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# 01-preprocessing_1.ipynb
Repository: BIMSBbioinfo/scregseg
<code>
import os
import pandas as pd
from anndata import read_h5ad
import scanpy as sc
import scregseg
import matplotlib.pyplot as plt
</code>
# Processing and preparing raw data
This tutorial shows how to create and manipulate count matrices.
Specifically... | {
"filename": "01-preprocessing_1.ipynb",
"repository": "BIMSBbioinfo/scregseg",
"query": "transformed_from_existing",
"size": 35713,
"sha": ""
} |
# This_Python_analysis_pipeline_integrates_singlecell_and_spatial_transcriptomics_data_to_identify_clonal_transitions_and_correlate_them_with_TME_interactions_1.ipynb
Repository: connerlambden/BioloGPT
Below we describe the steps for downloading PDAC single-cell datasets and processing them for clonal analysis.
<code... | {
"filename": "This_Python_analysis_pipeline_integrates_singlecell_and_spatial_transcriptomics_data_to_identify_clonal_transitions_and_correlate_them_with_TME_interactions_1.ipynb",
"repository": "connerlambden/BioloGPT",
"query": "transformed_from_existing",
"size": 4490,
"sha": ""
} |
# 0_Index.ipynb
Repository: KitwareMedicalPublications/2018-05-30-KRSCourseInBiomedicalImageAnalysisAndVisualization
# Biomedical Image Analysis and Visualization: ITK
### Kitware, Carrboro, North Carolina
### May, 2018
Instructors:
- Matt McCormick, PhD
- Dženan Zukić, PhD
- Francois Budin
[![Kitware](data/kitwar... | {
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"repository": "KitwareMedicalPublications/2018-05-30-KRSCourseInBiomedicalImageAnalysisAndVisualization",
"query": "transformed_from_existing",
"size": 2727,
"sha": ""
} |
# ode2_lie.ipynb
Repository: bigfooted/maxima-odesolve
<h1 align="center"> Integrating factors for second order ODEs </h1>
<h3 align="center">A symbolic algorithm for the maxima CAS.</h3>
In this manual you will find how to use ode2_lie to find an integrating factor, a lambda symmetry or a first integral of a second... | {
"filename": "ode2_lie.ipynb",
"repository": "bigfooted/maxima-odesolve",
"query": "transformed_from_existing",
"size": 27511,
"sha": ""
} |
# sp24_lab03.ipynb
Repository: bethanyj0/data271
<code>
# Initialize Otter
import otter
grader = otter.Notebook("lab03.ipynb")
</code>
# Lab 3: Regular Expression with Python
Welcome to Lab 3 of DATA 271!
This document contains examples and small tasks ("appetizers") for you to make sure you understand the example... | {
"filename": "sp24_lab03.ipynb",
"repository": "bethanyj0/data271",
"query": "transformed_from_existing",
"size": 50947,
"sha": ""
} |
# prepare_data_2.ipynb
Repository: TJU-CMC-Org/CorrAdjust
# Preparing input data
To use the CorrAdjust, you will need to prepare the following input data:
- Data table and additional tables with feature/sample annotations.
- One or more GMT files listing which features (e.g., genes) belong to the same reference sets... | {
"filename": "prepare_data_2.ipynb",
"repository": "TJU-CMC-Org/CorrAdjust",
"query": "transformed_from_existing",
"size": 36729,
"sha": ""
} |
# demo1_1.ipynb
Repository: ZJUFanLab/bulk2space
## Demonstration of Bulk2Space on demo1 dataset
### Import Bulk2Space
<code>
from bulk2space import Bulk2Space
model = Bulk2Space()
</code>
### Decompose bulk-seq data into scRNA-seq data
Train β-VAE model to generate scRNA-seq data
<code>
generate_sc_meta, generat... | {
"filename": "demo1_1.ipynb",
"repository": "ZJUFanLab/bulk2space",
"query": "transformed_from_existing",
"size": 56277,
"sha": ""
} |
# ERP009703_QC_analysis_v4_1.ipynb
Repository: EBI-Metagenomics/examples
# Download QC ERP009703 pipeline v4
List all runs
https://www.ebi.ac.uk/metagenomics/api/v0.2/pipelines/4.0/analysis?experiment_type=metagenomic&study_accession=ERP009703
<code>
import collections
try:
from urllib import urlencode
except Im... | {
"filename": "ERP009703_QC_analysis_v4_1.ipynb",
"repository": "EBI-Metagenomics/examples",
"query": "transformed_from_existing",
"size": 149046,
"sha": ""
} |
# nlp-5.ipynb
Repository: juniantowicaksono06/belajar-nlp
# Tokenization
<code>
import spacy
nlp = spacy.load('en_core_web_sm')
</code>
<code>
mystring = '"We\'re moving to L.A.!"'
mystring
</code>
<code>
print(mystring)
</code>
<code>
doc = nlp(mystring)
</code>
<code>
for token in doc:
print(token.text)
</c... | {
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"repository": "juniantowicaksono06/belajar-nlp",
"query": "transformed_from_existing",
"size": 11416,
"sha": ""
} |
# genai_rag.ipynb
Repository: tPrashant1729/prashant
<code>
import streamlit as st
import os
from groq import Groq
import random
import requests
from bs4 import BeautifulSoup
from langchain.chains import ConversationChain, LLMChain
from langchain_core.prompts import (
ChatPromptTemplate,
HumanMessagePromptTemp... | {
"filename": "genai_rag.ipynb",
"repository": "tPrashant1729/prashant",
"query": "transformed_from_existing",
"size": 353950,
"sha": ""
} |
# CDD P3_2.ipynb
Repository: agusscarmu/Aromatase-Drug-Discovery
# PART 3
---
Se calcularán los descriptores moleculares. Y finalmente se preparara el DataSet
<code>
import pandas as pd
</code>
<code>
!ls
</code>
<code>
df3 = pd.read_csv('bioactivity_data_pIC50.csv')
</code>
<code>
df3
</code>
<code>
selection = ... | {
"filename": "CDD P3_2.ipynb",
"repository": "agusscarmu/Aromatase-Drug-Discovery",
"query": "transformed_from_existing",
"size": 210232,
"sha": ""
} |
# project_drug_1.ipynb
Repository: satish2705/major
<code>
import pandas as pd
import numpy as np
import random
# Generate synthetic dataset
num_samples = 1000
# Patient Information
patient_ids = [f"P{str(i).zfill(5)}" for i in range(1, num_samples + 1)]
ages = np.random.randint(18, 90, num_samples)
genders = np.ran... | {
"filename": "project_drug_1.ipynb",
"repository": "satish2705/major",
"query": "transformed_from_existing",
"size": 46092,
"sha": ""
} |
# Tumor Tissue Normal Matched TCGA_1.ipynb
Repository: satsumas/okAPI
# Programmatically Access TCGA Data using the Seven Bridges Cancer Genomics Cloud via the Datasets API
TCGA is one of the world’s largest cancer genomics data collections, including more than eleven thousand patients, representing 33 cancers, and o... | {
"filename": "Tumor Tissue Normal Matched TCGA_1.ipynb",
"repository": "satsumas/okAPI",
"query": "transformed_from_existing",
"size": 20433,
"sha": ""
} |
# genomica_13.Modulo_13_filogenetica.ipynb
Repository: cabana-online/Vigilancia
# Módulo 13: Filogenética
## Descripción general
La filogenética es el estudio de las relaciones evolutivas entre entidades biológicas, a menudo especies, individuos o genes (que pueden denominarse taxones). Los principales elementos de ... | {
"filename": "genomica_13.Modulo_13_filogenetica.ipynb",
"repository": "cabana-online/Vigilancia",
"query": "transformed_from_existing",
"size": 24600,
"sha": ""
} |
# II_Run_CLASTER.ipynb
Repository: RasmussenLab/CLASTER
# 2. CREATE & RUN CLASTER
CLASTER is, at its core, a deep convolutional neural network aimed to translate a given chromatin landscape and its matching 3D structure to the corresponding nascent RNA landscape.
The network consists of:
- **Feature extractors**:... | {
"filename": "II_Run_CLASTER.ipynb",
"repository": "RasmussenLab/CLASTER",
"query": "transformed_from_existing",
"size": 187287,
"sha": ""
} |
# Workshop_1.ipynb
Repository: NGSchoolEU/ngs19
# Import the necessary libraries
<code>
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from sklearn import preprocessing
from sklearn import ... | {
"filename": "Workshop_1.ipynb",
"repository": "NGSchoolEU/ngs19",
"query": "transformed_from_existing",
"size": 79839,
"sha": ""
} |
# example_transcriptomics_obs_segmentations_polygon_1.ipynb
Repository: vitessce/vitessce-python-tutorial
View this example on [Google Colab](https://colab.research.google.com/drive/1iB-GWk-hAmjuOUjYehHs_S94bhjxaVAP?usp=sharing)
<code>
import importlib.util
if importlib.util.find_spec('vitessce') is None:
!pip inst... | {
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"repository": "vitessce/vitessce-python-tutorial",
"query": "transformed_from_existing",
"size": 18159,
"sha": ""
} |
# DataIngestion_1.ipynb
Repository: sateeshfrnd/LangChain
# Data Ingestion using Documentloaders
A Document Loader in LangChain is a tool that helps load data from various sources, such as text files, PDFs, web pages, databases, and more. Once the data is loaded, it can be used for natural language processing (NLP), ... | {
"filename": "DataIngestion_1.ipynb",
"repository": "sateeshfrnd/LangChain",
"query": "transformed_from_existing",
"size": 249150,
"sha": ""
} |
# autoencoder_autoencoder_citeseq_saturn_3.ipynb
Repository: naity/citeseq
# Integrative analysis of single-cell multiomics data using deep learning
**Jupyter notebook:**
[](https://github.com/naity/c... | {
"filename": "autoencoder_autoencoder_citeseq_saturn_3.ipynb",
"repository": "naity/citeseq",
"query": "transformed_from_existing",
"size": 31161,
"sha": ""
} |
# QA_APP_RAG_NoteBook_1.ipynb
Repository: karthikbharadhwajKB/RAG
### RAG Application
<code>
# monitoring & tracing
import os
monitoring = True
if monitoring:
os.environ['LANGCHAIN_TRACING_V2'] = "true"
os.environ['LANGCHAIN_PROJECT'] = "Rag_App"
</code>
<code>
from dotenv import load_dotenv
# loading a... | {
"filename": "QA_APP_RAG_NoteBook_1.ipynb",
"repository": "karthikbharadhwajKB/RAG",
"query": "transformed_from_existing",
"size": 69918,
"sha": ""
} |
# cellxgene_nexus_index_2.ipynb
Repository: BiomedSciAI/biomed-multi-omic
# Create split index for CellXGeneNexusDataModule
The NexusDB data-loader consists of two layers: a front-end and a back-end. The front-end serves data to multiple node GPUs, while the back-end is responsible for data storage. We use the unive... | {
"filename": "cellxgene_nexus_index_2.ipynb",
"repository": "BiomedSciAI/biomed-multi-omic",
"query": "transformed_from_existing",
"size": 59021,
"sha": ""
} |
# Figure10g_Random_Current_1.ipynb
Repository: Fw-Franz/Volvox
# Import packages and intilize functions
<code>
from __future__ import division, unicode_literals, print_function # for compatibility with Python 2 and 3
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_man... | {
"filename": "Figure10g_Random_Current_1.ipynb",
"repository": "Fw-Franz/Volvox",
"query": "transformed_from_existing",
"size": 26238,
"sha": ""
} |
# distributed-end-to-end-flow.ipynb
Repository: aws-samples/sagemaker-distributed-training-digital-pathology-images
# Distributed training of tissue slide images using SageMaker and Horovod
## Background
Neural networks have proven effective at solving complex computer vision tasks such as object detection, image si... | {
"filename": "distributed-end-to-end-flow.ipynb",
"repository": "aws-samples/sagemaker-distributed-training-digital-pathology-images",
"query": "transformed_from_existing",
"size": 28991,
"sha": ""
} |
# project_231116_1.ipynb
Repository: sriku2412/dataraction
<code>
import pandas as pd
import re
import nltk
from nltk.stem.porter import PorterStemmer
from nltk.corpus import stopwords
from bs4 import BeautifulSoup
from datasets import load_dataset
</code>
<code>
data = pd.read_csv(r"C:\Users\srika\OneDrive\Document... | {
"filename": "project_231116_1.ipynb",
"repository": "sriku2412/dataraction",
"query": "transformed_from_existing",
"size": 141859,
"sha": ""
} |
# pfizer_correlations_1.ipynb
Repository: rheashroff/Lobbying-and-the-Market
<code>
import os, sys, time
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
from tqdm import tqdm
sns.set_style("whitegrid")
</code>
<code>
data_dir = 'LDA_data/Filings_2... | {
"filename": "pfizer_correlations_1.ipynb",
"repository": "rheashroff/Lobbying-and-the-Market",
"query": "transformed_from_existing",
"size": 104746,
"sha": ""
} |
# paresSL_blitzGSEA.ipynb
Repository: MartinSenPom/HNSCC
# Análisis de enriquecimiento con blitzGSEA
```
Autor: Martín Sende Pombo (email: martinsendepombo@outlook.com)
Se utilizó ChatGPT 3.5 como asistente de programación, para elaborar este código basado en los ejemplos proporcionados por el Ma'ayan Laboratory.
Cre... | {
"filename": "paresSL_blitzGSEA.ipynb",
"repository": "MartinSenPom/HNSCC",
"query": "transformed_from_existing",
"size": 61015,
"sha": ""
} |
# Project.ipynb
Repository: Nocnava/EmergingTechnologies
## **Deutsch's Algorithm**
##### By Conor Murphy
<br>
## **Introduction**
---
In the quantum computing field, constant advancements are being made in the realm of information computation. Among these advancements is Deutsch's algorithm, which was created by ... | {
"filename": "Project.ipynb",
"repository": "Nocnava/EmergingTechnologies",
"query": "transformed_from_existing",
"size": 103111,
"sha": ""
} |
# Comparison in single-cell data.ipynb
Repository: cantinilab/momix-notebook
# SUB-BENCHMARK3: Comparing jDR methods on single-cell datasets
The performances of the 9 jDR methods are here compared based on their ability to cluster cells based on their cancer cell line of origine. The clustering is performed jointly c... | {
"filename": "Comparison in single-cell data.ipynb",
"repository": "cantinilab/momix-notebook",
"query": "transformed_from_existing",
"size": 11968,
"sha": ""
} |
# Project02_factoranalysis.ipynb
Repository: deeplife4eu/Lecture-materials
## Project: Factor analysis for multimodal data using pyro
### Introduction
Single-cell genomics allows to profile not only a single data modality (gene expression, chromatin accessibility,...) but multiple modalities at once from the same c... | {
"filename": "Project02_factoranalysis.ipynb",
"repository": "deeplife4eu/Lecture-materials",
"query": "transformed_from_existing",
"size": 4901,
"sha": ""
} |
# KSEA_example_1.ipynb
Repository: saezlab/kinact
# Protocol for Kinase-Substrate Enrichment Analysis (KSEA)
This IPython notebook accompanies the chapter 'Phosphoproteomics-based profiling of kinase activities in cancer cell' in the book 'Methods of Molecular Biology: Cancer Systems Biology' from Springer, 2016.
Th... | {
"filename": "KSEA_example_1.ipynb",
"repository": "saezlab/kinact",
"query": "transformed_from_existing",
"size": 149932,
"sha": ""
} |
# Tangram_osmFISH.ipynb
Repository: ericcombiolab/HarmoDecon
<code>
import scanpy as sc
import squidpy as sq
import numpy as np
import pandas as pd
import anndata as ad
from anndata import AnnData
import pathlib
import matplotlib.pyplot as plt
import matplotlib as mpl
import skimage
import os
import time
</code>
<cod... | {
"filename": "Tangram_osmFISH.ipynb",
"repository": "ericcombiolab/HarmoDecon",
"query": "transformed_from_existing",
"size": 10457,
"sha": ""
} |
# log_reg_1.ipynb
Repository: RasmussenLab/njab
# Logistic regression model
Procedure:
Example: Alzheimers mass spectrometry-based proteomics dataset
> Predict Alzheimer disease based on proteomics measurements.
<code>
# Setup colab installation
# You need to restart the runtime after running this cell
%pip instal... | {
"filename": "log_reg_1.ipynb",
"repository": "RasmussenLab/njab",
"query": "transformed_from_existing",
"size": 50499,
"sha": ""
} |
# index.ipynb
Repository: yoavram/SciComPy
# Scientific Computing with Python
## Yoav Ram
## [scicompy.yoavram.com](http://scicompy.yoavram.com)
## Tutorials
- [Python](notebooks/python.ipynb)
- [NumPy](notebooks/numpy.ipynb)
- [Matplotlib](notebooks/matplotlib.ipynb)
## Lectures
1. [Pandas & Seaborn](notebooks/pa... | {
"filename": "index.ipynb",
"repository": "yoavram/SciComPy",
"query": "transformed_from_existing",
"size": 3434,
"sha": ""
} |
# Metabolomics_Shannon.ipynb
Repository: PriceLab/ShannonMets
<code>
# Run order - 1
# Needed input files: 'second_genome_2.csv', 'data_discovery.csv'
# Generated output files: '_40_coefs.csv', 'top_11_mets.csv', 'coeff_validation.csv'
</code>
<code>
# Load libraries
from sklearn.preprocessing import StandardScaler
i... | {
"filename": "Metabolomics_Shannon.ipynb",
"repository": "PriceLab/ShannonMets",
"query": "transformed_from_existing",
"size": 27681,
"sha": ""
} |
# Introduction_to_Epigenetics.ipynb
Repository: Tseehay/Standford-Data-Ocean
<img src="materials/images/introduction-to-epigenetics-cover.png"/>
# **Introduction to Epigenetics**
`🕒 This module should take less than 1 hour to complete.`
`✍️ This notebook is written using Python.`
Epigenetics is a field of study f... | {
"filename": "Introduction_to_Epigenetics.ipynb",
"repository": "Tseehay/Standford-Data-Ocean",
"query": "transformed_from_existing",
"size": 17510,
"sha": ""
} |
# Phylo_1.ipynb
Repository: mkborregaard/JuliaWorkshopIBS
Let's do some analyses combining trees and map objects
<code>
using Phylo # phylogenetics
using SpatialEcology #spatial ecology, duh
using Plots # plotting
using JLD2, SparseArrays, DataFrames #jld2 is to load our files. Due to a bug we need the other two
</co... | {
"filename": "Phylo_1.ipynb",
"repository": "mkborregaard/JuliaWorkshopIBS",
"query": "transformed_from_existing",
"size": 10817,
"sha": ""
} |
# Notes_3.ipynb
Repository: hekaplex/HSPC
# Open Problems - Multimodal Single-Cell Integration
While splitting the CITEseq RNA expression data by day-donor, I noticed that day2-donor32606 from train_cite_inputs.h5 and day2-donor27678 from test_cite_inputs.h5 had the same number of cells(7476). I got two separate expr... | {
"filename": "Notes_3.ipynb",
"repository": "hekaplex/HSPC",
"query": "transformed_from_existing",
"size": 17128,
"sha": ""
} |
# COMO_2.ipynb
Repository: HelikarLab/COMO
# COMO: Constraint-based Optomization of Metabolic Objectives
COMO is used to build computational models that simulate the biochemical and phisiological processes that occur in a cell or organism, known as constraint-based metabolic models. The basic idea behind a constraint... | {
"filename": "COMO_2.ipynb",
"repository": "HelikarLab/COMO",
"query": "transformed_from_existing",
"size": 117565,
"sha": ""
} |
# PXRD_1.ipynb
Repository: molmod/gpxrdpy
<code>
# Import statements
import numpy as np
import matplotlib.pyplot as pt
import glob
import os
from ase.io import read
from pyiron import Project, ase_to_pyiron
from molmod.units import *
from molmod.constants import *
from collections import namedtuple
from dataclasses... | {
"filename": "PXRD_1.ipynb",
"repository": "molmod/gpxrdpy",
"query": "transformed_from_existing",
"size": 42368,
"sha": ""
} |
# Assignments_Regression_5_1.ipynb
Repository: MayankG001/PW
Q1. What is Elastic Net Regression and how does it differ from other regression techniques?
Elastic Net Regression is a type of linear regression that combines the penalties of Lasso (L1) and Ridge (L2) methods. It aims to improve model accuracy and preven... | {
"filename": "Assignments_Regression_5_1.ipynb",
"repository": "MayankG001/PW",
"query": "transformed_from_existing",
"size": 9211,
"sha": ""
} |
# bottleneck_Phylogenetic-Analysis_1.ipynb
Repository: jbloomlab/SARS-CoV-2
## Phylogenetic Analysis
The goal of this notebook is to perform phylogenetic inference on the samples from the boat as well as other genomes sampled from around the same time as the boat outbreak.
**Requirements:**
Make sure you have `Biop... | {
"filename": "bottleneck_Phylogenetic-Analysis_1.ipynb",
"repository": "jbloomlab/SARS-CoV-2",
"query": "transformed_from_existing",
"size": 317835,
"sha": ""
} |
# map_citation_map_app_1.ipynb
Repository: lyuzhuoqi/citation
<code>
import pandas as pd
</code>
<code>
node_labels = {0: 'Law, Politics',
1: 'Geography & Environment',
2: 'Computing',
3: 'Dentistry, Ophthalmology, Dermatology',
4: 'Oncology',
5: 'Electrical... | {
"filename": "map_citation_map_app_1.ipynb",
"repository": "lyuzhuoqi/citation",
"query": "transformed_from_existing",
"size": 18754,
"sha": ""
} |
# vuegen_basic_case_study_1.ipynb
Repository: Multiomics-Analytics-Group/vuegen
# Predefined Directory Case Study - Notebook
[![Open In Colab][colab_badge]][colab_link]
This notebook is a basic demo of the Vuegen Python library. This sofwtare automates the creation of reports based on a directory with plots, datafra... | {
"filename": "vuegen_basic_case_study_1.ipynb",
"repository": "Multiomics-Analytics-Group/vuegen",
"query": "transformed_from_existing",
"size": 17120,
"sha": ""
} |
# BioEmu.ipynb
Repository: pokynmr/POKY
# **Biomolecular Emulator (BioEmu) in ColabFold**
<img src="https://github.com/microsoft/bioemu/raw/main/assets/emu.png" height="130" align="right" style="height:240px">
[BioEmu](https://github.com/microsoft/bioemu) is a framework for emulating biomolecular dynamics and integra... | {
"filename": "BioEmu.ipynb",
"repository": "pokynmr/POKY",
"query": "transformed_from_existing",
"size": 25576,
"sha": ""
} |
# mpf_1.ipynb
Repository: Doulos/ESE24-python
# Mit Python Fliegen
Copyright 2024 by Doulos
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
Unless requ... | {
"filename": "mpf_1.ipynb",
"repository": "Doulos/ESE24-python",
"query": "transformed_from_existing",
"size": 26244,
"sha": ""
} |
# 02_preprocess_peak_data.ipynb
Repository: morris-lab/CellOracle
# Overview
Before building the base GRN, we need to annotate the coaccessible peaks and filter our active promoter/enhancer elements. First, we will identify the peaks around transcription starting sites (TSS). We will then merge the Cicero data with t... | {
"filename": "02_preprocess_peak_data.ipynb",
"repository": "morris-lab/CellOracle",
"query": "transformed_from_existing",
"size": 32413,
"sha": ""
} |
# RNAseq.ipynb
Repository: hosseinshn/MOLI
<code>
from __future__ import print_function
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import zscore
import seaborn as sns
import sys,os
from mapper import expand, parse_mapping_table, apply_mappers
%matplotlib inline
</code>
... | {
"filename": "RNAseq.ipynb",
"repository": "hosseinshn/MOLI",
"query": "transformed_from_existing",
"size": 263232,
"sha": ""
} |
# scRNAseq_Analysis_PartI_sample6_2.ipynb
Repository: SchoberLab/YF
# Analysis Part I - Preprocessing Sample 6
<code>
%load_ext autoreload
</code>
<code>
%matplotlib inline
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings(action='ignore')
</code>
<code>
import ... | {
"filename": "scRNAseq_Analysis_PartI_sample6_2.ipynb",
"repository": "SchoberLab/YF",
"query": "transformed_from_existing",
"size": 22368,
"sha": ""
} |
# Hierarchical Clustering using Euclidean Distance.ipynb
Repository: galkinc/Hierarchical-Clustering
# Hierarchical Clustering using Euclidean Distance
# Task 1: Introduction
## - Extending Skew Analysis
Six skews of different combinations of two nucleotides: CA-, GA-, UA-, UG-, UC-, and CG-skew are used to draw ... | {
"filename": "Hierarchical Clustering using Euclidean Distance.ipynb",
"repository": "galkinc/Hierarchical-Clustering",
"query": "transformed_from_existing",
"size": 82890,
"sha": ""
} |
# Chi_Cuadrada_1.ipynb
Repository: OsmarVar/Unidad-1-Simulacion
<a href="https://colab.research.google.com/github/OsmarVar/Unidad-1-Simulacion/blob/main/Chi_Cuadrada.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<code>
import numpy as np
from sci... | {
"filename": "Chi_Cuadrada_1.ipynb",
"repository": "OsmarVar/Unidad-1-Simulacion",
"query": "transformed_from_existing",
"size": 2893,
"sha": ""
} |
# Website_GetFinalGCFData_1.ipynb
Repository: gnick18/FungalICS
## Item 1: The list of species in a given GCF
<code>
import os
import pandas as pd
import pdb
import json
gcfTable_rootDir = r'/Users/gnickles/Desktop/FungalICS_Website/Data/GCFTables'
speciesInGCFs = {}
#looping over each GCF table's summary tsv
for ... | {
"filename": "Website_GetFinalGCFData_1.ipynb",
"repository": "gnick18/FungalICS",
"query": "transformed_from_existing",
"size": 36236,
"sha": ""
} |
# Evaluation.ipynb
Repository: sithvincent/Biomedical-Information-Retrieval
<code>
import helper.pubmed_search as pubs
from helper.pubmed_search import QueryExpansionManager
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
import random
import time
import json
import math
import csv
import os... | {
"filename": "Evaluation.ipynb",
"repository": "sithvincent/Biomedical-Information-Retrieval",
"query": "transformed_from_existing",
"size": 271348,
"sha": ""
} |
# Assignment1_Assignment1_2023.ipynb
Repository: newtonharry/BINF7000
# SCIE3100/BINF7000 Assignment 1
## Probability, motif discovery, and ancestral sequence reconstruction
* **Due:** 2PM Friday 18/8/2023 (Discussion board contributions), 2PM Friday 1/9/2023 (Part A and B solutions)
* **Revision:** 2023 v1
* **Mark... | {
"filename": "Assignment1_Assignment1_2023.ipynb",
"repository": "newtonharry/BINF7000",
"query": "transformed_from_existing",
"size": 75994,
"sha": ""
} |
# CustomDB_MTG_Taxa_Profiling_v1.0-checkpoint.ipynb
Repository: new-atlantis-labs/Metagenomics
# Re-formatting plankton-specific marker genes fetched from different sources to create a custom database (DB) compatible with the powerful metagenomics-based taxonomic profiling tool [Motus](https://www.nature.com/articles/... | {
"filename": "CustomDB_MTG_Taxa_Profiling_v1.0-checkpoint.ipynb",
"repository": "new-atlantis-labs/Metagenomics",
"query": "transformed_from_existing",
"size": 22671,
"sha": ""
} |
# ae_7.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_7.ipynb",
"repository": "CKolland/Research-Internship-SchulzLab",
"query": "transformed_from_existing",
"size": 16023,
"sha": ""
} |
# S2.ipynb
Repository: yackermann/udemy-langchain
<code>
from dotenv import load_dotenv
load_dotenv(dotenv_path='.env')
</code>
# LLMs
<code>
from langchain.llms import OpenAI
llm = OpenAI()
llm.predict("How are you?")
</code>
<code>
from langchain.chat_models import ChatOpenAI
chat_model = ChatOpenAI()
chat_mo... | {
"filename": "S2.ipynb",
"repository": "yackermann/udemy-langchain",
"query": "transformed_from_existing",
"size": 19982,
"sha": ""
} |
# Abstract_notebook_final.ipynb
Repository: atlantisq/PolymerDay
<code>
import os
import pandas
import re
directory = os.getcwd()
print(directory)
pandas.set_option('display.max_rows', None)
pandas.set_option('display.max_columns', None)
pandas.set_option('display.width', None)
pandas.set_option('display.max_colwid... | {
"filename": "Abstract_notebook_final.ipynb",
"repository": "atlantisq/PolymerDay",
"query": "transformed_from_existing",
"size": 222197,
"sha": ""
} |
# old_example_2.ipynb
Repository: saeyslab/ViVAE
# *ViVAE* and *ViScore* usage example
In this Jupyter notebook, we download a single-cell dataset from Zenodo, run basic pre-processing on in and make a simple 2-dimensional layout of the data using *ViVAE*.
*(It takes around 4 minutes to run this on an M1 MacBook Air... | {
"filename": "old_example_2.ipynb",
"repository": "saeyslab/ViVAE",
"query": "transformed_from_existing",
"size": 9621,
"sha": ""
} |
# Analysis of negative control data.ipynb
Repository: vals/Blog
<code>
%pylab inline
import pandas as pd
import plotnine as p
p.theme_set(p.theme_classic())
</code>
## The effect of Poisson zeros on OLS regression results
In a [previous post](http://www.nxn.se/valent/2018/1/30/count-depth-variation-makes-poisson-scr... | {
"filename": "Analysis of negative control data.ipynb",
"repository": "vals/Blog",
"query": "transformed_from_existing",
"size": 161680,
"sha": ""
} |
# Tutorial 5_Batch-learning on large-scale dataset_2.ipynb
Repository: Hgy1014/scAGDE
# Tutorial 5: Batch-learning on large-scale dataset
Here we will use scATAC-seq dataset `10XBlood' as an example to illustrate how to train large-scale scATAC-seq data with batch-learning strategy in an end-to-end style.
## 1. Read... | {
"filename": "Tutorial 5_Batch-learning on large-scale dataset_2.ipynb",
"repository": "Hgy1014/scAGDE",
"query": "transformed_from_existing",
"size": 303489,
"sha": ""
} |
# publications.ipynb
Repository: xuesoso/xuesoso.github.io
# Publications markdown generator for academicpages
Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-noteb... | {
"filename": "publications.ipynb",
"repository": "xuesoso/xuesoso.github.io",
"query": "transformed_from_existing",
"size": 18441,
"sha": ""
} |
# p4.ipynb
Repository: satuelisa/DataScience
**Práctica 4: Visualización de información con plotly**
Ahora vamos a dibujar todo lo que en la práctica pasada parecía que habría que graficarlo.
Para que las gráficas sean interactuables con plotly en jupyter,
primero hay que extraer los datos para graficar *sin informa... | {
"filename": "p4.ipynb",
"repository": "satuelisa/DataScience",
"query": "transformed_from_existing",
"size": 41765,
"sha": ""
} |
# PathwayEnrichmentOfModules_3.ipynb
Repository: XiaYangLabOrg/SCING
<code>
library('enrichR')
library('tidyverse')
</code>
<code>
input_dir <- '../intermediate_data/'
gene_modules <- paste0(input_dir,'gene.membership.csv.gz')
</code>
<code>
modules <- read.table(gene_modules,sep=',',header=TRUE)
</code>
<code>
for... | {
"filename": "PathwayEnrichmentOfModules_3.ipynb",
"repository": "XiaYangLabOrg/SCING",
"query": "transformed_from_existing",
"size": 176555,
"sha": ""
} |
# data_wrangling_te_1.ipynb
Repository: xavier-orcutt/TrialTranslator-notebooks
# Flatiron Health mCRC: Data Wrangling Test Set
**OBJECTIVE: Create a dataframe of relevant variables using test cohort patients which will be used to validate machine learning survival models.**
**BACKGROUND: The 11 CSV Flatiron files w... | {
"filename": "data_wrangling_te_1.ipynb",
"repository": "xavier-orcutt/TrialTranslator-notebooks",
"query": "transformed_from_existing",
"size": 279720,
"sha": ""
} |
# analysis-simulation_s4.ipynb
Repository: Young-won/deepbiome
# Deep MicroBiome
Aug. 14. 2019
@ Youngwon (youngwon08@gmail.com)
<code>
import os
import json
import numpy as np
import pandas as pd
import copy
import logging
import sys
import keras.backend as k
import tensorflow as tf
import matplotlib.pyplot as pl... | {
"filename": "analysis-simulation_s4.ipynb",
"repository": "Young-won/deepbiome",
"query": "transformed_from_existing",
"size": 336263,
"sha": ""
} |
# 03_filter_reviews.ipynb
Repository: NilsHellwig/exploring-absa-llm-augmentation
# Notebook: Filter Reviews from Collected HTMLs
## Packages
<code>
from bs4 import BeautifulSoup
import pandas as pd
import spacy
import json
import nltk
from nltk.tokenize import sent_tokenize
import re
</code>
## Settings
<code>
nl... | {
"filename": "03_filter_reviews.ipynb",
"repository": "NilsHellwig/exploring-absa-llm-augmentation",
"query": "transformed_from_existing",
"size": 63137,
"sha": ""
} |
# jupyter_1.ipynb
Repository: hmelberg/causal
# Jupyter Notebooks <img src="http://blog.jupyter.org/content/images/2015/02/jupyter-sq-text.png" width='150' align='right'>
## for Collaborative and Reproducible Research
## Reproducible Research
> reproducing conclusions from a single experiment based on the measureme... | {
"filename": "jupyter_1.ipynb",
"repository": "hmelberg/causal",
"query": "transformed_from_existing",
"size": 302628,
"sha": ""
} |
# J_Resume_analyzer_1.ipynb
Repository: Aishwarya-127/Aishwarya
<a href="https://colab.research.google.com/github/Aishwarya-127/Aishwarya_J/blob/main/Resume_analyzer.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<code>
!pip install -U langchain l... | {
"filename": "J_Resume_analyzer_1.ipynb",
"repository": "Aishwarya-127/Aishwarya",
"query": "transformed_from_existing",
"size": 54509,
"sha": ""
} |
# lesson2_1.ipynb
Repository: AlyssaRSchaefer/Neural-Engineering
# Week Two: What does our brain do?
## MONDAY
1. Review items 1 (quiz) and 2 (book Make it Stick), and summarize in itemized bullet form, limited to 1/4th a page each. The other items are optional for those interested in the topic, and you don't need to... | {
"filename": "lesson2_1.ipynb",
"repository": "AlyssaRSchaefer/Neural-Engineering",
"query": "transformed_from_existing",
"size": 4149,
"sha": ""
} |
# rna_3D.ipynb
Repository: CompGenomeLab/uv-3d-ddr
## Libraries
<code>
import bioframe
import numpy as np
import pandas as pd
import gseapy as gp
import seaborn as sns
import matplotlib.pyplot as plt
import tqdm
import glob
import cooler
#bm = gp.Biomart()
pd.options.mode.chained_assignment = None # default='warn'
<... | {
"filename": "rna_3D.ipynb",
"repository": "CompGenomeLab/uv-3d-ddr",
"query": "transformed_from_existing",
"size": 41148,
"sha": ""
} |
# homework-5_4.ipynb
Repository: IB-ULFRI/homework-5
# Homework 5: Effect of SARS-CoV-2 on the host organism
We will learn about the basics of gene expression data analysis. Biologists have found a way to measure how much each gene is *expressed* in each cell in an experiment. We do this by counting the number of mRN... | {
"filename": "homework-5_4.ipynb",
"repository": "IB-ULFRI/homework-5",
"query": "transformed_from_existing",
"size": 27818,
"sha": ""
} |
# RL_1.ipynb
Repository: JoseEliel/RL
## LINKS: https://tinyurl.com/UUAIRL
## What Is Reinforcement Learning?
Imagine teaching someone to play a video game without being able to tell them the rules. You can only give them a thumbs up when they do s... | {
"filename": "RL_1.ipynb",
"repository": "JoseEliel/RL",
"query": "transformed_from_existing",
"size": 82926,
"sha": ""
} |
# chapter04_1.ipynb
Repository: leelabcnbc/book-notes
## 4.1 Introduction
### pp. 103
Eq. (4.28) looks wierd, as it seems that Gaussian plays no role in proof. No. This is because $\log p(x)$ takes a quadratic form (check Eq. (4.24)), and in this Theorem, we assume that $q$ and $p$ match in terms of second order mome... | {
"filename": "chapter04_1.ipynb",
"repository": "leelabcnbc/book-notes",
"query": "transformed_from_existing",
"size": 15947,
"sha": ""
} |
# Hands_on_8_1.ipynb
Repository: osbama/Phys437
<code>
!pip install pennylane
</code>
# Symmetry-invariant quantum machine learning force fields
Symmetries are ubiquitous in physics. From condensed matter to particle
physics, they have helped us make connections and formulate new
theories. In the context of machine ... | {
"filename": "Hands_on_8_1.ipynb",
"repository": "osbama/Phys437",
"query": "transformed_from_existing",
"size": 140786,
"sha": ""
} |
# week_4_group5_v2_1.ipynb
Repository: LaDa26/8dm50group5
# Preliminaries
## Dataset
In this set of exercises we will use the same dataset as from [week 3](week_3.ipynb).
As before, we provide the data already curated in the following two files:
`RNA_expression_curated.csv`: [148 cell lines , 238 genes]
`drug_r... | {
"filename": "week_4_group5_v2_1.ipynb",
"repository": "LaDa26/8dm50group5",
"query": "transformed_from_existing",
"size": 156752,
"sha": ""
} |
# voila_app_voila_app.ipynb
Repository: NIVANorge/watexr
<code>
%matplotlib inline
import datetime as dt
import glob
import os
import warnings
import ipywidgets as widgets
import matplotlib.pyplot as plt
import pandas as pd
from IPython.display import Image, Markdown, clear_output, display
import app_utils as au
w... | {
"filename": "voila_app_voila_app.ipynb",
"repository": "NIVANorge/watexr",
"query": "transformed_from_existing",
"size": 9122,
"sha": ""
} |
# IF_1.ipynb
Repository: Mark-Kramer/Case-Studies-Python
# The integrate and fire neuron
In this notebook we will use Python to simulate the integrate and fire (I&F) neuron model. We'll investigate, in particular, how the spiking activity varies as we adjust the input current $I$.
# Background information about the... | {
"filename": "IF_1.ipynb",
"repository": "Mark-Kramer/Case-Studies-Python",
"query": "transformed_from_existing",
"size": 90526,
"sha": ""
} |
# visium_1.ipynb
Repository: vitessce/paper-figures
<code>
# Cell type annotation with celltypist
from anndata import read_zarr
import celltypist
from celltypist import models
import scanpy as sc
from os.path import join
import numpy as np
from vitessce.data_utils import (
VAR_CHUNK_SIZE,
)
</code>
<code>
!pwd
</... | {
"filename": "visium_1.ipynb",
"repository": "vitessce/paper-figures",
"query": "transformed_from_existing",
"size": 25989,
"sha": ""
} |
# MullerianMesenchymeDifferentiation_SCENICPLUS.ipynb
Repository: ventolab/Human-ReproductiveTract-Development-Atlas
## SCENIC+ Mullerian duct mesenchymal cells
### method benchmarking
<code>
#supress warnings
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import sys
import os
</code... | {
"filename": "MullerianMesenchymeDifferentiation_SCENICPLUS.ipynb",
"repository": "ventolab/Human-ReproductiveTract-Development-Atlas",
"query": "transformed_from_existing",
"size": 219070,
"sha": ""
} |
# trrust-single-branch.ipynb
Repository: joepatmckenna/scRutiNy
# Single branch from human TRRUST network
This is an example of using [scRutiNy](http://lbm.niddk.nih.gov/mckennajp/scRutiNy) to generate single-cell RNA-seq data from a biologically realistic genetic regulatory network (TRRUST: http://www.grnpedia.org/t... | {
"filename": "trrust-single-branch.ipynb",
"repository": "joepatmckenna/scRutiNy",
"query": "transformed_from_existing",
"size": 184798,
"sha": ""
} |
# eval.ipynb
Repository: agatha-duzan/feature-intervention-for-unlearning
<code>
!pip install "lm-eval"
!pip install "lm-eval[api]"
</code>
<code>
import os
key_path = 'goodfire_api_key.txt'
with open(key_path, 'r') as file:
GOODFIRE_API_KEY = file.read().strip()
os.environ['OPENAI_API_KEY'] = GOODFIRE_API_KE... | {
"filename": "eval.ipynb",
"repository": "agatha-duzan/feature-intervention-for-unlearning",
"query": "transformed_from_existing",
"size": 80928,
"sha": ""
} |
# corona.ipynb
Repository: 0xpranjal/COVID-Genome-Computational-Analysis
# Corona Genome Analysis
#### Let's start by retreiving the complete genome of Coronavirus. The records are extracted from the wuhan region. Source: https://www.ncbi.nlm.nih.gov/nuccore/NC_045512
>Orthocoronavirinae, in the family Coronaviridae... | {
"filename": "corona.ipynb",
"repository": "0xpranjal/COVID-Genome-Computational-Analysis",
"query": "transformed_from_existing",
"size": 320296,
"sha": ""
} |
# design_NLLB_model_1.ipynb
Repository: Dimildizio/system
<a href="https://colab.research.google.com/github/Dimildizio/system_design/blob/main/NLLB_model.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Install huggingface lib
<code>
%%capture
!p... | {
"filename": "design_NLLB_model_1.ipynb",
"repository": "Dimildizio/system",
"query": "transformed_from_existing",
"size": 149499,
"sha": ""
} |
# URTmetaanalysis_logistic_regression.ipynb
Repository: Gibbons-Lab/2023
# Case vs. Control Analysis
In this notebook we'll use logistic regression to examine differences in taxonomic composition between cases and controls, conducted on a per-study basis to account for covariates. Here, we hope to uncover URT microbio... | {
"filename": "URTmetaanalysis_logistic_regression.ipynb",
"repository": "Gibbons-Lab/2023",
"query": "transformed_from_existing",
"size": 15286,
"sha": ""
} |
# schema.ipynb
Repository: EATRIS/motbx
# Schema for MOTBX resources
This notebook defines a data schema for MOTBX resources. The schema is first validated against the metaschema JSON schema draft 2020-12. It is then used to validate MOTBX resources. While MOTBX resources are stored as YAML files and the schema is st... | {
"filename": "schema.ipynb",
"repository": "EATRIS/motbx",
"query": "transformed_from_existing",
"size": 20330,
"sha": ""
} |
# lab4_1.ipynb
Repository: CSCI-360-Spring2024/Lab4
# Lab 4
- Name:
- USC Id:
### 1. Gene expression cancer RNA-Seq
Package Imports
<code>
import pandas as pd
import numpy as np
from sklearn.preprocessing import OrdinalEncoder
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
fr... | {
"filename": "lab4_1.ipynb",
"repository": "CSCI-360-Spring2024/Lab4",
"query": "transformed_from_existing",
"size": 14937,
"sha": ""
} |
# L02_1.ipynb
Repository: let-unimi/handouts
# Strutture dati ed algoritmi
## Alberi
La rappresentazione più comune che sarà adoperata per il corso per gli alberi $n$-ari sono le *lol* (liste di liste)
<code>
# [radice]
# [radice alberi…]
tree = [1, [11, [111]], [12, [121], [122]], [13]]
</code>
Accedere a radic... | {
"filename": "L02_1.ipynb",
"repository": "let-unimi/handouts",
"query": "transformed_from_existing",
"size": 98140,
"sha": ""
} |
# EnrichrConsensus.ipynb
Repository: MaayanLab/appyter-catalog
<code>
#%%appyter init
from appyter import magic
magic.init(lambda _=globals: _())
</code>
<code>
%%appyter hide_code
{% do SectionField(
name='PRIMARY',
title='Enrichr Consensus Terms',
subtitle='This appyter returns consensus Enrichr terms u... | {
"filename": "EnrichrConsensus.ipynb",
"repository": "MaayanLab/appyter-catalog",
"query": "transformed_from_existing",
"size": 23367,
"sha": ""
} |
# 03_learner_1.ipynb
Repository: matjesg/deepflash2
<code>
#default_exp learner
from nbdev.showdoc import show_doc
</code>
# Ensemble Training and Prediction
> Implements the meta classes for training and inference with deep model ensembles for deepflash2.
<code>
#hide
from fastcore.test import *
</code>
<code>
#e... | {
"filename": "03_learner_1.ipynb",
"repository": "matjesg/deepflash2",
"query": "transformed_from_existing",
"size": 38796,
"sha": ""
} |
# Fall2018Import.ipynb
Repository: mglerner/IntroductoryPhysics
<code>
import pandas as pd, makesyllabus as ms, imp
from IPython.display import HTML
</code>
<code>
df = pd.read_csv('PHYS125-0201910(10302)-Non Newtonian Physicist-responses.csv')
</code>
<code>
for row in df.sort_values('Surname').iterrows():
r = ... | {
"filename": "Fall2018Import.ipynb",
"repository": "mglerner/IntroductoryPhysics",
"query": "transformed_from_existing",
"size": 79977,
"sha": ""
} |
# Preprocess_sample22.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.ipynb",
"repository": "jiang-junyao/DRCTdb",
"query": "transformed_from_existing",
"size": 3043,
"sha": ""
} |
# marrow_analysis_bone_marrow_atlas.ipynb
Repository: Sarah145/bone
<a href="https://colab.research.google.com/github/Sarah145/bone_marrow_analysis/blob/master/bone_marrow_atlas.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Bone Marrow Atlas
Th... | {
"filename": "marrow_analysis_bone_marrow_atlas.ipynb",
"repository": "Sarah145/bone",
"query": "transformed_from_existing",
"size": 3827,
"sha": ""
} |
# nn.ipynb
Repository: lucaprotelli/nn-MNIST
### Semplice rete neurale per MNIST da zero
Questa progetto mostra come costruire e addestrare una rete neurale semplice (1 hidden layer) per classificare le cifre MNIST, implementando tutto da zero in NumPy.
##### 1. Import delle librerie e caricamento del dataset
Import... | {
"filename": "nn.ipynb",
"repository": "lucaprotelli/nn-MNIST",
"query": "transformed_from_existing",
"size": 69369,
"sha": ""
} |
# pyreft.ipynb
Repository: 3ricchen/CS224N-Project
<code>
import argparse
import random
import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
from torch.utils.data import DataLoader
from tqdm import tqdm
from my_datasets import (
ParaphraseDetectionDataset,
ParaphraseDetectionTest... | {
"filename": "pyreft.ipynb",
"repository": "3ricchen/CS224N-Project",
"query": "transformed_from_existing",
"size": 74538,
"sha": ""
} |
# a_1.ipynb
Repository: Abhishekyes/Sensor-Fault-Detection
<code>
pwd
</code>
<code>
import pandas as pd
</code>
<code>
pip install PyYAML
</code>
<code>
import yaml
</code>
<code>
pip install dill
</code>
<code>
import dill
</code>
<code>
from sensor.utils.main_utils import write_yaml_file
</code>
<code>
path... | {
"filename": "a_1.ipynb",
"repository": "Abhishekyes/Sensor-Fault-Detection",
"query": "transformed_from_existing",
"size": 12195,
"sha": ""
} |
# ref_rem_1.ipynb
Repository: skand001/MSc-Medical-Text-Summarisation-for-IRD-Publications
<code>
import re
def remove_references(text):
"""
Remove the references section from the text. This function looks for the word 'References'
followed by '1.' and removes everything from that point onward.
"""
... | {
"filename": "ref_rem_1.ipynb",
"repository": "skand001/MSc-Medical-Text-Summarisation-for-IRD-Publications",
"query": "transformed_from_existing",
"size": 116159,
"sha": ""
} |
# ia4genet.run.ipynb
Repository: grimbough/biocworkflows
- [Background](#background)
- [The gwascat package for the EMBL-EBI (formerly NHGRI) GWAS
catalog](#the-gwascat-package-for-the-embl-ebi-formerly-nhgri-gwas-catalog)
- [Basic operations, fields, and interactive
tabulation](#basic-operations... | {
"filename": "ia4genet.run.ipynb",
"repository": "grimbough/biocworkflows",
"query": "transformed_from_existing",
"size": 64054,
"sha": ""
} |
# Droplet_DPT_4.ipynb
Repository: ManchesterBioinference/GrandPrix
# Applying GrandPrix on droplet based single-cell RNA-seq of mouse embryonic stem cells
_Sumon Ahmed_, 2017, 2018
This notebooks shows how GrandPrix with informative prior over the latent space can be used to infer one dimensional pseudotime from sing... | {
"filename": "Droplet_DPT_4.ipynb",
"repository": "ManchesterBioinference/GrandPrix",
"query": "transformed_from_existing",
"size": 136068,
"sha": ""
} |
# transcriptomics_10_drug_visium_2.ipynb
Repository: imsb-uke/ANCA-GN
<code>
import sys
sys.path.append("../src")
from utils import *
</code>
<code>
adata = sc.read(os.path.join(datadir, "anca_samples_annotated_v2.h5ad"))
</code>
<code>
adata.obs["cluster_annot"].replace({"Inflamed interstitial": "Inflamed",
... | {
"filename": "transcriptomics_10_drug_visium_2.ipynb",
"repository": "imsb-uke/ANCA-GN",
"query": "transformed_from_existing",
"size": 161124,
"sha": ""
} |
# model_main2_1.ipynb
Repository: Rajcc/RAG
<code>
import langchain
</code>
<code>
from langchain_community.document_loaders import PyPDFLoader
</code>
<code>
from langchain.text_splitter import RecursiveCharacterTextSplitter
</code>
<code>
from langchain.vectorstores import Chroma
from langchain.embeddings impo... | {
"filename": "model_main2_1.ipynb",
"repository": "Rajcc/RAG",
"query": "transformed_from_existing",
"size": 70647,
"sha": ""
} |
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