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#bioinformatics

9 publicaciones9 participantes1 publicación hoy

DDoS-атака
1. Одна человеческая клетка содержит 75Мб генетической информации
2. Один сперматозоид содержит 37.5Мб.
3. В одном миллилитре содержится около 100 млн сперматозоидов.
4. В среднем, эякуляция длится 5 секунд и составляет 2.25 мл спермы.
5. Таким образом, пропускная способность мужского члена будет равна:
(37.5Мб x 100M x 2.25)/5 = (37 500 000 байт/сперматозоид x 100 000 000 сперматозоид/мл x 2.25 мл) / 5 секунд = 1 687 500 000 000 000 байт/секунду = 1 687.5 Терабайт/с
Получается что женская яйцеклетка выдерживает эту DDoS-атаку на полтора терабайта в секунду, пропуская только один выбранный пакет данных и является самым офигенным в мире хардварным фаерволом...
Но тот один пакет, который она пропускает, кладёт систему на 9 месяцев...

Sure! Here's the English version of your post with categories and hashtags:
🔐 DDoS vs. Biology
**The human egg is the world’s most advanced hardware firewall.**
Incoming data stream: **1.7 TB/sec**,
Only **one packet** gets through — and it **crashes the system for 9 months.**
📌 Categories:
#Cybersecurity
#Biotech
#InfoSec
#Science
#DDoS
#TechHumor
#Bioinformatics
#Genetics
#Engineering
#NerdJokes
Let me know if you'd like it turned into a visual card, carousel, or meme graphic!

new BridgeDb Datasources release: github.com/bridgedb/datasource

"Uses UniProtKB as name, removes EcoGene, and multiple small updates"

BridgeDb Datasources is a dataset with metadata about data sources and organisms used by BridgeDb Java and downstream tools like @wikipathways, PathVisio, and others

Uses UniProtKB as name, removes EcoGene, and multiple small updates.
Full Changelog: 2024092...2027072
GitHubRelease Release 20250728 · bridgedb/datasourcesUses UniProtKB as name, removes EcoGene, and multiple small updates. Full Changelog: 2024092...2027072

Saw a Reddit post about a "Peer Review LLM", and I decided to test it with one of my own current preprints to see how good it actually is.

As I expected, it's rubbish, at least in the context of what one should actually expect from a peer review, because an LLM doesn't actually *think*.

You can see it's output here:

bgpt.pro/?q=Paper%20Review%3A%

/1

On NCBI what determines which genome assembly gets the green 'reference" star tag? I'm seeming a scaffold level assembly with 40X coverage from 2019 getting the 'reference' star whilst a complete chromosome scale assembly with 300X coverage from 2021 for the same species not... Is this a community driven label that just takes time to update?
#genomics #bioinformatics

Sequencing validates deep learning models for EHR-based detection of Noonan syndrome in pediatric patients

Cohort characteristics and model risk score distribution The study cohort comprised 92,493 patients enrolled i…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Genetics #Bioinformatics #Biomedicine #GeneFunction #GeneTherapy #general #Geneticsresearch #HumanGenetics #InternalMedicine #Next-generationsequencing #personalizedmedicine #Science
newsbeep.com/us/26569/

Sequencing validates deep learning models for EHR-based detection of Noonan syndrome in pediatric patients
United States · Sequencing validates deep learning models for EHR-based detection of Noonan syndrome in pediatric patients - United StatesDespite advanced diagnostic tools, early detection of rare genetic conditions like Noonan syndrome (NS) remains challenging. We evaluated a deep learning model’s real-world performance in identifying potential NS cases using electronic health record (EHR) data, validated through genetic sequencing and clinical assessment. The model analyzed 92,428 patients, identifying 171 high-risk individuals (score > 0.8) who underwent comprehensive review. Among these, 86 had prior genetic diagnoses, including three NS cases diagnosed during the study period. Genetic sequencing of remaining patients identified two additional NS cases with pathogenic variants. The model achieved 2.92% precision and 99.82% specificity. While precision was lower than prior validation (33.3%), this reflected expected differences in disease prevalence rather than model degradation. NS-associated phenotypes were enriched among high-risk patients, and trajectory analysis showed potential for earlier identification, highlighting both promise and limitations of EHR-based computational screening tools.

#vembrane, our CLI tool for manipulating VCF/BCF files via Python expressions has gained a new subcommand for sorting. Use it to sort by impact, pathogenicity, frequencies, or any complex Python logic, e.g. for variant prioritization.
github.com/vembrane/vembrane/b
#genomics #bioinformatics

vembrane filters VCF records using python expressions - vembrane/vembrane
GitHubvembrane/docs/sort.md at main · vembrane/vembranevembrane filters VCF records using python expressions - vembrane/vembrane