected to have some knowledge of R or Python (each can choose their preferred language), but they will be guided "line-by-line" in writing their script. The aim is that, by the end of the week, each participant will have written their own MCMC – from scratch! Participants are encouraged to bring their own datasets and questions and we will (try to) figure them out during the course and implement scripts to analyze them in a Bayesian framework.
More information and registration at https://www.transmittingscience.com/courses/statistics-and-bioinformatics/introduction-bayesian-inference-practice/ or writing courses[at]transmittingscience.com
Best regards,
Sole
Soledad De Esteban-Trivigno
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July 15, 2024 - July 18, 2024
Shanghai, China
https://ieeeaitest.com/wp-content/uploads/2023/12/CFP-6th-IEEE-AITest-2024.pdf
CALL FOR PAPERS
Artificial Intelligence (AI) technologies are widely used in computer applications to perform tasks such as monitoring, forecasting, recommending, predicting, and statistical reporting. They are deployed in a variety of systems, including driverless vehicles, robot-controlled warehouses, financial forecasting applications, and security enforcement and are increasingly integrated with cloud/fog/edge computing, big data analytics, robotics, Internet-of-Things (IoT), mobile computing, smart cities, smart homes, intelligent healthcare, and many more. Despite this dramatic progress, the quality assurance of existing AI application development processes is still far from satisfactory, and the demand for demonstrable levels of confidence in such systems is growing. Software testing is a fundamental, effective, and recognized quality assurance method which has shown its cost-effectiveness to ensure the reliability of many complex software systems. However, the adaptation of software testing to the peculiarities of AI applications remains largely unexplored and needs extensive research to be performed. On the other hand, the availability of AI technologies provides an exciting opportunity to improve existing software testing processes, and recent years have shown that machine learning, data mining, knowledge representation, constraint optimization, planning, scheduling, multi-agent systems, etc. have real potential to positively impact software testing. Recent years have seen a rapid growth of interest in testing AI applications as well as the application of AI techniques to software testing. This conference provides an international forum for researchers and practitioners to exchange novel research results, articulate the problems and challenges from practices, deepen our understanding of the subject area with new theories, methodologies, techniques, process models, impacts, etc., and improve the practices with new tools and resources.
IMPORTANT DATES
Abstract Registration Due: March 8, 2024
Submission Deadline: April 1, 2024
Notification Due: June 1, 2024
Final Version Due: June 15, 2024
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| Acknowledgments |
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We wish to thank the following for their support:
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