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    Education: Training course: Introduction to Machine Learning
    Submitted by Carlo Pecoraro; posted on Thursday, April 18, 2019


    June 3-7, 2019
    BGBM/ Freie Universität Berlin, Königin-Luise-Straße 6-8, 14195 Berlin[...]se43/

    Registration deadline: May 4, 2019
    Instructor: Prof. Paolo Frasconi (University of Florence, Italy;


    This workshop is aimed to students and researchers aiming to understand the basic principles of machine learning. It will focus on supervised learning, starting with linear models (regression, logistic regression, support vector machines) and will extend to the basic technologies of deep learning and kernel methods for vector data, signals, and structured data. Basic principles of learning theory that are useful to analyze results of practical applications will be also covered. Finally, there will be practical sessions using scikit-learn, TensorFlow, and Keras. After completing the workshop, students should able to understand the most popular learning algorithms, to apply them to solve simple practical problems, and to analyze and interpret the results. All course materials (including copies of presentations, practical exercises, data files, and example scripts prepared by the instructing team) will be provided electronically to participants.


    This workshop is aimed at all researchers and technical workers with a background in biology, computer science, mathematics, physics or related disciplines who want to understand and apply supervised machine learning algorithms to practical problems. The syllabus has been planned for people with zero or very basic knowledge of machine learning.

    Students are assumed to know calculus, linear algebra, and algorithms and data structures at the undergraduate level. Students should also have sufficient programming skills, and preferably previous knowledge of the Python programming language.


    Session content:[...]um43/
    For more information about the course, please visit our website:[...]se43/
    Here is the full list of our courses and Workshops:


    We developed an in silico tool NeuroPIpred for predicting and designing insect neuropeptides from natural and modified peptides.

    Paper URL:[...]538-x
    Web server :[...]pred/



    Scientists at ETH Zurich have created the first fully computer-generated genome of a living organism. The brand new genome, named Caulobacter ethensis-2.0, was built by essentially cleaning up and simplifying the natural code of a bacterium called Caulobacter crescentus. For now it exists as one large DNA molecule and not a living organism itself, but the team says this is a huge step towards creating completely synthetic life and medicinal DNA molecules.

    May 30-31, 2019
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD 20892, USA


    Participants will create and access cloud instances (renting and using a windows or linux machine), set security, configure storage, create snapshots, create images, up or down scaling the resources, monitoring resources, billing and perform routine tasks.


    Cloud offers computers with one to hundreds of cores and megabytes to terabytes of memory, on a hourly basis, from pennies to dollars and on-demand, for anyone who can use a computer and internet. This democratizes the high performance computing that everybody can use.

    Hands-on Skills/Tools Taught:
    • Cloud platform: Amazon Web Services (AWS)
    • Cloud platform: Google Cloud Platform
    • Cloud platform: Azure – Microsoft Cloud Platform
    • Computing: AWS Elastic Cloud Compute (EC2)
    • Computing: Secure Shell, Secure File Transfer (SSH/SFTP)
    • Computing: AWS Identity and Access Management (IAM)
    • Computing: AWS CPU, GPU, Cluster
    • Standards: GovCloud
    • Pricing: On-demand, spot, reserved
    • Storage: AWS Simple Storage Service (S3)
    • Storage: AWS Elastic Block Store (EBS)
    • Storage: AWS Volumes, Snapshots, Amazon Machine Images (AMI)
    • BigData: Hadoop, MapReduce, DynamoDB, Serverless computing
    • Monitoring: CloudWatch
    • Marketplace: MolBioCloud


    • Participants will be provided with step-by-step walk through instructions to setup, configure, secure, monitor and access computing resources rented in public cloud infrastructures.
    • Training provided by active NIH researchers
    • Cookbook style manual for all exercises
    • Direct, after training support through exclusive forum membership
    Books: Human genome textbook
    Submitted by Tore Samuelsson; posted on Wednesday, March 13, 2019


    A textbook with the title "The Human Genome in Health and Disease: A Story of Four Letters" by Tore Samuelsson was recently published by Garland Science / CRC Press. The book explores the intimate link between sequence information and biological function. A range of sequence-based functional units of the genome are discussed and illustrated with inherited disorders and cancer. One chapter is dedicated to sequence bioinformatics.

    The human genome is a linear sequence of roughly 3 billion bases and information regarding this genome is accumulating at an astonishing rate. Inspired by these advances, The Human Genome in Health and Disease: A Story of Four Letters explores the intimate link between sequence information and biological function. A range of sequence-based functional units of the genome are discussed and illustrated with inherited disorders and cancer. In addition, the book considers valuable medical applications related to human genome sequencing, such as gene therapy methods and the identification of causative mutations in rare genetic disorders.

    The primary audiences of the book are students of genetics, biology, medicine, molecular biology and bioinformatics. Richly illustrated with review questions provided for each chapter, the book helps students without previous studies of genetics and molecular biology. It may also be of benefit for advanced non-academics, which in the era of personal genomics, want to learn more about their genome.

    AVAILABILITY[...]5917/ and


    August 12-16, 2019 (Monday-Friday)
    University of Washington Campus, Seattle, WA, USA[...]n.pdf

    The University of Washington Center for Mendelian Genomics (UW-CMG) offers is offering a weeklong course on the analysis of next generation sequence data that will focus on strategies and tools for solving rare Mendelian disorders. The course is intended for data analysts and researchers who analyze next‐generation sequence data.

    We are currently accepting applications for this year's course

    Lunch and snacks provided, on-campus housing available.

    A laptop and specific software are required for participation.

    The course will consist of lectures and hands-on exercises designed to help reinforce the lecture content.

    Monday: IT infrastructure, pipeline overview, CMG data, VCF files and Unix basics
    Tuesday: QC, study design & modes of analysis
    Wednesday: Annotation, interpreting CNV results, introduction to Gemini, and analysis using Gemini for de novo model
    Thursday: Analysis using Gemini for autosomal recessive and autosomal dominant models, candidate genes and variants, and hands-on exercises
    Friday: Team and challenge projects using real-world data, presentations, wrap up, additional questions, and feedback

    For more information and to download a workshop flyer and application, please see the PDF.
    Resources: New NCI Data Science and Informatics Website
    Submitted by Jordan; posted on Thursday, March 07, 2019

    The National Cancer Institute's (NCI) Center for Biomedical Informatics and Information Technology (CBIIT) is excited to announce the launch of, a new website for data science and informatics resources!

    Replacing CBIIT's former public site, has a wide variety of resources, including: data sets, data sharing policies and guidance, NCI Cloud Resources, and more.

    Bookmark the new website and follow NCI CBIIT on Twitter @NCIDataSci and on the NCI Cancer Data Science LinkedIn group for the latest NCI data science news and updates.

    April 23-26, 2019
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD 20892, USA


    Participants will get a brief introduction to the programming concepts, followed by hands-on walkthrough for writing scripts using the Unix Shell Programming Language, R, Perl and Python. We will start from reading the data, processing it and all the way until saving the processed data. This training will walk through participants in writing programs that would help them solve their own problems.


    Computer programs are meant to perform repeated, monotonous, fast, reproducible tasks, handling any amount of data. Researchers often come across situations where existingprograms don't suit their needs. In the era of BigData, without the ability to quickly put together aprogram that would solve their problem, researchers face a road block that is not efficientlysolvable by a human.This training will walk through participants in writing programs that wouldhelp them solve their own problems.


    • Participants will work in a Unix environment.
    • Participants will get a copy of all the scripts used in the class.
    • Participants will also receive a cookbook style manual for all the hands-on exercises.
    • After training support is also provided through exclusive members only forum.


    ccPDB 2.0 ( is an updated version of the manually curated database ccPDB that maintains datasets required for developing methods to predict the structure and function of proteins. The number of datasets compiled from literature increased from 45 to 141 in ccPDB 2.0. Similarly, the number of protein structures used for creating datasets also increased from ~74 000 to ~137 000 (PDB March 2018 release).


    URL of resource:


    Paper URL:[...]3045/
    Education: Training in Computational Drug Design and Discovery @ NIH
    Submitted by Vijayaraj Nagarajan; posted on Thursday, February 07, 2019

    April 9-12, 2019
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD 20892, USA


    This hands-on training will introduce researchers to the concepts, methods and tools for structure and ligand based computational drug designing and discovery using the open source tools and the cloud computing facilities.


    Computational drug design and discovery has been a challenging task due to limitations in available computing resources. Public cloud computing facilities have dramatically changed this scenario, by bringing the most powerful computing systems within a click away, with unprecedented low cost options.


    • Ligand preparation: OpenBabel
    • Target preparation: Chimera
    • Databases: ZINC, PubChem, ChemSpider, ChEMBL, DrugBank, Binding DB
    • Docking: AutoDockTools
    • Visualization: Chimera, PyMOL
    • Structure based virtual screening: AutoDock, Dock
    • Ligand drawing & visualization: Chemdraw, Chemsketch
    • Compound library: ChemT (or DRUGSTER or FSees)
    • Descriptors: Mold2
    • QSAR: Open3DQSAR (or TEST or Coral)
    • Pharmacophore exploration: Open3DQSAR (or DRUGON or pharmacophore)
    • Ligand alignment: LIGSIFT
    • Ligand based virtual screening: LIGSIFT (or MOLA or lisica)
    • ADMET, Toxicity estimation: Toxtree (or DataWarrior)


    • Cloud-based, high performance computing platform
    • Cloud image freely provided to participants
    • Training provided by active NIH researchers
    • Cookbook style bound manual for all exercises
    • Direct, after training support through exclusive forum membership
    • Continuing Educational Credits
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