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    <description>recent bookmarks from amy</description>
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    <title>sandbox.bio</title>
    <dc:date>2024-01-16T17:44:07+00:00</dc:date>
    <link>https://sandbox.bio/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Learn bioinformatics from your browser.
Everything runs in a sandbox, so you can experiment all you want.]]></description>
<dc:subject>bioinformatics programming tools biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:05eca588eaf8/</dc:identifier>
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<item rdf:about="https://www.biorxiv.org/content/10.1101/2023.07.05.547496v1.abstract">
    <title>xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein | bioRxiv</title>
    <dc:date>2023-07-11T14:41:19+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/2023.07.05.547496v1.abstract</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Protein language models have shown remarkable success in learning biological information from protein sequences. However, most existing models are limited by either autoencoding or autoregressive pre-training objectives, which makes them struggle to handle protein understanding and generation tasks concurrently. This paper proposes a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework. Our key technical contribution is an exploration of the compatibility and the potential for joint optimization of the two types of objectives, which has led to a strategy for training xTrimoPGLM at an unprecedented scale of 100 billion parameters and 1 trillion training tokens. Our extensive experiments reveal that xTrimoPGLM significantly outperforms other advanced baselines in diverse protein understanding tasks (13 out of 15 tasks across four categories) and generates novel protein sequences which are structurally similar to natural ones. Furthermore, using the same xTrimoPGLM framework, we train an antibody-specific model (xTrimoPGLM-Ab) using 1 billion parameters. This model set a new record in predicting antibody naturalness and structures, both essential to the field of antibody-based drug design, and demonstrated a significantly faster inference speed than AlphaFold2. These results highlight the substantial capability and versatility of xTrimoPGLM in understanding and generating protein sequences.]]></description>
<dc:subject>bioinformatics omics verily</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:f9c04bbfcee5/</dc:identifier>
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<item rdf:about="https://github.com/DataBiosphere/tanagra">
    <title>DataBiosphere/tanagra: Repo for the Tanagra service being developed by the All of Us DRC</title>
    <dc:date>2022-09-08T15:01:22+00:00</dc:date>
    <link>https://github.com/DataBiosphere/tanagra</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Repo for the Tanagra service being developed by the All of Us DRC

]]></description>
<dc:subject>verily terra bioinformatics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:b0d25f7fc426/</dc:identifier>
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<item rdf:about="https://www.helix.com/pages/life-sciences">
    <title>Helix - Life Sciences</title>
    <dc:date>2021-12-12T23:39:45+00:00</dc:date>
    <link>https://www.helix.com/pages/life-sciences</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Helix provides a scalable genomics research platform to conduct large-scale genetic analyses on deeply phenotyped, recontactable individuals. Our proprietary clinico-genomic database of Exome+® sequencing data and deep phenotypic data can be used to accelerate efforts across the drug development lifecycle from discovery to commercialization.]]></description>
<dc:subject>bioinformatics verily</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:275f9dd254b9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:verily"/>
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<item rdf:about="https://dockstore.org/">
    <title>Dockstore</title>
    <dc:date>2021-07-12T17:42:27+00:00</dc:date>
    <link>https://dockstore.org/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Dockstore, developed by the Cancer Genome Collaboratory, is an open platform used by the GA4GH for sharing Docker-based tools described with either the Common Workflow Language (CWL), the Workflow Description Language (WDL), Nextflow, or Galaxy]]></description>
<dc:subject>bioinformatics computing docker genomics terra</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:90e9d72f5a53/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:computing"/>
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<item rdf:about="https://github.com/googlegenomics">
    <title>Google Genomics</title>
    <dc:date>2021-07-08T23:40:01+00:00</dc:date>
    <link>https://github.com/googlegenomics</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Tools for working with the Google Genomics API
]]></description>
<dc:subject>bioinformatics genomics google gcp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:1b089d8b7bac/</dc:identifier>
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<item rdf:about="https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology">
    <title>AlphaFold: a solution to a 50-year-old grand challenge in biology | DeepMind</title>
    <dc:date>2020-11-30T16:49:28+00:00</dc:date>
    <link>https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology</link>
    <dc:creator>amy</dc:creator><dc:subject>bioinformatics biology google machine_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:c9a23260b21a/</dc:identifier>
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<item rdf:about="https://github.com/google/nucleus">
    <title>google/nucleus: Python and C++ code for reading and writing genomics data.</title>
    <dc:date>2018-03-30T16:14:11+00:00</dc:date>
    <link>https://github.com/google/nucleus</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Python and C++ code for reading and writing genomics data.

Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF. In addition, Nucleus enables painless integration with the TensorFlow machine learning framework, as anywhere a genomics file is consumed or produced, a TensorFlow tfrecords file may be substituted.]]></description>
<dc:subject>genomics google bioinformatics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:eb4f3f92639c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
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<item rdf:about="https://www.bioconductor.org/help/docker/">
    <title>Bioconductor - Docker for Bioconductor</title>
    <dc:date>2017-05-22T17:59:34+00:00</dc:date>
    <link>https://www.bioconductor.org/help/docker/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Docker containers for Bioconductor

Docker allows software to be packaged into containers: self-contained environments that contain everything needed to run the software. Containers can be run anywhere (containers run in modern Linux kernels, but can be run on Windows and Mac as well using a virtual machine called Docker Toolbox. Containers can also be deployed in the cloud using Amazon EC2 Container Service or other cloud providers.
With Bioconductor containers, we hope to enhance
reproducibility: If you run some code in a container today, you can run it again in the same container (with the same tag) years later and know that nothing in the container has changed. You should always take note of the tag you used if you think you might want to reproduce some work later.
ease of use: With one command, you can be running the latest release or devel Bioconductor. No need to worry about whether packages and system dependencies are installed.
convenience: Sometimes you just want a fresh R with no packages installed, in order to test something; or you typically don’t have microarray packages installed but suddenly you need to do a microarray analysis. Containers make this easy.
Our aim is to provide up-to-date containers for the current release and devel versions of Bioconductor, and some older versions. Bioconductor’s Docker images are stored in Docker Hub; the source Dockerfiles are in Github.
Our release images are based on rocker/rstudio and built when a Biocondcutor Release occurs. Our devel images are based on rocker/rstudio-daily and built weekly with the latest versions of R and Bioconductor packages.]]></description>
<dc:subject>bioinformatics genomics docker</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:0b1547bc8139/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:docker"/>
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</item>
<item rdf:about="https://github.com/googlegenomics/cloudml-examples">
    <title>googlegenomics/cloudml-examples: Examples of using CloudML with genomic data.</title>
    <dc:date>2017-05-22T17:52:26+00:00</dc:date>
    <link>https://github.com/googlegenomics/cloudml-examples</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Examples of using CloudML with genomic data.
]]></description>
<dc:subject>TensorFlow machine_learning genomics bioinformatics biology google</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:657e870275e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:TensorFlow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:google"/>
</rdf:Bag></taxo:topics>
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<item rdf:about="http://googlegenomics.readthedocs.io/en/latest/use_cases/analyze_variants/analyze_variants_with_bigquery.html">
    <title>Analyze variants using Google BigQuery — Google Genomics v1 documentation</title>
    <dc:date>2017-05-22T17:50:02+00:00</dc:date>
    <link>http://googlegenomics.readthedocs.io/en/latest/use_cases/analyze_variants/analyze_variants_with_bigquery.html</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Analyze variants using Google BigQuery
Nicole]]></description>
<dc:subject>bigquery biology bioinformatics DataScience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:cb3d910cb0ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bigquery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:DataScience"/>
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</item>
<item rdf:about="http://googlegenomics.readthedocs.io/en/latest/use_cases/linkage_disequilibrium/compute_linkage_disequilibrium.html">
    <title>Compute Linkage Disequilibrium on a Variant Set — Google Genomics v1 documentation</title>
    <dc:date>2017-05-22T17:49:10+00:00</dc:date>
    <link>http://googlegenomics.readthedocs.io/en/latest/use_cases/linkage_disequilibrium/compute_linkage_disequilibrium.html</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Compute Linkage Disequilibrium on a Variant Set
Nicole]]></description>
<dc:subject>biology bioinformatics DataScience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:0ddc25eac338/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:DataScience"/>
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</item>
<item rdf:about="https://github.com/broadinstitute/scalable_analytics">
    <title>broadinstitute/scalable_analytics: Public collaboration of Scalable Single Cell Analytics</title>
    <dc:date>2017-05-22T16:14:25+00:00</dc:date>
    <link>https://github.com/broadinstitute/scalable_analytics</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Public collaboration of Scalable Single Cell Analytics
]]></description>
<dc:subject>TensorFlow biology bioinformatics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:ba150996a5d1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:TensorFlow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
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<item rdf:about="https://www.calicolabs.com/cynthia-kenyon/">
    <title>Calico – Cynthia Kenyon, Ph.D.</title>
    <dc:date>2017-05-21T21:00:09+00:00</dc:date>
    <link>https://www.calicolabs.com/cynthia-kenyon/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Cynthia Kenyon is one of the world’s foremost authorities on the molecular biology and genetics of aging and life extension. She is Calico’s vice president of aging research.

In 1993, Kenyon’s pioneering discovery that a single-gene mutation could double the lifespan of healthy, fertile C. elegans roundworms sparked an intensive study of the molecular biology of aging. Her findings showed that, contrary to popular belief, aging does not “just happen” in a completely haphazard way. Instead, the rate of aging is subject to genetic control: Animals (and likely people) contain regulatory proteins that affect aging by coordinating diverse collections of downstream genes that together protect and repair the cells and tissues. Kenyon’s findings have led to the realization that a universal hormone-signaling pathway influences the rate of aging in many species, including humans. She has identified many longevity genes and pathways, and her lab was the first to discover that neurons, and also the germ cells, can control the lifespan of the whole animal.

Kenyon graduated valedictorian in chemistry from the University of Georgia in 1976. She received her Ph.D. from MIT in 1981 and was a postdoctoral fellow with Nobel laureate Sydney Brenner in Cambridge, England. In 1986 she joined the faculty of the University of California, San Francisco, where she became the Herb Boyer Distinguished Professor and an American Cancer Society Professor, before joining Calico in 2014. Kenyon is a member of the U.S. National Academy of Sciences, the National Academy of Medicine and the American Academy of Arts and Sciences, and she is a former president of the Genetics Society of America. She has received many scientific honors and awards.]]></description>
<dc:subject>biology bioinformatics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:43e93802525d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
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<item rdf:about="https://github.com/danmaclean/gee_fu">
    <title>danmaclean/gee_fu - GitHub</title>
    <dc:date>2011-08-24T01:28:17+00:00</dc:date>
    <link>https://github.com/danmaclean/gee_fu</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Gee Fu:  #ruby #rails #bioinformatics]]></description>
<dc:subject>bioinformatics ruby rails</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:862beaa52837/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:ruby"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:rails"/>
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<item rdf:about="http://openpcr.org/what-is-openpcr/">
    <title>What is OpenPCR | OpenPCR - Open source, hackable PCR machine</title>
    <dc:date>2011-08-08T00:01:07+00:00</dc:date>
    <link>http://openpcr.org/what-is-openpcr/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[Most astonishing bit of technology that has been pointing out to me (by @fcw): ]]></description>
<dc:subject>genetics technology bioinformatics</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:667324e06494/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:amy/t:bioinformatics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.labkey.com/">
    <title>Home | LabKey Software</title>
    <dc:date>2011-06-27T15:12:18+00:00</dc:date>
    <link>http://www.labkey.com/</link>
    <dc:creator>amy</dc:creator><description><![CDATA[The best way to manage your biomedical research data.]]></description>
<dc:subject>bioinformatics collaboration research science open_source</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:amy/b:41c4b722da41/</dc:identifier>
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