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Condition:
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    Healthcare is generating and collecting huge amounts of data from electronic health record systems, medical imaging, lab data and genomics testing. Tools and methods are currently being developed to improve our ability to analyze and understand this abundant amount of health data. Through the correct application of tools and methods, we can improve patient outcomes and reduce costs. Professionals with this ability are increasingly in high demand.

    The mission of Georgetown University's Health Informatics and Data Science program (https://healthinformatics.georgetown.edu) is to equip our students with cutting-edge techniques, to give them a firm grasp on methodologies and to be highly qualified for the market. It is designed to help students develop core competencies in data science, health-related data, predictive analytics, machine learning (ML), Artificial Intelligence (AI) and other advanced technologies. Through the program, students will be prepared to successfully take on a wide range of opportunities in health care organizations and related industries including, health technology developers, device manufacturers, pharma/biotech, academic medical centers and management consulting firms. We help our students build critical skills for this growing sector to lead the evolution of healthcare.

    Through multi-disciplinary coursework and projects, HIDS students will be exposed to, and interact with, field experts, peers and faculty from top-ranked Georgetown University departments. Our unique Georgetown experience prepares the next generation of health informaticians to lead and make a difference in patient care and population health.

    Make your career move now! Come talk to us about how Georgetown's Health Informatics & Data Science fits into your academic and professional goals! Please visit https://bit.ly/3qqHB2r.

    Georgetown-HIDS is a proud member of the AI/ML Consortium to Advance Health Equity & Researcher Diversity (AIM-AHEAD) Program (https://datascience.nih.gov/artificial-intelligence/aim-ahead).

    June 13-15, 2022
    Hotel Le Méridien, 20 Sidney St, Cambridge, MA, USA
    https://www.biscglobal.com/training/bioinformatics-biostatistics-and-machine-learning-techniques-for-biotech-applications/

    We are pleased to announce that the registration for our training "Bioinformatics, Biostatistics and Machine Learning Techniques for Biotech Applications" has been opened.

    If you want to learn about the latest techniques and applications of bioinformatics, statistics and ML in life science, you can find out more and register on the website listed above.

    During this 3-day hands-on training in Boston you will learn about:
    • NGS data analysis (Day 1)
    • Introduction to Biostatistics of Clinical Trials (Day 2)
    • Machine Learning (Day 3)
    Takeaway: You will learn about the most in-demand bioinformatics, biostatistics and machine learning techniques you need to succeed as a bioinformatician or data scientist in the biotech industry. For each pipeline and model, you will learn how it works conceptually first, then apply it to a particular industrial application, and finally learn to analyze and visualize the results.

    Registration: Secure your spot now. Seats are limited, and we accept applicants on a first-come, first-served basis.

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    There's a lot to be impressed about when considering a career in bioinformatics.

    First, it's one of the most multidisciplinary fields of science, merging not just computing and biology but also drawing on methods from mathematics, engineering, physics and chemistry.

    Second, bioinformaticians are at the bleeding edge in applied computing, making use of artificial intelligence, Big Data and cloud computing to treat diseases and advance our understanding of life. In fact, many of the most powerful supercomputers in the world are used to solve bioinformatics problems.

    In the future, bioinformatics will bring personalized healthcare through the rapid and inexpensive analysis of a patient's whole genome, which can be ordered at their doctor's office. The future will also bring new therapies designed by artificial intelligence, cutting years off of development time.

    At the University of Birmingham you can be on your way to such an exciting and impactful career. Their flexible, 100% Online MSc Bioinformatics connects biology, clinical services, mathematics, and computer science, giving students the knowledge they need to respond to growing healthcare challenges.

    The university is now accepting Summer 2022 applications. Request more information to find out how to apply (https://bit.ly/34rUQIy).

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    Antibodies, small proteins produced by the immune system, can attach to specific parts of a virus to neutralize it. As scientists continue to battle SARS-CoV-2, the virus that causes Covid-19, one possible weapon is a synthetic antibody that binds with the virus' spike proteins to prevent the virus from entering a human cell.

    To develop a successful synthetic antibody, researchers must understand exactly how that attachment will happen. Proteins, with lumpy 3D structures containing many folds, can stick together in millions of combinations, so finding the right protein complex among almost countless candidates is extremely time-consuming.

    To streamline the process, MIT researchers created a machine-learning model that can directly predict the complex that will form when two proteins bind together. Their technique is between 80 and 500 times faster than state-of-the-art software methods, and often predicts protein structures that are closer to actual structures that have been observed experimentally.
    Source: https://news.mit.edu/2022/ai-predicts-protein-docking-0201
    Education: Be part of bioscience advancements with the University of Birmingham's Online MSc Bioinformatics (Sponsored Content)
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    A new bioinformatics software and cloud computing approach developed at the University of Birmingham has enabled the UK's COVID-19 genome sequencing effort to be the most sophisticated in the world.

    CLIMB-COVID was designed for the COVID-19 Genomics UK (COG-UK) consortium, set up in March 2020 to tackle the huge challenge of rapidly sequencing SARS-CoV-2 genomes.

    The first version of CLIMB-COVID was designed and built by researchers at the University of Birmingham and Cardiff University in under a month and it has been crucial in processing the sequencing data of more than 675,000 coronavirus genomes, including identifying and tracking the Alpha and Delta variants that became dominant in the UK last year. The collaborative approach also integrates new software developed at the University of Edinburgh and the Centre for Genomic Pathogen Surveillance.

    The CLIMB-COVID system enables a distributed sequencing system which harnesses sequencing capability from universities, academic institutes and the UK's four public health agencies. The software and database infrastructure was able to receive all this data, process it and analyse it into interpretable outputs for public health analysts, helping to make the UK a world leader in sequencing the Coronavirus genome.

    All the data from the project has been integrated and shared in real-time. Not only has this enabled the UK's public health agencies to work together more effectively, but it also allowed seamless access and collaboration across academics, thereby helping to create and advance systems for the early detection and evaluation of new variants of the virus.

    You can be a part of the continuing advancements in bioscience at the University of Birmingham through the Online MSc Bioinformatics offered by the university (https://bit.ly/3GsI4GJ). The postgraduate programme has three intakes per year – request more information to find out how to apply.

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    The game was created to boost worldwide research efforts that depend on cancer cell lines, a critical resource used by scientists to study cancer and test new drugs to treat the disease. One of the limitations of cancer cell lines are a lack of high-resolution genome reference maps, which are necessary to help researchers interpret their scientific results, for example pinpointing the location of genes of therapeutic interest or potential mutation sites.
    Source: https://scitechdaily.com/videogame-players-needed-to-solve-puzzles-and-help-advance-cancer-research/

    Game website: https://genigmagame.app/en/

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    Using the Nanoscale Molecular Dynamics, or NAMD, code on Summit, the nation's most powerful supercomputer, the researchers simulated the spike proteins' molecular structures for SARS-CoV-2 and three other human coronaviruses: SARS-CoV-1, MERS-CoV and HCoV-HKU1. After completing this unique and comprehensive comparison of four different spike proteins, they compared the components and behavior of SARS-CoV-2 with thousands of sample structures from the other viruses using a deep learning architecture called a convolutional variational autoencoder, or CVAE.

    These efforts revealed previously unexplored regions of the coronavirus's spike protein in which targeted medical intervention might prevent SARS-CoV-2 from infecting healthy cells.
    Source: https://www.ornl.gov/news/supercomputing-exposes-potential-pathways-inhibiting-covid-19
    Software: Evolutionary conservation of key amino acids more easily seen
    Submitted by Eric Martz; posted on Tuesday, January 25, 2022


    FirstGlance in Jmol http://bioinformatics.org/firstglance (free and open source as always) provides guided visual exploration of macromolecular structures with remarkable ease of use (no command language needed). The "FirstGlance" it offers is maximally informative. It alerts you automatically to missing residues (quite common in X-ray crystallographic models), incomplete sidechains, and unusual components or features including unusual covalent protein crosslinks (such as thioester or lysine-cysteine NOS bonds).

    Since 2006, FirstGlance has provided visualization of evolutionary conservation, calculated by the ConSurf Server http://consurf.tau.ac.il, coloring each amino acid in a protein structure by conservation level. All of ConSurf's state-of-the-art algorithms are published in peer-reviewed journals. Using ConSurf is easy. It gathers sequences, aligns them into a multiple protein sequence alignment (MSA), calculates conservation values, and colors your protein structure – all automatically. When the MSA provides inadequate information for a particular residue (large confidence interval), it is colored yellow for "insufficient data".

    This new version of FirstGlance optimizes zooming in on key residues and coloring them by evolutionary conservation. "One click (or a few)" tools do this for any residues that you specify, disulfide bonds, six other kinds of covalent protein crosslinks (e.g. isopeptide bonds), all salt bridges, all cation-pi interactions, and residues contacting any ligand or other substructure that you specify. You can toggle between coloring the atoms by chemical element or evolutionary conservation, provided that the protein structure model (PDB file) has been processed by ConSurf beforehand.

    Quick example for isopeptide and thioester crosslinks in FirstGlance: Here are some illustrated practical guides: Feedback always welcome!

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    The OneZoom explorer – available at https://onezoom.org – maps the connections between 2.2 million living species, the closest thing yet to a single view of all species known to science.

    The interactive tree of life allows users to zoom in to any species and explore its relationships with others, in a seamless visualisation on a single web page. The explorer also includes images of over 85,000 species, plus, where known, their vulnerability to extinction.
    Source: https://www.imperial.ac.uk/news/232634/visually-stunning-tree-known-life-unveiled/
    Resources: Computational resources in health care
    Submitted by Gajendra P.S. Raghava; posted on Wednesday, November 17, 2021

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    We are pleased to share our list of computational resources in health care. It covers a wide range of informatics-based fields like chemoinformatics, medical informatics, pharmacoinformatics and bioinformatics. The paper and server are available from the following links.

    Paper link: https://doi.org/10.1002/widm.1437
    Web server: https://webs.iiitd.edu.in
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    Acknowledgments

    We wish to thank the following for their support:

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