生信小木屋

Text2Diagram is an AI based diagramming tool that uses Natural language text to create diagrams.
https://github.com/bhaskatripathi/Text2Diagram
webrtc
https://github.com/livekit/livekit
https://github.com/AudioLLMs/Awesome-Audio-LLM
Introduction to Text-to-Speech Using LLM’s
https://medium.com/@prajwal_/introduction-to-text-to-speech-using-llms-58ef41a92f9e
https://17aitech.com/?p=13611

https://github.com/Lightning-AI/litgpt
https://huggingface.co/THUDM/chatglm-6b/blob/main/modeling_chatglm.py

<a href="https://colab.research.google.com/gist/wangyang1749/f783b1fbdfd15ae9f3fa133499a035ca/graphrag_openai_neo4j_langchain.ipynb" target="_black"><img alt="Static Badge" src="/badge/colab-badge.svg"></a> <a href="https://gist.github.com/wangyang1749/f783b1fbdfd15ae9f3fa133499a035ca" target="_black"><img alt="Static Badge" src="/badge/github-badge.svg"></a>

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "gpt2"  
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "hello"
inputs = tokenizer(input_text, return_tensors="pt")

https://ant-design-charts.antgroup.com/zh/examples/statistics/line#line-var-size
https://zhuanlan.zhihu.com/p/27903098086
https://sandeep14.medium.com/running-graphrag-locally-with-neo4j-and-ollama-text-format-371bf88b14b7
点击下载

点击下载
http://wangyang-bucket.oss-cn-beijing.aliyuncs.com/cms/image/sh_1741857686607.pdf

https://sandbox.neo4j.com/

https://python.langchain.ac.cn/docs/how_to/graph_constructing/
----小爱发货内容----

使用方法：
- 关闭魔法（国外网）
- 输入URL和key
  2.使用说明【教程在第八步，报错请看第七步】：
  https://kcnmu6rhf93r.feishu.cn/wiki/QIdLwkgcAiTBopkHG8oc2WfZnHg?from=from_copylink
  3.小爱导航官网 chat -学术优化- lobe -查询余额地址：
- https://a.xiaoai.plus/
  注：商品仅供个人测试，请遵守法律法规。

接口：

请求地址1：https://xiaoai.plus
请求地址2：https://xiaoai.plus/v1
路由请求：https://xiaoai.plus/v1/chat/completions

依次测试接口
key在最上面，有报错问题不要发淘宝，联系【使用文档】里有售后专员！

-----每周有老客户专属小额福利活动，可联系聊天室里专员了解----
sk-FgKk2OO5RYzYRJEf7eaMytOLsuIbZecGxaJvRnWDg1GCIkNh
https://js.langchain.com/docs/integrations/chat/
https://onlinelibrary.wiley.com/doi/full/10.1002/qub2.69
https://link.springer.com/article/10.1007/s13721-024-00458-1
https://arxiv.org/abs/2502.06890
Foundation models in bioinformatics

https://www.mdpi.com/2813-2998/4/1/9#
https://link.springer.com/article/10.1186/s13073-024-01315-6
Learning the language of protein-protein interactions https://github.com/VarunUllanat/mint
seqLens: optimizing language models for genomic predictions https://www.biorxiv.org/content/10.1101/2025.03.12.642848v1

https://jyj.xianyang.gov.cn/zwgk/fdzdgknr/zcwj/wjzq/202403/t20240319_1744296.html
https://readloudly.com/viewer?type=reader&id=67e143b2947f68c8ab10a163&page=1
https://mermaid.live/
https://ant-design-charts.antgroup.com/examples
https://tidyplots.org/
https://www.ncbi.nlm.nih.gov/books/NBK179288/
https://www.ncbi.nlm.nih.gov/home/tools/
Improved metagenome binning and assembly using deep variational autoencoders

针对一个模板基因组，进行多基因组序列比较

https://www.jianshu.com/p/9df3352eb34d

细菌基因组测序项目: https://www.yunbios.net/Bacterial-genome-sequencing.html

Interactive visualisation with Phandango https://sanger-pathogens.github.io/Roary/ https://jameshadfield.github.io/phandango/#/examples
https://etetoolkit.org/treeview/
https://github.com/sanger-pathogens/pathogen-informatics-training
https://github.com/microgenomics/tutorials/blob/master/pangenome.md
https://merenlab.org/2016/11/08/pangenomics-v2/
https://www.nature.com/articles/s41467-025-57966-5
https://github.com/shenwei356/seqkit
qt问题解决:https://blog.csdn.net/LOVEmy134611/article/details/107212845
qt问题解决:https://blog.csdn.net/LOVEmy134611/article/details/107212845

pip install setuptools wheel twine
rm -rf dist/ build/ *.egg-info
python setup.py sdist bdist_wheel
twine upload dist/*



# @user.get(
#     "/users",
#     tags=["users"],
#     response_model=List[User],
#     description="Get a list of all users",
# )
# def get_users():
#     return conn.execute(users.select()).fetchall()


    

# @user.get("/users/count", tags=["users"], response_model=UserCount)
# def get_users_count():
#     result = conn.execute(select([func.count()]).select_from(users))
#     return {"total": tuple(result)[0][0]}


# @user.get(
#     "/users/{id}",
#     tags=["users"],
#     response_model=User,
#     description="Get a single user by Id",
# )
# def get_user(id: str):
#     return conn.execute(users.select().where(users.c.id == id)).first()


# @user.post("/", tags=["users"], response_model=User, description="Create a new user")
# def create_user(user: User):
#     new_user = {"name": user.name, "email": user.email}
#     new_user["password"] = f.encrypt(user.password.encode("utf-8"))
#     result = conn.execute(users.insert().values(new_user))
#     return conn.execute(users.select().where(users.c.id == result.lastrowid)).first()


# @user.put(
#     "users/{id}", tags=["users"], response_model=User, description="Update a User by Id"
# )
# def update_user(user: User, id: int):
#     conn.execute(
#         users.update()
#         .values(name=user.name, email=user.email, password=user.password)
#         .where(users.c.id == id)
#     )
#     return conn.execute(users.select().where(users.c.id == id)).first()


# @user.delete("/{id}", tags=["users"], status_code=HTTP_204_NO_CONTENT)
# def delete_user(id: int):
#     conn.execute(users.delete().where(users.c.id == id))
#     return conn.execute(users.select().where(users.c.id == id)).first()

https://sbg.github.io/tidycwl/articles/tidycwl.html

import subprocess
import time

ascp_cmd = [
    "ascp", "-QT", "-l", "100M", "-P33001",
    "-i", "C:\\path\\to\\asperaweb_id_dsa.openssh",
    "user@server:/remote/path/file.txt",
    "C:\\local\\path\\"
]

MAX_RETRY = 5
for attempt in range(1, MAX_RETRY + 1):
    print(f"尝试下载，第 {attempt} 次...")
    result = subprocess.run(ascp_cmd)
    if result.returncode == 0:
        print("下载成功")
        break
    else:
        print("下载失败，5 秒后重试...")
        time.sleep(5)
else:
    print("已达最大重试次数，下载失败")

earlyoom
https://gwu-libraries.github.io/metagenomics-analysis/aio/index.html
点击下载

点击下载

CREATE DATABASE brave
    CHARACTER SET utf8mb4
    COLLATE utf8mb4_0900_ai_ci;

连续变量 → t-test / ANOVA
分类变量 → χ² / Fisher
One-way ANOVA vs Welch’s ANOVA
非正态 + 方差不齐 → 使用 Kruskal-Wallis test
一个轻量级 LangGraph + Temporal + Node-RED 混合体

pybrave: 8def04797273866f797cc24297029fe2811f87bc

brave-ui: 2bf8c61d4a79260c0399d32579a0601bb65f6c7b
https://cloud.tencent.com/developer/article/2384602

# 1. 卸载旧版本 Docker（如果没有安装过，提示找不到包属于正常现象）
sudo apt remove -y docker docker-engine docker.io containerd runc

# 2. 更新系统并安装基础依赖
sudo apt update
sudo apt install -y ca-certificates curl gnupg lsb-release

# 1. 创建存放密钥的目录
sudo install -m 0755 -d /etc/apt/keyrings

# 2. 导入阿里云的 Docker GPG 密钥
curl -fsSL https://mirrors.aliyun.com/docker-ce/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg

# 3. 添加阿里云 Docker 软件源（会自动识别你的 Ubuntu 版本）
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://mirrors.aliyun.com/docker-ce/linux/ubuntu \
  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

# 1. 再次更新软件源，让阿里云的 Docker 源生效
sudo apt update

# 2. 安装 Docker 全套组件
sudo apt install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

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