[1] https://deeppcb.ai/reinforcement-learning-pcb-routing-explai... [2] https://deeppcb.ai/cooper/ [3] https://deeppcb.ai/deeppcb-kicad-plugin-ai-pcb-routing/

Disclaimer: I work at InstaDeep, the company behind DeepPCB, but I don't work on this product.

https://chemrxiv.org/engage/chemrxiv/public-api/documentatio...

Yes! That is what has been done in ChatNT [1] where you can ask natural language questions like "Determine the degradation rate of the human RNA sequence @myseq.fna on a scale from -5 to 5." and the ChatNT will answer with "The degradation rate for this sequence is 1.83."

> My biggest point of confusion is what type of practical things these models can do.

See for example this notebook [2] where the Nucleotide Transformer is finetuned to classify genomic sequences as two of the most basic genomic motifs: promoters and enhancers types.

Disclaimer: I work at InstaDeep but was not involved in either of the above projects.

[1] https://www.biorxiv.org/content/10.1101/2024.04.30.591835v2 [2] https://github.com/huggingface/notebooks/blob/main/examples/...

Comments URL: https://news.ycombinator.com/item?id=39262623

Points: 4

# Comments: 2

The SAMPLE system used four different autonomous agents, each of which designed slightly different proteins. These agents search the fitness landscape for a protein and then proceed to test and refine it over 20 cycles. The entire process took just under six months. It took one hour to assemble genes for each protein, one hour to run PCR, three hours to express the proteins in a cell-free system, and three hours to measure each protein’s heat tolerance. That’s nine hours per data point! The agents had access to a microplate reader and Tecan automation system, and some work was also done at the Strateos Cloud Lab.

SAMPLE made sugar-cutting enzymes that could tolerate temperatures 10°C higher than even the best natural sequence, called Bgl3. The AI agents weren’t “told” to enhance catalytic efficiency, but their designs also had catalytic efficiencies that matched or exceeded Bgl3.

[1] https://www.biorxiv.org/content/10.1101/2023.05.20.541582v1 [2] https://www.readcodon.com/i/122504181/ai-agents-design-prote...

Comments URL: https://news.ycombinator.com/item?id=29978785

Points: 14

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=29238270

Points: 2

# Comments: 0

[1] https://www.donkeycar.com/

    count = int(record["Count"])

So the other profiles you found could just be sloppy webmasters who didn't remove the default Team pages of their Wordpress theme.

[0] https://www.radiustheme.com/demo/wordpress/themes/financeco/...

    from Bio import Entrez
    import time
    from urllib.error import HTTPError
    
    DB = 'nucleotide'
    QUERY = '("pneumoviridae"[Organism] OR "Coronaviridae"[Organism])'
    
    Entrez.email = 'your.email@provider.com'
    handle = Entrez.esearch(db=DB, term=QUERY, rettype='fasta')
    record = Entrez.read(handle)
    
    handle = Entrez.esearch(db=DB, term=QUERY, retmax=count, rettype='fasta')
    record = Entrez.read(handle)

    id_list = record['IdList']
    count = len(id_list)
    post_xml = Entrez.epost(DB, id=",".join(id_list))
    search_results = Entrez.read(post_xml)
    
    webenv = search_results['WebEnv']
    query_key = search_results['QueryKey']

    batch_size = 200
    with open('viruses.fasta', 'w') as out_handle:
        for start in range(0, count, batch_size):
            end = min(count, start+batch_size)
            print(f"Going to download record {start+1} to {end}")
            attempt = 0
            success = False
            while attempt < 3 and not success:
                attempt += 1
                try:
                    fetch_handle = Entrez.efetch(db=DB, rettype='fasta',
                                                 retstart=start, retmax=batch_size,
                                                 webenv=webenv, query_key=query_key)
                    success = True
                except HTTPError as err:
                    if 500 <= err.code <= 599:
                        print(f"Received error from server {err}")
                        print("Attempt {attempt} of 3")
                        time.sleep(15)
                    else:
                        raise
            data = fetch_handle.read()
            fetch_handle.close()
            out_handle.write(data)

[1] https://trmm.net/Taking_things_apart#Extending [2] https://www.flickr.com/photos/osr/16412008471/lightbox/

Most human pathogens, like Staphylococcus aureus, Streptococcus pneumoniae, Escherichia coli, Salmonella enterica, Mycobacterium tuberculosis, ... have several thousand assemblies each.

[1] https://www.ncbi.nlm.nih.gov/genome/browse/

This common specification already exists. It is called GTFS [1] (General Transit Feed Specification) and can be used to exchange static transit data. There is also GTFS-realtime [2], an extension to GTFS, to be used to exchange realtime transit data.

The specification was designed through a partnership of the initial Live Transit Updates partner agencies, a number of transit developers and Google. The specification was introduced and released under the Creative Commons Attribution 3.0 license in August 2011.

[1] https://developers.google.com/transit/gtfs/ [2] https://developers.google.com/transit/gtfs-realtime/

[1] http://youtu.be/r0LH5cCuc_4

[1] http://jvns.ca/blog/2013/12/30/questions-im-asking-in-interv... [2] http://www.runtime-era.com/2013/03/my-interview-questions-fo...

He released a 37 page eBook with an easy-to-implement blueprint: http://www.amazon.com/dp/B00HY351S2

Comments URL: https://news.ycombinator.com/item?id=5850558

Points: 1

# Comments: 3

An annotated collection of all publicly available DNA sequences is available from the DNA DataBank of Japan (DDBJ), the European Molecular Biology Laboratory (EMBL), and GenBank at NCBI. They are basically all three the same database and they exchange updates on a daily basis. However, small timelags in propagating data between the database centers causes minor differences.