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# epidemiology_1.ipynb
Repository: ur-whitelab/maxent
## Epidemiology Example
### Packages
<code>
# to speed-up execution, mark this as True
USE_CACHED_RESULTS = False
# cross-fold crashes Github CI
USE_CACHED_CV5_RESULTS = True
</code>
<code>
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import maxent
import... | {
"filename": "epidemiology_1.ipynb",
"repository": "ur-whitelab/maxent",
"query": "transformed_from_existing",
"size": 39709,
"sha": ""
} |
# assignment-slcomer2000_SizeAnalysis_SC.ipynb
Repository: VU-CSP/improc
# Analysis of the particle sizes from segmentation output
You should have an output file named `Results.csv` from FIJI/ImageJ segmentation. If you are using Google Colab to run this code, you will need to upload the file manually to Google using ... | {
"filename": "assignment-slcomer2000_SizeAnalysis_SC.ipynb",
"repository": "VU-CSP/improc",
"query": "transformed_from_existing",
"size": 17470,
"sha": ""
} |
# sandbox_1.ipynb
Repository: clarencew0083/contrans2024
<code>
from contrans import contrans
import numpy as np
import pandas as pd
import json
import dotenv
import requests
import os
import plotly.express as px
dotenv.load_dotenv()
postgres_password = os.getenv('POSTGRES_PASSWORD')
</code>
<code>
ct = contrans()
c... | {
"filename": "sandbox_1.ipynb",
"repository": "clarencew0083/contrans2024",
"query": "transformed_from_existing",
"size": 133794,
"sha": ""
} |
# analysis_explore_gracia2021genome_2.ipynb
Repository: yuanzhiyuan/SODB
<code>
from soview import *
import shutil
import os
import gc
</code>
<code>
data_path = 'zhiyuanyuan/PUBDT/st/visium/gracia2021genome/data'
h5ad_path = 'zhiyuanyuan/PUBDT/st/visium/gracia2021genome/h5ad'
</code>
<code>
from shutil import copyf... | {
"filename": "analysis_explore_gracia2021genome_2.ipynb",
"repository": "yuanzhiyuan/SODB",
"query": "transformed_from_existing",
"size": 9085,
"sha": ""
} |
# main_3.ipynb
Repository: ZenVInnovations/9.-enhancing-text-analytics-data-quality-with-nlp---d24f3a13
<code>
!pip uninstall -y numpy thinc spacy torch
</code>
<code>
!pip install numpy==1.26.4
!pip install torch==2.2.2
!pip install spacy==3.7.2 thinc==8.2.2
!pip install nltk textblob
</code>
<code>
!python -m sp... | {
"filename": "main_3.ipynb",
"repository": "ZenVInnovations/9.-enhancing-text-analytics-data-quality-with-nlp---d24f3a13",
"query": "transformed_from_existing",
"size": 35326,
"sha": ""
} |
# process_EHR_data_omics_1.ipynb
Repository: samson920/COMET
<code>
import pyspark
import dxpy
import dxdata
import pandas as pd
import random
from pyspark.sql import functions as F
from pyspark.sql import SparkSession
from pyspark.ml.feature import Word2Vec
from pyspark.sql.functions import col, udf, to_date, mean, e... | {
"filename": "process_EHR_data_omics_1.ipynb",
"repository": "samson920/COMET",
"query": "transformed_from_existing",
"size": 22076,
"sha": ""
} |
# CrewAi.ipynb
Repository: xoftex-airesearch/AINotes
<a href="https://colab.research.google.com/github/xoftex-airesearch/LocalAI/blob/master/CrewAI_Tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Install required pacakges
<code>
!pip... | {
"filename": "CrewAi.ipynb",
"repository": "xoftex-airesearch/AINotes",
"query": "transformed_from_existing",
"size": 55704,
"sha": ""
} |
# clinical-trials_1.ipynb
Repository: gjyoungjr/clinical-trials
# Clinical Trials
<code>
%pip install pandas
</code>
<code>
import pandas as pd
</code>
<code>
df = pd.read_csv('data/ovarian_cancer.csv')
</code>
<code>
df.head()
</code>
| {
"filename": "clinical-trials_1.ipynb",
"repository": "gjyoungjr/clinical-trials",
"query": "transformed_from_existing",
"size": 16207,
"sha": ""
} |
# proteomics_analysis_1.ipynb
Repository: MannLabs/alphapept
<code>
#| default_exp proteomics_analysis
from nbdev import *
</code>
# AlphaPept: Proteomic Analysis
This tutorial will cover the basic analysis of a mixed species dataset.
We will analyze files from Puyvelde et al.: ["A comprehensive LFQ benchmark datase... | {
"filename": "proteomics_analysis_1.ipynb",
"repository": "MannLabs/alphapept",
"query": "transformed_from_existing",
"size": 9667,
"sha": ""
} |
# Glassdoor_1.ipynb
Repository: srivarshan53/Data-Job-Trends-Analysis
<code>
import pandas as pd
</code>
<code>
df = pd.read_csv('DSJobs.csv')
</code>
<code>
df.head()
</code>
<code>
df.drop('index', axis=1, inplace=True)
</code>
<code>
df.head()
</code>
<code>
df.shape
</code>
<code>
df.nunique()
</code>
<code... | {
"filename": "Glassdoor_1.ipynb",
"repository": "srivarshan53/Data-Job-Trends-Analysis",
"query": "transformed_from_existing",
"size": 400031,
"sha": ""
} |
# 22_ODEs_1.ipynb
Repository: elizavetasemenova/prob-epi
# Ordinary Differential Equations
## Differential Equations: ODEs and PDEs
*Differential equations* are a fundamental concept in mathematics and play a crucial role in various fields of science and engineering, including physics, biology, economics, and comput... | {
"filename": "22_ODEs_1.ipynb",
"repository": "elizavetasemenova/prob-epi",
"query": "transformed_from_existing",
"size": 70853,
"sha": ""
} |
# keyword_filtering.ipynb
Repository: epfl-ada/ada-2024-project-adacadabra2048
<code>
import json
import re
import unicodedata
from nltk.corpus import stopwords
import nltk
import os
os.environ["MKL_VERBOSE"] = "0"
</code>
# Filtering the scraped data from MIT OpenCourseWare
We collected lecture video titles across... | {
"filename": "keyword_filtering.ipynb",
"repository": "epfl-ada/ada-2024-project-adacadabra2048",
"query": "transformed_from_existing",
"size": 292480,
"sha": ""
} |
# UseCases_1.ipynb
Repository: marnec/pubmedpy
<code>
import importlib
import pubmedpy as pm
from article import Figure
import itertools
import pandas as pd
import matplotlib.pyplot as plt
import pprint
from IPython.display import IFrame
import warnings
warnings.filterwarnings("ignore")
</code>
# PubMedPy
## structu... | {
"filename": "UseCases_1.ipynb",
"repository": "marnec/pubmedpy",
"query": "transformed_from_existing",
"size": 298231,
"sha": ""
} |
# figure5.ipynb
Repository: cgpu/sbas-nf
# Figure 5 - Two types of exons involved in sex-biased exon skipping.
- **(a)** log fold change expression vs. log fold change inclusion for all sex-biased events in mammary tissue. A higher fold change of female-to-male expression corresponds to a higher value on the X axis... | {
"filename": "figure5.ipynb",
"repository": "cgpu/sbas-nf",
"query": "transformed_from_existing",
"size": 269983,
"sha": ""
} |
# submit-scrna-seq-config-files_2.ipynb
Repository: ENCODE-AWG/encode-202006-jamboree-detrout-rna-sc-pipeline
# Introduction
We discussed what to do witht the pipeline configuration files, and it was suggested I submit them as attachments.
They attachments may also need to be added to Analysis objects that Jennifer ... | {
"filename": "submit-scrna-seq-config-files_2.ipynb",
"repository": "ENCODE-AWG/encode-202006-jamboree-detrout-rna-sc-pipeline",
"query": "transformed_from_existing",
"size": 76015,
"sha": ""
} |
# CODE fuzzy.ipynb
Repository: Python-Fuzzylogic/fuzzylogic
<code>
from matplotlib import pyplot as plt
from matplotlib.pyplot import plot, hlines, vlines, semilogy
plt.rc("figure", figsize=(20, 20))
</code>
<code>
%%writefile fuzzylogic/rules.py
"""Functions to evaluate, infer and defuzzify."""
from math import is... | {
"filename": "CODE fuzzy.ipynb",
"repository": "Python-Fuzzylogic/fuzzylogic",
"query": "transformed_from_existing",
"size": 327384,
"sha": ""
} |
# Geo_Data_Loading_GEO_data.ipynb
Repository: marktrix99/Loading
<code>
import os
import pandas as pd
import tarfile
import gzip
from pathlib import Path
import requests
from io import StringIO, BytesIO
from IPython.display import display
import matplotlib.pyplot as plt
class GEODataAccess:
def __init__(self, bas... | {
"filename": "Geo_Data_Loading_GEO_data.ipynb",
"repository": "marktrix99/Loading",
"query": "transformed_from_existing",
"size": 52979,
"sha": ""
} |
# cluster_lingo_1.ipynb
Repository: MaazLab/Evidence-Retrieval-For-EBM
#### TODO
1. Select cluster that have maximum numbers of high rank documents(TF-IDF ranking)
<code>
import requests
# Set the URL of the Carrot2 REST API
url = 'http://localhost:8080/service/cluster'
# Set the input data and clustering parameter... | {
"filename": "cluster_lingo_1.ipynb",
"repository": "MaazLab/Evidence-Retrieval-For-EBM",
"query": "transformed_from_existing",
"size": 31288,
"sha": ""
} |
# Bioconductor_package_curation_with_OpenAI_1.ipynb
Repository: anngvu/bioc-curation
<a href="https://colab.research.google.com/github/anngvu/bioc-curation/blob/main/Bioconductor_package_curation_with_OpenAI.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"... | {
"filename": "Bioconductor_package_curation_with_OpenAI_1.ipynb",
"repository": "anngvu/bioc-curation",
"query": "transformed_from_existing",
"size": 22227,
"sha": ""
} |
# Comparative Metagenomics_1.ipynb
Repository: EBI-Metagenomics/notebooks
<div style="max-width:1200px"><img src="../_resources/mgnify_banner.png" width="100%"></div>
<img src="../_resources/mgnify_logo.png" width="200px">
# Comparative metagenomics
## Normalization methods, alpha & beta diversity, and differential... | {
"filename": "Comparative Metagenomics_1.ipynb",
"repository": "EBI-Metagenomics/notebooks",
"query": "transformed_from_existing",
"size": 64864,
"sha": ""
} |
# phages_prep.ipynb
Repository: tsenoner/protspace
<code>
import pandas as pd
from pathlib import Path
def process_files_pandas(input_path):
output_csv = input_path.parent / f"{input_path.stem}_with_ids.csv"
output_fasta = input_path.with_suffix(".fasta")
# Read CSV and add identifiers
df = pd.read_c... | {
"filename": "phages_prep.ipynb",
"repository": "tsenoner/protspace",
"query": "transformed_from_existing",
"size": 40502,
"sha": ""
} |
# class_19_Course_Review.ipynb
Repository: kundajelab/humbio51-student
## Big Data for Biologists: Course Review - Class 19
# Course Learning Objectives
***Students should be able to***
<ol>
<li> <a href=#UnixPython>Use Unix and/or Python to view, sort and parse large data sets such as those from genome-wide gene e... | {
"filename": "class_19_Course_Review.ipynb",
"repository": "kundajelab/humbio51-student",
"query": "transformed_from_existing",
"size": 18951,
"sha": ""
} |
# Cas9_gRNA_design_CNN_off_target_after_293r_3.ipynb
Repository: happyendingddd/CRISPR
<a href="https://colab.research.google.com/github/happyendingddd/CRISPR_Cas9_gRNA_design/blob/main/CNN_off_target_after_293r.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Co... | {
"filename": "Cas9_gRNA_design_CNN_off_target_after_293r_3.ipynb",
"repository": "happyendingddd/CRISPR",
"query": "transformed_from_existing",
"size": 83567,
"sha": ""
} |
# demo_3.ipynb
Repository: xy-chen16/stPlus
# stPlus
stPlus is a reference-based method for the enhancement of spatial transcriptomics. Leveraging the holistic information in reference scRNA-seq data but not limited to the genes shared with spatial data, stPlus performs non-linear embedding for cells in both datasets... | {
"filename": "demo_3.ipynb",
"repository": "xy-chen16/stPlus",
"query": "transformed_from_existing",
"size": 49859,
"sha": ""
} |
# ST_0_createSCE.ipynb
Repository: astrid12345/Visium
<code>
BiocManager::install("BayesSpace")
</code>
<code>
########################################################################
# Author : A. Alsema
# Date : Augustus 2021
# Dataset : Visium Spatial Transcriptomics for MS lesions
# Purpose : create S... | {
"filename": "ST_0_createSCE.ipynb",
"repository": "astrid12345/Visium",
"query": "transformed_from_existing",
"size": 36843,
"sha": ""
} |
# EDA.ipynb
Repository: jpuray/ER-Games-Case-Study
# Buisness Problem
# Research Questions:
1. How should ER Games ensure they are in line with the trends of the industry?
2. Which market besides the US should ER Games target when releasing the game?
3. What impact does the changing preferences and demographics of t... | {
"filename": "EDA.ipynb",
"repository": "jpuray/ER-Games-Case-Study",
"query": "transformed_from_existing",
"size": 116239,
"sha": ""
} |
# to_Hematopoietic_Transition_01_atacdata_Zhu_et_al_analysis_with_cicero_and_monocle3_1.ipynb
Repository: aregano/Endothelial
# Overview
This notebook is an example R script on how to prepare the input data prior to building a base GRN.
Here, we use Cicero to extract the cis-regulatory connections between scATAC-s... | {
"filename": "to_Hematopoietic_Transition_01_atacdata_Zhu_et_al_analysis_with_cicero_and_monocle3_1.ipynb",
"repository": "aregano/Endothelial",
"query": "transformed_from_existing",
"size": 90241,
"sha": ""
} |
# SentimentAnalysis.ipynb
Repository: alabidi/AnnalectIntern
<code>
from transformers import pipeline
</code>
## Initiate the sentiment analysis pipleline
We are using the latest sentiment analysis to be trained on ~124M tweets. This is a fine tuned model of the base BERT model.
Further information can be found he... | {
"filename": "SentimentAnalysis.ipynb",
"repository": "alabidi/AnnalectIntern",
"query": "transformed_from_existing",
"size": 21645,
"sha": ""
} |
# Chen-2019.ipynb
Repository: zengsihang/Multiomics-Integration
<code>
import numpy as np
import pandas as pd
import networkx as nx
import scipy.io
import anndata
import scanpy as sc
from networkx.algorithms.bipartite import biadjacency_matrix
import scglue
</code>
# scRNA-seq
## Read data
<code>
rna_matrix = scip... | {
"filename": "Chen-2019.ipynb",
"repository": "zengsihang/Multiomics-Integration",
"query": "transformed_from_existing",
"size": 9199,
"sha": ""
} |
# process_website.ipynb
Repository: 13point5/langchain-experiments
<code>
from langchain.document_loaders import SeleniumURLLoader
</code>
<code>
urls = [
"https://thedecisionlab.com/reference-guide/neuroscience/behaviorism",
]
</code>
<code>
loader = SeleniumURLLoader(urls=urls)
data = loader.load()
</code>
<c... | {
"filename": "process_website.ipynb",
"repository": "13point5/langchain-experiments",
"query": "transformed_from_existing",
"size": 5206,
"sha": ""
} |
# RRBS-downstream_3.ipynb
Repository: NIGMS/Integrating-Multi-Omics-Datasets
# <span> Module 2: DNA Methylation Analysis <span>
Watch [this video](https://youtu.be/_T46fuV7qYw) to learn more about this submodule.
## **Introduction**
### <span> What is Epigenetics? <span>
+ Changes in gene expression caused by mecha... | {
"filename": "RRBS-downstream_3.ipynb",
"repository": "NIGMS/Integrating-Multi-Omics-Datasets",
"query": "transformed_from_existing",
"size": 44744,
"sha": ""
} |
# Shane_Carey_SizeAnalysis_1.ipynb
Repository: VU-CSP/github-assignment-ShaneIGP
<a href="https://colab.research.google.com/github/VU-CSP/github-assignment-ShaneIGP/blob/main/Shane_Carey_SizeAnalysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
... | {
"filename": "Shane_Carey_SizeAnalysis_1.ipynb",
"repository": "VU-CSP/github-assignment-ShaneIGP",
"query": "transformed_from_existing",
"size": 24101,
"sha": ""
} |
# Filogenia_1.ipynb
Repository: InesWeijde/TrabalhoBioInformaticaGrupo16
Para a realização da análise filogenética, seguiu-se o tutorial disponível em: https://taylor-lindsay.github.io/phylogenetics/
#### Proteína isoforma 1 codificada pelo gene NLRP3
<code>
from Bio import AlignIO
from Bio import Phylo
from Bio.Phy... | {
"filename": "Filogenia_1.ipynb",
"repository": "InesWeijde/TrabalhoBioInformaticaGrupo16",
"query": "transformed_from_existing",
"size": 268221,
"sha": ""
} |
# 201802.ipynb
Repository: nemosail/Jupyter
# 20180207 Wednesday
> 向马克思学习，向马斯克学习。。。
> Falcon Heavy 上天了， 重型， 成本低， 可回收， 跑车上天，最快最高的跑车。。。还去了火星。。。
> 做人还是要有理想的。。。有追求， 敢于say yes possible，... | {
"filename": "201802.ipynb",
"repository": "nemosail/Jupyter",
"query": "transformed_from_existing",
"size": 50108,
"sha": ""
} |
# notebook_Human_organs_1.ipynb
Repository: xindong95/SCRIP
# TF analysis
## count NMI
<code>
library(Seurat)
library(NMI)
library(RColorBrewer)
library(ggplot2)
library(dplyr)
library(ComplexHeatmap)
library(RColorBrewer)
library(patchwork)
library(data.tree)
library(gridExtra)
library(rlist)
library(phangorn)
... | {
"filename": "notebook_Human_organs_1.ipynb",
"repository": "xindong95/SCRIP",
"query": "transformed_from_existing",
"size": 32847,
"sha": ""
} |
# Peak_Analysis_1.ipynb
Repository: NPSDC/alevin-fry-atac-paper-scripts
<code>
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(GenomicRanges))
suppressPackageStartupMessages(library(plyranges))
suppressPackageStartupMessages(library(Signac))
</code>
### PBMC
<code>
piscem_da... | {
"filename": "Peak_Analysis_1.ipynb",
"repository": "NPSDC/alevin-fry-atac-paper-scripts",
"query": "transformed_from_existing",
"size": 34926,
"sha": ""
} |
# NLP_read_embedding_matrix_1.ipynb
Repository: brianyiktaktsui/DEEP
<code>
%matplotlib inline
import matplotlib as mpl
#mpl.use('Agg')
import seaborn as sns
import spacy
import os
import gensim
from tqdm import tqdm
from sklearn import manifold ,decomposition
import matplotlib.pyplot as plt
#os.chdir('/data/cellar... | {
"filename": "NLP_read_embedding_matrix_1.ipynb",
"repository": "brianyiktaktsui/DEEP",
"query": "transformed_from_existing",
"size": 297497,
"sha": ""
} |
# microbiome_project_01_microbiome_analysis.ipynb
Repository: PriceLab/Statins
#Rarefaction, beta-diversity analysis and alpha-diversity calculations
<code>
#Import packages
library(phyloseq)
library(vegan)
library(ggplot2)
library(pspearman)
library(OneR)
library(plyr)
library(dplyr)
</code>
<code>
#import phyloseq... | {
"filename": "microbiome_project_01_microbiome_analysis.ipynb",
"repository": "PriceLab/Statins",
"query": "transformed_from_existing",
"size": 5134,
"sha": ""
} |
# trails.ipynb
Repository: Shwetagithub24/End-to-end-Medical-Chatbot-using-Llama2
<code>
from langchain.document_loaders import PyPDFDirectoryLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
</code>
<code>
'''
def load_pdf_file(data):
loader = PyPDFDirectoryLoader(data)
... | {
"filename": "trails.ipynb",
"repository": "Shwetagithub24/End-to-end-Medical-Chatbot-using-Llama2",
"query": "transformed_from_existing",
"size": 21811,
"sha": ""
} |
# 03_training_hg38_all_evaluation.DNase.parallel_9_2.ipynb
Repository: gersteinlab/DECODE
<code>
#-----import packages-----#
#common python packages
import numpy as np
import string
import random
import os
import pickle
import argparse
import wget
import math
import gc
import sys
import multiprocessing as mp
import m... | {
"filename": "03_training_hg38_all_evaluation.DNase.parallel_9_2.ipynb",
"repository": "gersteinlab/DECODE",
"query": "transformed_from_existing",
"size": 18104,
"sha": ""
} |
# sbol.ipynb
Repository: combine-org/combine-notebooks
[](https://mybinder.org/v2/gh/combine-org/combine-notebooks/main?labpath=%2Fnotebooks%2Fsedml.ipynb)
<a href="https://colab.research.google.com/github/combine-org/combine-notebooks/blob/main/notebooks/sedml.ipynb" targ... | {
"filename": "sbol.ipynb",
"repository": "combine-org/combine-notebooks",
"query": "transformed_from_existing",
"size": 112445,
"sha": ""
} |
# process_website_1.ipynb
Repository: 13point5/langchain-experiments
<code>
from langchain.document_loaders import SeleniumURLLoader
</code>
<code>
urls = [
"https://thedecisionlab.com/reference-guide/neuroscience/behaviorism",
]
</code>
<code>
loader = SeleniumURLLoader(urls=urls)
data = loader.load()
</code>
... | {
"filename": "process_website_1.ipynb",
"repository": "13point5/langchain-experiments",
"query": "transformed_from_existing",
"size": 5206,
"sha": ""
} |
# with_IBM_Recommendations_with_IBM_1.ipynb
Repository: bernalp/recommendations
# Recommendations with IBM
In this notebook, you will be putting your recommendation skills to use on real data from the IBM Watson Studio platform.
You may either submit your notebook through the workspace here, or you may work from y... | {
"filename": "with_IBM_Recommendations_with_IBM_1.ipynb",
"repository": "bernalp/recommendations",
"query": "transformed_from_existing",
"size": 182007,
"sha": ""
} |
# STRIDE-MERFISH-50_2.ipynb
Repository: LiaoYunxi/STGAT
# function #
<code>
import os
import logging
import sys
import random
import numpy as np
import pandas as pd
import h5py
import tables
import argparse as ap
import scipy
import scipy.sparse as sp_sparse
import scipy.stats as ss
import collections
from collecti... | {
"filename": "STRIDE-MERFISH-50_2.ipynb",
"repository": "LiaoYunxi/STGAT",
"query": "transformed_from_existing",
"size": 96447,
"sha": ""
} |
# IndirectNeuro_0.consensus_atlas_ATACregions_hg38_2.ipynb
Repository: jjaa-mp/MultiLayered
# **Consensus atlas of ATAC regions (hg38)**
<code>
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os
# pip install gprofiler
from gprofiler import gprofiler
</code>
... | {
"filename": "IndirectNeuro_0.consensus_atlas_ATACregions_hg38_2.ipynb",
"repository": "jjaa-mp/MultiLayered",
"query": "transformed_from_existing",
"size": 82125,
"sha": ""
} |
# notebook.ipynb
Repository: j-adamczyk/podstawy-uczenia-maszynowego-24-25
# Metody probabilistyczne
<code>
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
</code>
## Ryzyko kredytowe
Każda instytucja udzielająca kredytu musi szacować zdolność kredytową i prawdopodobieństwo, czy dana osoba sp... | {
"filename": "notebook.ipynb",
"repository": "j-adamczyk/podstawy-uczenia-maszynowego-24-25",
"query": "transformed_from_existing",
"size": 38501,
"sha": ""
} |
# SMILES_1.ipynb
Repository: wtwver/iGVPT2
<code>
!pip install rdkit
</code>
<code>
import numpy as np
from rdkit.Chem import AddHs, MolFromSmiles, MolToXYZBlock, AllChem
def OrcaWrapper(SMILES, theory, basis, *args, parallel=None):
# header = f'! {" ".join([theory, basis, *args])} bohrs verytightscf ' + '{job_t... | {
"filename": "SMILES_1.ipynb",
"repository": "wtwver/iGVPT2",
"query": "transformed_from_existing",
"size": 195245,
"sha": ""
} |
# structures_in_scRNAseq_data_LDA.ipynb
Repository: Jannetty/latent
<code>
import pandas as pd
import math
import numpy as np
import scipy.io
import scipy.sparse
</code>
<code>
hu_aa_p1_barcode = pd.read_csv('../../data/Huetal2022/AA_patient_1/GSM5515741_AA1_barcodes.tsv', sep='\t', header=None)
hu_aa_p1_features = p... | {
"filename": "structures_in_scRNAseq_data_LDA.ipynb",
"repository": "Jannetty/latent",
"query": "transformed_from_existing",
"size": 14056,
"sha": ""
} |
# bdn202210.ipynb
Repository: muzaale/denotas
#### 10/2022
Bob Dylan
09/30/22
Your money meant shall be my gentle verse,
Which eyes not yet created shall o’er read;
And tongues to be, your being shall rehearse,
When all the breathers of this world are dead;
You still shall live,— such virtue hath ... | {
"filename": "bdn202210.ipynb",
"repository": "muzaale/denotas",
"query": "transformed_from_existing",
"size": 41070,
"sha": ""
} |
# TP3_1.ipynb
Repository: matthieuneau/deep-learning-practice
# TP 3 : Graph Neural Networks Architecture
**Théo Rudkiewicz, Cyriaque Rousselot**
# TUTORIAL
### Install Pytorch Geometric
To handle graph data, we use the library Pytorch Geometric : https://pytorch-geometric.readthedocs.io/en/latest/
* If you us... | {
"filename": "TP3_1.ipynb",
"repository": "matthieuneau/deep-learning-practice",
"query": "transformed_from_existing",
"size": 54732,
"sha": ""
} |
# generate-queries.ipynb
Repository: plopezgarcia/specialist-lexicon
<code>
from Bio import Entrez
import pandas as pd
import numpy as np
</code>
<code>
MAX_RESULTS = '1000'
# TODO: paginate to get more results (set to 1000 for testing quickly, max is 10000)
def search(query):
Entrez.email = 'your.email@example.c... | {
"filename": "generate-queries.ipynb",
"repository": "plopezgarcia/specialist-lexicon",
"query": "transformed_from_existing",
"size": 43009,
"sha": ""
} |
# GSE227080.ipynb
Repository: Liu-Hy/GenoTEX
<code>
import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "COVID-19"
cohort = "GSE227080"
# Input paths
in_trait_dir = "../../input/GEO/COVID-19"
in_co... | {
"filename": "GSE227080.ipynb",
"repository": "Liu-Hy/GenoTEX",
"query": "transformed_from_existing",
"size": 23422,
"sha": ""
} |
# 1_ipyrad_1.ipynb
Repository: NIGMS/Population-Genomics-and-Phylogenetics-with-RADseq
# Assembling RADseq data with ipyrad
<br>
## Overview
This Jupyter notebook covers how to assemble RADseq data from raw reads into aligned loci that can be used for downstream analyses. You will download raw data from a Google b... | {
"filename": "1_ipyrad_1.ipynb",
"repository": "NIGMS/Population-Genomics-and-Phylogenetics-with-RADseq",
"query": "transformed_from_existing",
"size": 32904,
"sha": ""
} |
# GTEXExample-PFB_1.ipynb
Repository: ga4gh/fasp-scripts
<img src="../fasp/runner/credits/images/GTEXExample.jpg" style="float: right;">
### GTEX Example using PFB in BigQuery
This notebook computes on the freely available GTEX version 8 files on Amazon AWS using the Seven Bridges Cancer Genomics Cloud WES service.
... | {
"filename": "GTEXExample-PFB_1.ipynb",
"repository": "ga4gh/fasp-scripts",
"query": "transformed_from_existing",
"size": 8104,
"sha": ""
} |
# notebooks_PNOC-008_4_1.ipynb
Repository: migbro/ipython
<code>
#!/usr/bin/env python3
import sevenbridges as sbg
from sevenbridges.errors import SbgError
from sevenbridges.http.error_handlers import rate_limit_sleeper, maintenance_sleeper
import sys
import re
import concurrent.futures
import pdb
config = sbg.Config(... | {
"filename": "notebooks_PNOC-008_4_1.ipynb",
"repository": "migbro/ipython",
"query": "transformed_from_existing",
"size": 19127,
"sha": ""
} |
# CMS_CellChat_4.ipynb
Repository: TAPE-Lab/Qin-CardosoRodriguez-et-al
<code>
library(Seurat)
library(dplyr)
library(tidyverse)
library(Matrix)
library(CellChat)
library(patchwork)
library(stringr)
library(here)
</code>
# Code to generate the SMC_subset Seurat object
Data from Lee _et. al._, Nature Genetics, 2020, r... | {
"filename": "CMS_CellChat_4.ipynb",
"repository": "TAPE-Lab/Qin-CardosoRodriguez-et-al",
"query": "transformed_from_existing",
"size": 146958,
"sha": ""
} |
# NS_Only.ipynb
Repository: ObuayaO/ObuayaO
<a href="https://colab.research.google.com/github/ObuayaO/ObuayaO/blob/main/NS_Only.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# CNS Only
<code>
from google.colab import drive
drive.mount('/content/... | {
"filename": "NS_Only.ipynb",
"repository": "ObuayaO/ObuayaO",
"query": "transformed_from_existing",
"size": 79922,
"sha": ""
} |
# SegComp_T3_1.ipynb
Repository: GabrielRQueiroz/UnB
<code>
import random
import hashlib
# import base64
import math
import time
</code>
## $\text{Parte I}$
### Formatação Base64
<code>
class Base64:
CHARSET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def b64encode(self, data: byt... | {
"filename": "SegComp_T3_1.ipynb",
"repository": "GabrielRQueiroz/UnB",
"query": "transformed_from_existing",
"size": 24440,
"sha": ""
} |
# Preprocess_sample22_1.ipynb
Repository: jiang-junyao/DRCTdb
<code>
import scanpy as sc
import numpy as np
import pandas as pd
import scipy.io as sio
import scipy.sparse as sparse
import sys
import os
</code>
<code>
def convert(filename,anndata):
if not os.path.lexists(filename):
os.makedirs(filename)
... | {
"filename": "Preprocess_sample22_1.ipynb",
"repository": "jiang-junyao/DRCTdb",
"query": "transformed_from_existing",
"size": 3043,
"sha": ""
} |
# 2_1.ipynb
Repository: Mukilan03h/nlp
<code>
import nltk
from nltk.stem import PorterStemmer, WordNetLemmatizer
</code>
<code>
import nltk
nltk.download('wordnet')
</code>
<code>
# Initialize stemmer and lemmatizer
stemmer = PorterStemmer()
lemmatizer = WordNetLemmatizer()
</code>
<code>
words = [
"running",... | {
"filename": "2_1.ipynb",
"repository": "Mukilan03h/nlp",
"query": "transformed_from_existing",
"size": 5016,
"sha": ""
} |
# test_1.ipynb
Repository: charecktowa/company-crawler
<code>
import pandas as pd
df = pd.read_excel("./data/webs.xlsx")
df.head(5)
</code>
<code>
# Check if 'Identifier (RIC)' is unique
print(df['Identifier (RIC)'].is_unique)
</code>
<code>
import sqlite3
# Connect to the SQLite database
conn = sqlite3.connect('.... | {
"filename": "test_1.ipynb",
"repository": "charecktowa/company-crawler",
"query": "transformed_from_existing",
"size": 315269,
"sha": ""
} |
# lab3.ipynb
Repository: dp1/quantum-challenges
<div align=center class="alert alert-block alert-info">
<h1>IBM Quantum Challenge Africa:</h1>
<h1>Quantum Chemistry for HIV</h1>
</div>
<img src="HIV-1_capsid_wikipedia.png"/>
<div align=center class="alert alert-block alert-info">
<h1>Table of Content... | {
"filename": "lab3.ipynb",
"repository": "dp1/quantum-challenges",
"query": "transformed_from_existing",
"size": 177186,
"sha": ""
} |
# day075_1.ipynb
Repository: hithesh111/Hith100
<div dir="ltr" style="text-align: left;" trbidi="on">
<h2 style="text-align: left;">
100 Days of ML Day 75</h2>
<h3 style="text-align: left;">
Minimum Edit Distance</h3>
Section 3 of Dan Jurafsky's NLP Course on Youtube<br />
<br />
Videos:<br />
<a href="https://w... | {
"filename": "day075_1.ipynb",
"repository": "hithesh111/Hith100",
"query": "transformed_from_existing",
"size": 1826,
"sha": ""
} |
# SaprotHub_v2_1.ipynb
Repository: westlake-repl/SaprotHub
# **SaprotHub: Democratizing Protein Language Model Training, Sharing and Collaboration for the Biology Community**
<a href="https://www.biorxiv.org/content/10.1101/2024.05.24.595648v3"><img src="https://img.shields.io/badge/Paper-bioRxiv-green" style="max-wi... | {
"filename": "SaprotHub_v2_1.ipynb",
"repository": "westlake-repl/SaprotHub",
"query": "transformed_from_existing",
"size": 255029,
"sha": ""
} |
# PythonCodeBoxes.ipynb
Repository: migariane/TutorialCausalInferenceEstimators
# Tutorial: causal inference methods made easy for applied resarchers/epidemiologists/statisticians
### ICON-LSHTM, LONDON, 16th October 2020
Miguel Angel Luque Fernandez PhD, Assistant Professor of Epidemiology and Biostatistics
Matth... | {
"filename": "PythonCodeBoxes.ipynb",
"repository": "migariane/TutorialCausalInferenceEstimators",
"query": "transformed_from_existing",
"size": 116585,
"sha": ""
} |
# engineering_usecases_MY_notebook_v2_2.ipynb
Repository: beavishead/prompt
## 1. Current weather
### 1.1 obtain current weather(without llm prompt)
<code>
!pip install pyowm
import warnings
warnings.filterwarnings("ignore")
</code>
<code>
import os
from getpass import getpass
from langchain_openai import ChatOpe... | {
"filename": "engineering_usecases_MY_notebook_v2_2.ipynb",
"repository": "beavishead/prompt",
"query": "transformed_from_existing",
"size": 67799,
"sha": ""
} |
# authors_from_label_1.ipynb
Repository: 2InfinityN6eyond/PaperClip
<code>
import os
import json
from dataclasses import dataclass
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.by import By
from bs4 import BeautifulSoup
Computer_Vision = "https://schola... | {
"filename": "authors_from_label_1.ipynb",
"repository": "2InfinityN6eyond/PaperClip",
"query": "transformed_from_existing",
"size": 140453,
"sha": ""
} |
# 00_2.ipynb
Repository: Programmer-RD-AI-Archive/Mobile-Price-Prediction
<code>
# Do Mobile-Clf-Ram and Mobile-Clf-Int-Memory
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
from sklearn.preprocessing import (
StandardScaler,
RobustScaler,
MinMaxScaler,
MaxAbsScal... | {
"filename": "00_2.ipynb",
"repository": "Programmer-RD-AI-Archive/Mobile-Price-Prediction",
"query": "transformed_from_existing",
"size": 19940,
"sha": ""
} |
# 13 - Phylogenetics with Bio.Phylo.ipynb
Repository: tiagoantao/biopython-notebook
**Source of the materials**: Biopython Tutorial and Cookbook (adapted)
# Phylogenetics with Bio.Phylo
## Demo: what is in a tree?
Lets open an example newick file
<code>
import copy
from io import StringIO
from Bio import Phylo
fr... | {
"filename": "13 - Phylogenetics with Bio.Phylo.ipynb",
"repository": "tiagoantao/biopython-notebook",
"query": "transformed_from_existing",
"size": 187553,
"sha": ""
} |
# drug_discovery_1.ipynb
Repository: XinyiYS/FAIR
### Collaborative Active Learning for Drug Discovery
<code>
import os
import json
import numpy as np
import pandas as pd
from zipfile import ZipFile
from copy import deepcopy
</code>
#### Loading drug/target interaction (DTI) data
- `X_drug`: Simplified Molecular-Inp... | {
"filename": "drug_discovery_1.ipynb",
"repository": "XinyiYS/FAIR",
"query": "transformed_from_existing",
"size": 161627,
"sha": ""
} |
# sc-smk-wl_sc_singleR_1.ipynb
Repository: CCRSF-IFX/SF
<code>
script_path = getwd() # %exclude_jupyterlab%
</code>
<code>
script_path # %exclude_jupyterlab%
</code>
<code>
system(paste0('Rscript ', script_path, # %exclude_jupyter... | {
"filename": "sc-smk-wl_sc_singleR_1.ipynb",
"repository": "CCRSF-IFX/SF",
"query": "transformed_from_existing",
"size": 107265,
"sha": ""
} |
# intro-notebook_1.ipynb
Repository: Zsailer/phylopandas
# Introduction to Phylopandas
Let me introduce you to PhyloPandas. A Pandas dataframe and interface for phylogenetics.
<code>
import pandas as pd
</code>
<code>
import phylopandas as ph
</code>
## Reading data
Phylopandas comes with various `read_` methods ... | {
"filename": "intro-notebook_1.ipynb",
"repository": "Zsailer/phylopandas",
"query": "transformed_from_existing",
"size": 61150,
"sha": ""
} |
# ae_5.ipynb
Repository: CKolland/Research-Internship-SchulzLab
# Main
Autoencoders are powerful neural network architectures used for unsupervised learning, enabling the extraction of meaningful features from high-dimensional datasets such as single-cell RNA sequencing (scRNA-seq) data. When applied to scRNA-seq dat... | {
"filename": "ae_5.ipynb",
"repository": "CKolland/Research-Internship-SchulzLab",
"query": "transformed_from_existing",
"size": 16023,
"sha": ""
} |
# pwskills_30mar_1.ipynb
Repository: Harshit28071995/assignment
<code>
Q1. What is Elastic Net Regression and how does it differ from other regression techniques?
Elastic Net Regression:
Definition: Elastic Net Regression is a regularization technique that combines both L1 (Lasso) and L2 (Ridge)
penalties. This appr... | {
"filename": "pwskills_30mar_1.ipynb",
"repository": "Harshit28071995/assignment",
"query": "transformed_from_existing",
"size": 5680,
"sha": ""
} |
# 00_Setup.ipynb
Repository: tebe-nigrelli/MMN-Group-Project
This file sets up the various variables and functions that are used at
every point in the project.
The contents of this file are also made available via `import dataset`
from `dataset.py`.
# Imports
Various useful builtins:
<code>
from typing import *
im... | {
"filename": "00_Setup.ipynb",
"repository": "tebe-nigrelli/MMN-Group-Project",
"query": "transformed_from_existing",
"size": 127400,
"sha": ""
} |
# day1-1_cellranger_4.ipynb
Repository: sib-swiss/single-cell-python-training
# Cell Ranger
<style>
.large-link {
font-size: 30px;
font-family: Arial, sans-serif;
color: #333;
text-decoration: none;
}
</style>
<a href="../assets/pdf/01_introduction.pdf" target="_blank" class="l... | {
"filename": "day1-1_cellranger_4.ipynb",
"repository": "sib-swiss/single-cell-python-training",
"query": "transformed_from_existing",
"size": 26959,
"sha": ""
} |
# chlorophyll_science.ipynb
Repository: robfatland/chlorophyll
# Chlorophyll science
<BR>
<img src="./images/misc/Sphyrna_mokarran_feeding.png" style="float: left;" alt="drawing" width="2000"/>
<div style="clear: left"><BR>
Two apex predators share a snack
## Introduction
The idea of **chlorophyll** ... | {
"filename": "chlorophyll_science.ipynb",
"repository": "robfatland/chlorophyll",
"query": "transformed_from_existing",
"size": 12522,
"sha": ""
} |
# practical_PhyloPractical_1.ipynb
Repository: davelunt/phylo
<h1><font color='DarkBlue'>PRACTICAL PHYLOGENETICS NOTEBOOK</font></h1>
<hr>
Dr Dave Lunt d.h.lunt@hull.ac.uk
<h2><font color='Blue'>Goals of these experiments</font></h2>
This Jupyter notebook will take you through two case studies using phylogenetic ana... | {
"filename": "practical_PhyloPractical_1.ipynb",
"repository": "davelunt/phylo",
"query": "transformed_from_existing",
"size": 43559,
"sha": ""
} |
# bulk_analysis_1.ipynb
Repository: LiLabAtVT/ConSReg
## Introduction
This Jupyter notebook walks through the basic functionalities of ConSReg that allow for building regulatory networks, and prioritizing important transcription factors (TFs) from the integration of DAP-seq, ATAC-seq and RNA-seq. Datasets used in this... | {
"filename": "bulk_analysis_1.ipynb",
"repository": "LiLabAtVT/ConSReg",
"query": "transformed_from_existing",
"size": 267058,
"sha": ""
} |
# 05_data_analysis_wrangling_1.ipynb
Repository: uleth-advanced-bioinformatics/BCHM5420A-summer-2025
# Data Wrangling and Analysis
2025-05-21
Once you have your Nextflow pipeline outputs it is time to filter, format, and analyze them. This lesson looks at best practices and provides suggestions to help you extract m... | {
"filename": "05_data_analysis_wrangling_1.ipynb",
"repository": "uleth-advanced-bioinformatics/BCHM5420A-summer-2025",
"query": "transformed_from_existing",
"size": 76085,
"sha": ""
} |
# nb025-langchain-langsmith_1.ipynb
Repository: JuanitoC/GENAI
<code>
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai_api_key = os.environ["OPENAI_API_KEY"]
</code>
## LangSmith
*Log In at https://smith.langchain.com*
<code>
# LANGCHAIN_TRACING_V2=true
# LANGCHAIN_ENDPOIN... | {
"filename": "nb025-langchain-langsmith_1.ipynb",
"repository": "JuanitoC/GENAI",
"query": "transformed_from_existing",
"size": 55483,
"sha": ""
} |
# IR_classwork_1.ipynb
Repository: sanjok1988/jupyternote
<code>
# Crawler Example
</code>
<code>
!pip3 install beautifulsoup4
import requests
from bs4 import BeautifulSoup
import csv
</code>
<code>
url="https://pureportal.coventry.ac.uk/en/organisations/ihw-centre-for-health-and-life-sciences-chls/publications/"
o... | {
"filename": "IR_classwork_1.ipynb",
"repository": "sanjok1988/jupyternote",
"query": "transformed_from_existing",
"size": 14104,
"sha": ""
} |
# log_elsa_1.ipynb
Repository: Aitslab/BioNLP
# December
## Aims:
*
## Activities:
*
## Next steps:
*
# December
## Aims:
*
## Activities:
*
## Next steps:
*
# December
## Aims:
*
## Activities:
*
## Next steps:
*
# December 2nd
## Aims:
* Improve discussion
## Activiti... | {
"filename": "log_elsa_1.ipynb",
"repository": "Aitslab/BioNLP",
"query": "transformed_from_existing",
"size": 108590,
"sha": ""
} |
# Week6.ipynb
Repository: bence-szalai/datasci-adv-phd-course
<code>
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA
import statsmodels.formula.api as smf
</code>
### Functional analysis of transcriptomics data from SARS-CoV-2 i... | {
"filename": "Week6.ipynb",
"repository": "bence-szalai/datasci-adv-phd-course",
"query": "transformed_from_existing",
"size": 271695,
"sha": ""
} |
# preprocess_5.ipynb
Repository: cellatlas/human
<a href="https://colab.research.google.com/github/cellatlas/human/blob/master/data/mammary/GSM3516947/preprocess.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<code>
!pip install --quiet kb-python
... | {
"filename": "preprocess_5.ipynb",
"repository": "cellatlas/human",
"query": "transformed_from_existing",
"size": 7205,
"sha": ""
} |
# csl_writer.ipynb
Repository: front-matter/commonmeta-py
As with all commonmeta-py Writer notebooks, we start by fetching metadata, in this example a journal article via its Crossref DOI, and convert them to the internal commonmeta format.
<code>
from commonmeta import Metadata
# Fetch metadata from a DOI
string = ... | {
"filename": "csl_writer.ipynb",
"repository": "front-matter/commonmeta-py",
"query": "transformed_from_existing",
"size": 19713,
"sha": ""
} |
# Requirements_1.ipynb
Repository: fraenkel-lab/QBD
# AnswerALS Cloud: Technical Requirements
#### Alex LeNail, alex@lenail.org
This notebook describes requirements for a system to **"Query by Data"** and **"Compute on Data"** against the AnswerALS datastore in Azure.
[This document provides background on the prob... | {
"filename": "Requirements_1.ipynb",
"repository": "fraenkel-lab/QBD",
"query": "transformed_from_existing",
"size": 28072,
"sha": ""
} |
# cuartopunto_4.ipynb
Repository: nicollF/parcial1
# **Ejercicio 4 (modelo de regresión)**
<code>
import warnings
warnings.filterwarnings('ignore')
import mglearn
import matplotlib
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklear... | {
"filename": "cuartopunto_4.ipynb",
"repository": "nicollF/parcial1",
"query": "transformed_from_existing",
"size": 47397,
"sha": ""
} |
# zzz.ipynb
Repository: j-ntw/tube
<code>
def isPrime(n):
# Write your code here
for i in range(n//2):
if i == 0 or i == 1:
continue
elif (n % i == 0):
return i
return 1
isPrime(6)
</code>
<code>
import requests
def getMovieTitles(substr):
base_url = "https://js... | {
"filename": "zzz.ipynb",
"repository": "j-ntw/tube",
"query": "transformed_from_existing",
"size": 19925,
"sha": ""
} |
# demo.ipynb
Repository: sbhattlab/phylo2vec
# Phylo2Vec demo
Welcome to the Phylo2Vec demo! Here, we will quickly visit the main functions of Phylo2Vec, including:
* How to sample random tree topologies (cladograms) as Phylo2Vec vectors
* How to convert Phylo2Vec vectors to Newick format and vice versa
* How to samp... | {
"filename": "demo.ipynb",
"repository": "sbhattlab/phylo2vec",
"query": "transformed_from_existing",
"size": 38697,
"sha": ""
} |
# 2019_homework05_1.ipynb
Repository: fredhutchio/tfcb
# Homework05 (50 points): Parsing a 10x genomics T cell receptor sequencing file
The 10x genomics single-cell platform is increasingly being used to study T cells, and they've recently released a kit that combines targeted sequencing of the T cell receptor alpha ... | {
"filename": "2019_homework05_1.ipynb",
"repository": "fredhutchio/tfcb",
"query": "transformed_from_existing",
"size": 9070,
"sha": ""
} |
# evaluate_AMI_1.ipynb
Repository: Bela4321/article-clustering
<code>
from tqdm import tqdm
from embeddings.embedding_utils import get_queries, get_query_key
import pickle
import numpy as np
import pandas as pd
import math
</code>
<code>
def load_clustering(query_key, embedding_algo, clustering_algo):
embedding... | {
"filename": "evaluate_AMI_1.ipynb",
"repository": "Bela4321/article-clustering",
"query": "transformed_from_existing",
"size": 22033,
"sha": ""
} |
# Yao-2021_4.ipynb
Repository: gao-lab/GLUE
<code>
import gzip
import pickle
import anndata
import numpy as np
import pandas as pd
import scanpy as sc
import scipy.sparse
import sklearn.preprocessing
from networkx.algorithms.bipartite import biadjacency_matrix
from ALLCools.mcds import MCDS
import scglue
</code>
<c... | {
"filename": "Yao-2021_4.ipynb",
"repository": "gao-lab/GLUE",
"query": "transformed_from_existing",
"size": 24430,
"sha": ""
} |
# Ray_1.ipynb
Repository: nihalraza369/technology-background
#Distribute Computing using Ray
---
Ray is an open-source framework designed to simplify the development and scaling of distributed applications, particularly those involving machine learning (ML) and artificial intelligence (AI). By providing a unified i... | {
"filename": "Ray_1.ipynb",
"repository": "nihalraza369/technology-background",
"query": "transformed_from_existing",
"size": 3588,
"sha": ""
} |
# microbiome_ph_1.ipynb
Repository: martinjzhang/AdaFDRpaper
<code>
import numpy as np
import pandas as pd
import adafdr.method as md
import adafdr.data_loader as dl
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
</code>
# Microbiome: enigma_ph
<code>
data_path = '/data3/martin... | {
"filename": "microbiome_ph_1.ipynb",
"repository": "martinjzhang/AdaFDRpaper",
"query": "transformed_from_existing",
"size": 58598,
"sha": ""
} |
# Make_Tables.ipynb
Repository: sars-cov-2-origins/huanan-market-environment
<code>
import pandas as pd
prefix = '../huanan-env-paper-private/data/sample_metadata/Liu_etal_2023_market_samples_acc_Apr16.tsv'
</code>
## Table S1: Sample metadata
<code>
sample_metadata = pd.read_csv('{}/data/sample_metadata/Liu_etal_20... | {
"filename": "Make_Tables.ipynb",
"repository": "sars-cov-2-origins/huanan-market-environment",
"query": "transformed_from_existing",
"size": 40340,
"sha": ""
} |
# analysis_111_1.ipynb
Repository: KristofferC/PkgEvalAnalysis
# PkgEvalAnalysis
Latest pkgeval: https://s3.amazonaws.com/julialang-reports/nanosoldier/pkgeval/by_hash/bdc7fb7_vs_bd47eca/report.html
<code>
using DataFrames, Feather
using JuliaRegistryAnalysis
using Graphs, MetaGraphs
using Downloads
include("add_ba... | {
"filename": "analysis_111_1.ipynb",
"repository": "KristofferC/PkgEvalAnalysis",
"query": "transformed_from_existing",
"size": 35319,
"sha": ""
} |
# S6.49-53.ipynb
Repository: yackermann/udemy-langchain
<code>
from dotenv import load_dotenv
load_dotenv(dotenv_path='.env')
</code>
# The stuff strategy
<code>
from langchain.document_loaders import PyPDFLoader
file_path = "./mixed_data/ESLII_print12_toc (1).pdf"
loader = PyPDFLoader(file_path=file_path)
docs =... | {
"filename": "S6.49-53.ipynb",
"repository": "yackermann/udemy-langchain",
"query": "transformed_from_existing",
"size": 85946,
"sha": ""
} |
# 02.scatac_scrna_integration_1.ipynb
Repository: xuzhougeng/CrossSpeciesPlantShootAtlas
<code>
import anndata as ad
import scanpy as sc
import networkx as nx
import numpy as np
import pandas as pd
import scglue
import seaborn as sns
from IPython import display
from matplotlib import rcParams
from networkx.algorithms.... | {
"filename": "02.scatac_scrna_integration_1.ipynb",
"repository": "xuzhougeng/CrossSpeciesPlantShootAtlas",
"query": "transformed_from_existing",
"size": 12478,
"sha": ""
} |
# 03_GDSC_map_CNV_1.ipynb
Repository: PeeteKeesel/gnn-for-drug-response-prediction
<code>
import sys
import time
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style="white")
</code>
# Copy Number Variation Insights
The objective of this noteboo... | {
"filename": "03_GDSC_map_CNV_1.ipynb",
"repository": "PeeteKeesel/gnn-for-drug-response-prediction",
"query": "transformed_from_existing",
"size": 282475,
"sha": ""
} |
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