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# biopython_notebook_1.ipynb
Repository: Deya-B/Bioinformatics-notes
<code>
from Bio.Seq import Seq
seq = Seq('GATTACA')
#Seq methods represent biological sequences as strings
print(seq)
</code>
<code>
seq = Seq('CAT')
for base in seq:
print(base, end=' ')
seq + 'GAT'
</code>
<code>
dna = Seq('GATTACA')... | {
"filename": "biopython_notebook_1.ipynb",
"repository": "Deya-B/Bioinformatics-notes",
"query": "transformed_from_existing",
"size": 199361,
"sha": ""
} |
# 02-warmup-sol.ipynb
Repository: hanisaf/mist5730-6380-spring2020
Refer to [the University of Georgia by the Numbers Page](https://www.uga.edu/facts.php)
Reconstruct (most) of this page using markdown in this notebook
# UGA by the Numbers
**Founded:**
> January 27, 1785, by the Georgia General Assembly. UGA is... | {
"filename": "02-warmup-sol.ipynb",
"repository": "hanisaf/mist5730-6380-spring2020",
"query": "transformed_from_existing",
"size": 3939,
"sha": ""
} |
# SIMS_tutorial_4.ipynb
Repository: braingeneers/SIMS
## **SIMS Tutorial**
In this tutorial, we will walk through the [SIMS (Scalable, Interpretable Machine Learning for Single Cell)](https://www.cell.com/cell-genomics/fulltext/S2666-979X(24)00165-4) pipeline step by step. SIMS is a deep learning-based tool built on T... | {
"filename": "SIMS_tutorial_4.ipynb",
"repository": "braingeneers/SIMS",
"query": "transformed_from_existing",
"size": 119177,
"sha": ""
} |
# HiDENSEC.ipynb
Repository: songlab-cal/HiDENSEC
# Global Variables & Function Definitions
These global definitions require evaluation before running HiDENSEC on any concrete Hi-C map.
## Modules
<code>
import numpy as np
import scipy.sparse as sp_sparse
import scipy.signal as sp_signal
import scipy.ndimage as sp_... | {
"filename": "HiDENSEC.ipynb",
"repository": "songlab-cal/HiDENSEC",
"query": "transformed_from_existing",
"size": 51034,
"sha": ""
} |
# DESeq2_4.ipynb
Repository: LucaMenestrina/DEGA
# DESeq2 Use Case
## Load Libraries
<code>
library("DESeq2")
library("genefilter")
</code>
Set variables (data from the [Bottomly et al.](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0017820) dataset)
<code>
GENE_COUNTS = "https://raw.githubuser... | {
"filename": "DESeq2_4.ipynb",
"repository": "LucaMenestrina/DEGA",
"query": "transformed_from_existing",
"size": 14505,
"sha": ""
} |
# Project_未命名.ipynb
Repository: Peevin/TNBC
<code>
import scanpy as sc
import pandas as pd
import numpy as np
</code>
<code>
sc.settings.set_figure_params(dpi=300, facecolor='white')
</code>
<code>
adata = sc.read_h5ad('/Users/liupeiwen/BC/21 CC Single-cell analyses reveal key immune cell subsets associated with res... | {
"filename": "Project_未命名.ipynb",
"repository": "Peevin/TNBC",
"query": "transformed_from_existing",
"size": 255361,
"sha": ""
} |
# taxonomy_explore_github_topics.ipynb
Repository: kuefmz/define
<code>
import pandas as pd
</code>
<code>
df = pd.read_csv('topics.csv')
</code>
<code>
df.head()
</code>
<code>
df.shape
</code>
<code>
print('Number of different topics on GitHub')
len(df['topic'].unique())
</code>
<code>
topic_counter = {}
for in... | {
"filename": "taxonomy_explore_github_topics.ipynb",
"repository": "kuefmz/define",
"query": "transformed_from_existing",
"size": 48165,
"sha": ""
} |
# bioinformatics_bootcamp_2018_ATAC-seq-checkpoint_2.ipynb
Repository: ryanmarina/BMS
# BIOM 200 bioinformatics bootcamp - ATAC-seq analysis
* [(Pre-class) Introduction](#introduction)
* [(Pre-class) Installations](#installations)
* [(In-class) Data processing](#processing)
* [(In-class) Data analysis](#processing)
*... | {
"filename": "bioinformatics_bootcamp_2018_ATAC-seq-checkpoint_2.ipynb",
"repository": "ryanmarina/BMS",
"query": "transformed_from_existing",
"size": 28407,
"sha": ""
} |
# thesis_homer_genome_annotation_1.ipynb
Repository: liouhy/2022-Charite-master
# HOMER - genome annotation
Here, we used HOMER to annotate genomic regions from scATAC-seq datasets. First, we created bed files of genomic regions.
<code>
import pandas as pd
import anndata as ad
</code>
<code>
# Granja et al.
ft = pd.... | {
"filename": "thesis_homer_genome_annotation_1.ipynb",
"repository": "liouhy/2022-Charite-master",
"query": "transformed_from_existing",
"size": 3038,
"sha": ""
} |
# table_model_1_1.ipynb
Repository: DongjoonLim/EvoLSTM
<code>
import numpy as np
from tqdm.notebook import tqdm
!nvidia-smi
</code>
<code>
k = 7
des = str(np.load('prepData/insert2Des__HPGPNRMPC_hg38_chr2.npy'))
anc = str(np.load('prepData/insert2Anc__HPGPNRMPC_hg38_chr2.npy'))
print(len(anc), len(des))
def build... | {
"filename": "table_model_1_1.ipynb",
"repository": "DongjoonLim/EvoLSTM",
"query": "transformed_from_existing",
"size": 32967,
"sha": ""
} |
# Evaluate_Integration_LISI.ipynb
Repository: pughlab/cancer-scrna-integration
---
# Evaluate data integration using LISI
*L.Richards*
*2021-06-14*
*/cluster/projects/pughlab/projects/cancer_scrna_integration/evalutation/lisi*
---
https://github.com/immunogenomics/LISI
<code>
# install.packages("devtools")
# de... | {
"filename": "Evaluate_Integration_LISI.ipynb",
"repository": "pughlab/cancer-scrna-integration",
"query": "transformed_from_existing",
"size": 5486,
"sha": ""
} |
# MCAsubset-checkpoint.ipynb
Repository: CSUBioGroup/scNCL-release
<code>
%load_ext autoreload
%autoreload 2
import os
import h5py
import seaborn as sns
import numpy as np
import pandas as pd
import scanpy as sc
import anndata
import csv
import gzip
import scipy.io
import scipy.sparse as sps
import matplotlib.pyplot... | {
"filename": "MCAsubset-checkpoint.ipynb",
"repository": "CSUBioGroup/scNCL-release",
"query": "transformed_from_existing",
"size": 199827,
"sha": ""
} |
# GPT_1.ipynb
Repository: ZubairQazi/NDE-GPT
# GPT for Topic Categorization
<code>
import json
import pandas as pd
import numpy as np
import ast
import os
import re
from bs4 import BeautifulSoup
import csv
from tqdm.notebook import tqdm
import openai
from langchain.llms import OpenAI
from langchain.chat_models impo... | {
"filename": "GPT_1.ipynb",
"repository": "ZubairQazi/NDE-GPT",
"query": "transformed_from_existing",
"size": 117418,
"sha": ""
} |
# New_eng_academic_research_2.ipynb
Repository: kdj0712/teamKim1
<code>
import pandas as pd
import numpy as np
</code>
<code>
df_Riss_research = pd.read_csv("./csv/Seleniums.eng_academic_research.csv")
df_Riss_research.drop(labels='_id', axis=1, inplace=True)
df_Riss_research['research_subject']
</code>
## 데이터 전처리
... | {
"filename": "New_eng_academic_research_2.ipynb",
"repository": "kdj0712/teamKim1",
"query": "transformed_from_existing",
"size": 277407,
"sha": ""
} |
# analyses_3.SCENIC-V10-V2_1.ipynb
Repository: aertslab/scenicplus
### 1. Create SCENIC+ object
<code>
# Load functions
from scenicplus.scenicplus_class import SCENICPLUS, create_SCENICPLUS_object
from scenicplus.preprocessing.filtering import *
</code>
First we will load the scRNA-seq and the scATAC-seq data. We ma... | {
"filename": "analyses_3.SCENIC-V10-V2_1.ipynb",
"repository": "aertslab/scenicplus",
"query": "transformed_from_existing",
"size": 123784,
"sha": ""
} |
# generate_chapter_dataset_1.ipynb
Repository: AFF-Learntelligence/machine-learning
# Buat dataframe
<code>
import pandas as pd
# List of course topics with chapters
courses_with_chapters = {
"Introduction to Programming with Python": [
"Chapter 1: Getting Started with Python",
"Chapter 2: Variab... | {
"filename": "generate_chapter_dataset_1.ipynb",
"repository": "AFF-Learntelligence/machine-learning",
"query": "transformed_from_existing",
"size": 355506,
"sha": ""
} |
# bdn2022.ipynb
Repository: muzaale/denotas
```
#colleenhoover
— wtf is she?
— has no. 6, 8, 11, and 15 on Amazon
```
.
```
#modusoperandi
Ambition/Beyond
Morality/Good
Nyege Nyege/Evil
🏔
```
.
```
Dear GTPCI Advisory Committee members,
I have discussed the issue regarding my mentorship plan with Dr.... | {
"filename": "bdn2022.ipynb",
"repository": "muzaale/denotas",
"query": "transformed_from_existing",
"size": 300281,
"sha": ""
} |
# process_gre_output_1.ipynb
Repository: pat-jj/GenRES
<code>
import json
filepath_gpt4_turbo = 'results/wiki20m_rand_100_gpt-4-1106-preview_detailed.json'
filepath_gt = 'results/wiki20m_rand_100_groundtruth_detailed.json'
filepath_llama2 = 'results/wiki20m_rand_100_llama-2-70b_detailed.json'
filepath_openchat = 're... | {
"filename": "process_gre_output_1.ipynb",
"repository": "pat-jj/GenRES",
"query": "transformed_from_existing",
"size": 28526,
"sha": ""
} |
# ChIP_TIP.ipynb
Repository: gersteinlab/LatentDAG
<code>
import os
import pandas as pd
import scanpy as sc
</code>
<code>
genes = pd.read_csv("../../result/network_perturb_go/valid_genes", sep="\t")
id2genes = genes.set_index("ID")["genes"].to_dict()
genes2id = genes.set_index("genes")["ID"].to_dict()
genes = genes[... | {
"filename": "ChIP_TIP.ipynb",
"repository": "gersteinlab/LatentDAG",
"query": "transformed_from_existing",
"size": 5707,
"sha": ""
} |
# to_Python_Deitel_01_13.ipynb
Repository: weigeng-valpo/Intro
# 1.13 How Big Is Big Data?
For computer scientists and data scientists, data is now as important as writing programs
* According to IBM, approximately 2.5 quintillion bytes (2.5 _exabytes_) of data are created daily, and 90% of the world’s data was create... | {
"filename": "to_Python_Deitel_01_13.ipynb",
"repository": "weigeng-valpo/Intro",
"query": "transformed_from_existing",
"size": 19770,
"sha": ""
} |
# bioimage_analysis_course_2025_day1_1_instant_gratification_1.ipynb
Repository: brunicardoso/python
# **Instant Gratification**
**Install required packages and specific modules from packages**
<code>
from skimage.io import imread, imsave
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches im... | {
"filename": "bioimage_analysis_course_2025_day1_1_instant_gratification_1.ipynb",
"repository": "brunicardoso/python",
"query": "transformed_from_existing",
"size": 9181,
"sha": ""
} |
# trials_matching.ipynb
Repository: nigat12/ai-treatment-connect
<code>
%pip install openpyxl
</code>
<code>
# --- Setup and Imports ---
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import re # For cleaning text
imp... | {
"filename": "trials_matching.ipynb",
"repository": "nigat12/ai-treatment-connect",
"query": "transformed_from_existing",
"size": 157773,
"sha": ""
} |
# main_1.ipynb
Repository: mpnguyen2/dpo
<code>
%reload_ext autoreload
%autoreload 2
</code>
**TRAINING**
#### Helper functions
<code>
from train import train
import time
def train_helper(env_name, num_optimize_iters, warm_up_threshold, zero_order, save_interval):
start_time = time.time()
train(env_name, nu... | {
"filename": "main_1.ipynb",
"repository": "mpnguyen2/dpo",
"query": "transformed_from_existing",
"size": 17799,
"sha": ""
} |
# parse_trees_1.ipynb
Repository: eaton-lab/toytree
# Tree Parsing (I/O)
Parsing tree data involves loading a tree topology and associated metadata from a serialized text format into a
data structure. `toytree` loads trees from a variety of text formats (Newick, nexus, NHX) stored in a file, URL, or string, and retu... | {
"filename": "parse_trees_1.ipynb",
"repository": "eaton-lab/toytree",
"query": "transformed_from_existing",
"size": 42776,
"sha": ""
} |
# Spatial_Transcriptomics_1.ipynb
Repository: nunososorio/SingleCellGenomics2024
[](https://colab.research.google.com/github/nunososorio/SingleCellGenomics2024/blob/main/5_Friday_April12th/Spatial_Transcriptomics.ipynb)
<img src="https://githu... | {
"filename": "Spatial_Transcriptomics_1.ipynb",
"repository": "nunososorio/SingleCellGenomics2024",
"query": "transformed_from_existing",
"size": 52807,
"sha": ""
} |
# LDA.ipynb
Repository: ankitvgupta/rnaseqtopicmodeling
| {
"filename": "LDA.ipynb",
"repository": "ankitvgupta/rnaseqtopicmodeling",
"query": "transformed_from_existing",
"size": 13766,
"sha": ""
} |
# demo1_2.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_2.ipynb",
"repository": "ZJUFanLab/bulk2space",
"query": "transformed_from_existing",
"size": 56277,
"sha": ""
} |
# scRNAseq_Analysis_PartI_sample8.ipynb
Repository: SchoberLab/YF
# Analysis Part I - Preprocessing Sample 8
<code>
%load_ext autoreload
</code>
<code>
%matplotlib inline
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings(action='ignore')
</code>
<code>
import os... | {
"filename": "scRNAseq_Analysis_PartI_sample8.ipynb",
"repository": "SchoberLab/YF",
"query": "transformed_from_existing",
"size": 22277,
"sha": ""
} |
# rag-qa_2.ipynb
Repository: dair-ai/maven-pe-for-llms-13
# Data-Augmented Question Answering
We are interested to build a personal learning assistant using LangChain. The parts we need:
- user question (input)
- role prompting to mimic learning assistant role
- relevant context obtained via data source
- knowle... | {
"filename": "rag-qa_2.ipynb",
"repository": "dair-ai/maven-pe-for-llms-13",
"query": "transformed_from_existing",
"size": 11935,
"sha": ""
} |
# webscaping_1.ipynb
Repository: redashu/ML
<a href="https://colab.research.google.com/github/redashu/ML/blob/master/webscaping.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<code>
from urllib import request # for downloading data from url
... | {
"filename": "webscaping_1.ipynb",
"repository": "redashu/ML",
"query": "transformed_from_existing",
"size": 46000,
"sha": ""
} |
# example_external_evaluation_pipelines.ipynb
Repository: langfuse/langfuse-docs
---
description: This notebook explains how to build an external evaluation pipeline to measure the performance of your production LLM application using Langfuse
category: Evaluation
---
# Evaluate Langfuse LLM Traces with an External Ev... | {
"filename": "example_external_evaluation_pipelines.ipynb",
"repository": "langfuse/langfuse-docs",
"query": "transformed_from_existing",
"size": 198856,
"sha": ""
} |
# manifest_1.ipynb
Repository: adityakakarla/finetuned-manifest-generation
<code>
from openai import OpenAI
openai_api_key = ''
client = OpenAI(api_key=openai_api_key)
</code>
<code>
def create_manifest(wrapper_script_fp, LSID, author, docker_image, repo, documentation_url, filepath, output_fp='output/manifest'):
... | {
"filename": "manifest_1.ipynb",
"repository": "adityakakarla/finetuned-manifest-generation",
"query": "transformed_from_existing",
"size": 7937,
"sha": ""
} |
# Lecture 09_1.ipynb
Repository: ambujtewari/stats607a-fall2014
| {
"filename": "Lecture 09_1.ipynb",
"repository": "ambujtewari/stats607a-fall2014",
"query": "transformed_from_existing",
"size": 100963,
"sha": ""
} |
# second_level_evaluation-checkpoint_1.ipynb
Repository: thomyks/Automatic-Topic-Extraction-with-BERTopic
<code>
# Topic Diversity
# Describe the details.
</code>
<code>
import pandas as pd
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import nltk
# Ensure NLTK resources are downloaded
n... | {
"filename": "second_level_evaluation-checkpoint_1.ipynb",
"repository": "thomyks/Automatic-Topic-Extraction-with-BERTopic",
"query": "transformed_from_existing",
"size": 88655,
"sha": ""
} |
# gcn_1.ipynb
Repository: dcolinmorgan/gcn
<code>
%reset -f
%config Completer.use_jedi = True
# %matplotlib widget
# from scipy.stats import rankdata
# from sklearn.preprocessing import normalize
# import sklearn.utils as sku
# import plotly.graph_objects as go
# import plotly.express as px
# import chart_studio.plotl... | {
"filename": "gcn_1.ipynb",
"repository": "dcolinmorgan/gcn",
"query": "transformed_from_existing",
"size": 303642,
"sha": ""
} |
# lab_symintro_1.ipynb
Repository: nicollsf/eee2047s-notebooks
# Symbolic math introduction
Symbolic mathematics is a maturing technology that lets a computer do maths using symbolic manipulation rather than numerical computation. Python has support for symbolic computation via the "sympy" package.
The sympy docume... | {
"filename": "lab_symintro_1.ipynb",
"repository": "nicollsf/eee2047s-notebooks",
"query": "transformed_from_existing",
"size": 127913,
"sha": ""
} |
# scripts_Tabula_Muris_MM_2020.ipynb
Repository: xingjiepan/SCMG
<code>
%config InlineBackend.figure_format='retina'
</code>
<code>
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scanpy as sc
from cytofuture_data.gene_name_mapping import GeneNameMapper
</code>
<code>
# Load... | {
"filename": "scripts_Tabula_Muris_MM_2020.ipynb",
"repository": "xingjiepan/SCMG",
"query": "transformed_from_existing",
"size": 8826,
"sha": ""
} |
# RNA_ATAC_paired_scButterfly-C-checkpoint_1.ipynb
Repository: BioX-NKU/scButterfly
# RNA-ATAC: scButterfly-C
The following tutorial demonstrate how to use scButterfly-C variant with data augmentation using MultiVI cluster labels.
scButterfly-C with MultiVI cluster labels data augmentation will generate synthetic p... | {
"filename": "RNA_ATAC_paired_scButterfly-C-checkpoint_1.ipynb",
"repository": "BioX-NKU/scButterfly",
"query": "transformed_from_existing",
"size": 140839,
"sha": ""
} |
# index_1.ipynb
Repository: SheffieldML/notebook
## Sheffield ML Notebooks
This is a repository for the SheffieldML group's notebooks. They are broadly split into three categories.
* [Computational Biology and Bioinformatics](./compbio/index.ipynb) These notebooks are focussed on data analysis and new methodologies ... | {
"filename": "index_1.ipynb",
"repository": "SheffieldML/notebook",
"query": "transformed_from_existing",
"size": 2247,
"sha": ""
} |
# 0.intro.ipynb
Repository: PCHN63101-Advanced-Data-Skills/R-Programming-Language
# Introduction
## Contents
```{tableofcontents}
```
## About the Authors
```{figure} images/george.jpg
---
scale: 80%
align: right
---
```
**Dr George Farmer | PhD**
Lecturer
... Dover Street Building | Division of Psychology, Co... | {
"filename": "0.intro.ipynb",
"repository": "PCHN63101-Advanced-Data-Skills/R-Programming-Language",
"query": "transformed_from_existing",
"size": 2095,
"sha": ""
} |
# speed_benchmark_3.ipynb
Repository: MaayanLab/blitzgsea
# Benchmark GSEA speed
Compare speed of GSEApy, fGSEA, and blitzGSEA. The runtime of fGSEA is calculated in a separate notebook as it runs in an R environment.
<code>
%%capture
!pip3 install git+https://github.com/MaayanLab/blitzgsea.git
</code>
<code>
!pip3... | {
"filename": "speed_benchmark_3.ipynb",
"repository": "MaayanLab/blitzgsea",
"query": "transformed_from_existing",
"size": 334745,
"sha": ""
} |
# association.ipynb
Repository: tmichoel/BioFindrTutorials
## Introduction
While [BioFindr][1] is developed primarily for causal inference from genomics and transcriptomics data, association analysis between genomics and transcriptomics data is also possible. In association analysis, genetic effects on the transcript... | {
"filename": "association.ipynb",
"repository": "tmichoel/BioFindrTutorials",
"query": "transformed_from_existing",
"size": 6848,
"sha": ""
} |
# P6_2019_web_nn_101.ipynb
Repository: krzakala/ml
# Classification sur MNIST avec un reseau de neuronnes
<code>
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop
%mat... | {
"filename": "P6_2019_web_nn_101.ipynb",
"repository": "krzakala/ml",
"query": "transformed_from_existing",
"size": 135978,
"sha": ""
} |
# idea_Immunology1_1.ipynb
Repository: Elizaluckianchikova/Bioinformatics
<a href="https://colab.research.google.com/github/Elizaluckianchikova/Bioinformatics_idea/blob/main/Immunology1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**Моделиронива... | {
"filename": "idea_Immunology1_1.ipynb",
"repository": "Elizaluckianchikova/Bioinformatics",
"query": "transformed_from_existing",
"size": 38993,
"sha": ""
} |
# Base-02-GRN_preparation_for_CellOracle.ipynb
Repository: tmnolan/Brassinosteroid-gene-regulatory-networks-at-cellular-resolution
<code>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import os, sys, shutil, importlib, glob
from tqdm.notebook import ... | {
"filename": "Base-02-GRN_preparation_for_CellOracle.ipynb",
"repository": "tmnolan/Brassinosteroid-gene-regulatory-networks-at-cellular-resolution",
"query": "transformed_from_existing",
"size": 18617,
"sha": ""
} |
# lauzhacktest.ipynb
Repository: ihchaeryu/LauzHack23-RHR
<code>
pip install openai==0.28
</code>
<code>
pip install tiktoken
</code>
<code>
import numpy as np
import scipy
import matplotlib.pyplot as plt
import openai
import pandas as pd
import tiktoken
</code>
<code>
openai.api_key = 'sk-0HIcafBff8Za8KFYiTdTT3Blb... | {
"filename": "lauzhacktest.ipynb",
"repository": "ihchaeryu/LauzHack23-RHR",
"query": "transformed_from_existing",
"size": 31023,
"sha": ""
} |
# Traveling-Waves-nb.ipynb
Repository: Hallatscheklab/PAM
# Traveling waves
Now that we have a basic understanding of the stochastic dynamics of growing populations, we would like to embed the population in space. In the simplest case, we can imagine that each particle is merely diffusing along a line, without any a... | {
"filename": "Traveling-Waves-nb.ipynb",
"repository": "Hallatscheklab/PAM",
"query": "transformed_from_existing",
"size": 347696,
"sha": ""
} |
# Career.ipynb
Repository: annanya-mathur/Career-Prediction
<code>
!pip install pandas
!pip install sklearn
!pip install xlrd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
... | {
"filename": "Career.ipynb",
"repository": "annanya-mathur/Career-Prediction",
"query": "transformed_from_existing",
"size": 36414,
"sha": ""
} |
# project_drug.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.rando... | {
"filename": "project_drug.ipynb",
"repository": "satish2705/major",
"query": "transformed_from_existing",
"size": 46092,
"sha": ""
} |
# ml_basics_1.ipynb
Repository: timeowilliams/Responsible-ai
<code>
pip install numpy scikit-learn
</code>
<code>
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
# Sample training data
papers = [
"COVID vaccine clinical trials", ... | {
"filename": "ml_basics_1.ipynb",
"repository": "timeowilliams/Responsible-ai",
"query": "transformed_from_existing",
"size": 19006,
"sha": ""
} |
# 00_Setup_1.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 *
... | {
"filename": "00_Setup_1.ipynb",
"repository": "tebe-nigrelli/MMN-Group-Project",
"query": "transformed_from_existing",
"size": 127400,
"sha": ""
} |
# qc_rna-10xv3.ipynb
Repository: pachterlab/voyagerpy
# Basic quality control on scRNA-seq data wih 10X v3
<code>
!git clone https://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@github.com/cellatlas/cellatlas.git > /dev/null
!pip install --quiet git+https://github.com/pmelsted/voyagerpy
</code>
<code>
!pip install --qu... | {
"filename": "qc_rna-10xv3.ipynb",
"repository": "pachterlab/voyagerpy",
"query": "transformed_from_existing",
"size": 330759,
"sha": ""
} |
# llmops_with_langsmith_1.ipynb
Repository: buzzbing/llmops-platforms
### Configuration Requirements:
Definition of Environment Variables
- OPENAI_API_KEY= [openai api key]
- OPENAI_ORGANIZATION=[organization key]
- LANGCHAIN_TRACING_V2=true
- LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
- LANGCHAIN_API_KEY=... | {
"filename": "llmops_with_langsmith_1.ipynb",
"repository": "buzzbing/llmops-platforms",
"query": "transformed_from_existing",
"size": 26285,
"sha": ""
} |
# rds2h5ad_1.ipynb
Repository: jiang-junyao/DRCTdb
<code>
import scanpy as sc
</code>
<code>
sample1 = sc.read_h5ad('../../data/scATAC-seq/Sample1/Rds/sample1_scATAC-seq_80k_processed.h5ad')
</code>
<code>
print(sample1)
len(sample1.obs['cell_type'].value_counts())
</code>
<code>
sample4 = sc.read_h5ad('../../data/... | {
"filename": "rds2h5ad_1.ipynb",
"repository": "jiang-junyao/DRCTdb",
"query": "transformed_from_existing",
"size": 21542,
"sha": ""
} |
# graph-prac.ipynb
Repository: kortschak/graphprac
# Graphical analysis of protein interactions in yeast
The aim of this practical is to examine some uses of graphical analysis in a biological setting. The analyses are identical to those demonstrated in the London Tube Graph examples in the introduction to systems bi... | {
"filename": "graph-prac.ipynb",
"repository": "kortschak/graphprac",
"query": "transformed_from_existing",
"size": 17538,
"sha": ""
} |
# informed_binn-sandbox.ipynb
Repository: loucerac/robustness
<code>
# %%
# https://www.sc-best-practices.org/conditions/gsea_pathway.html#id380
# Kang HM, Subramaniam M, Targ S, et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
# Nat Biotechnol. 2020 Nov;38(11):1356]. Nat Biotech... | {
"filename": "informed_binn-sandbox.ipynb",
"repository": "loucerac/robustness",
"query": "transformed_from_existing",
"size": 91206,
"sha": ""
} |
# notes_gan_1.ipynb
Repository: xiptos/is
[](https://colab.research.google.com/github/xiptos/is_notes/blob/main/gan.ipynb)
# Generative Adversarial Networks (GAN)
Are based on a strategy where two different deep networks are pitted against one... | {
"filename": "notes_gan_1.ipynb",
"repository": "xiptos/is",
"query": "transformed_from_existing",
"size": 14252,
"sha": ""
} |
# 2.SOG_3.ipynb
Repository: xiaojierzi/iSORT
<code>
from iSORTlib import *
seed_everything(20)
</code>
# Tutorial for finding spatial-organzing genes (SOGs) and performing in silico knockouts
## 1. Load data
### Set directories
<code>
sc_data_dir = '151674_data.csv'
sc_meta_dir = '151674_meta.csv'
st_data_dir = '1... | {
"filename": "2.SOG_3.ipynb",
"repository": "xiaojierzi/iSORT",
"query": "transformed_from_existing",
"size": 217537,
"sha": ""
} |
# plotly_2.ipynb
Repository: siobhon-egan/internship
# Visualizing bioinformatics data with plot.ly
This notebook is used for visualising the quality scores of each sample.
It generates an interactive graph, one per sample of all the seqeuences generated from that sample.
https://plot.ly/~johnchase/22/visualizing-bi... | {
"filename": "plotly_2.ipynb",
"repository": "siobhon-egan/internship",
"query": "transformed_from_existing",
"size": 24217,
"sha": ""
} |
# Exploration_1.ipynb
Repository: krassowski/multi-omics-state-of-the-field
**Aims**:
- list high-impact works to aid navigation of the field
- check for unexpectedly common authors/affiliations/journals to screening for potential false-positive matches (see the Integromics and Panomics companies)
<code>
%run noteb... | {
"filename": "Exploration_1.ipynb",
"repository": "krassowski/multi-omics-state-of-the-field",
"query": "transformed_from_existing",
"size": 284352,
"sha": ""
} |
# basic_usage_2.ipynb
Repository: estorrs/enrichrpy
<code>
import enrichrpy.enrichr as een
import enrichrpy.plotting as epl
</code>
define some test genes
<code>
genes = [
'TYROBP',
'HLA-DRA',
'SPP1',
'LAPTM5',
'C1QB',
'FCER1G',
'GPNMB',
'FCGR3A',
'RGS1',
'HLA-DPA1',
'ITGB... | {
"filename": "basic_usage_2.ipynb",
"repository": "estorrs/enrichrpy",
"query": "transformed_from_existing",
"size": 55112,
"sha": ""
} |
# Prediction_Churn_Analysis_R.ipynb
Repository: RajEshwariCodes/Churn
<code>
import pandas as pd
import numpy as np
</code>
<code>
df=pd.read_csv("/content/drive/MyDrive/churn.csv")
</code>
<code>
df.head()
</code>
<code>
df.drop("customer_id",axis=1,inplace=True)
</code>
<code>
df
</code>
<code>
df.info() #struc... | {
"filename": "Prediction_Churn_Analysis_R.ipynb",
"repository": "RajEshwariCodes/Churn",
"query": "transformed_from_existing",
"size": 184524,
"sha": ""
} |
# wright_fisher-hints_1.ipynb
Repository: sparkingdark/dataset-test
This is an ipython notebook. Lectures about Python, useful both for beginners and experts, can be found at http://scipy-lectures.github.io.
I recommend installing the [Anaconda](https://store.continuum.io/cshop/academicanaconda) distribution. Make su... | {
"filename": "wright_fisher-hints_1.ipynb",
"repository": "sparkingdark/dataset-test",
"query": "transformed_from_existing",
"size": 44935,
"sha": ""
} |
# num-methods-E.ipynb
Repository: subblue/applying-maths-book
# 8 Reaction schemes with feedback. Predator -Prey (Lotka-Volterra) & Nerve impulses (Fitzhugh-Nagumo) equations.
## Introduction
Feedback in a chemical reaction implies that there are at least two reactions for which the product of one is the reactant fo... | {
"filename": "num-methods-E.ipynb",
"repository": "subblue/applying-maths-book",
"query": "transformed_from_existing",
"size": 25861,
"sha": ""
} |
# R-api.ipynb
Repository: kipoi/kipoi
# Using Kipoi from R
Thanks to the [reticulate](https://github.com/rstudio/reticulate) R package from RStudio, it is possible to easily call python functions from R. Hence one can use kipoi python API from R. This tutorial will show how to do that.
Make sure you have git-lfs and... | {
"filename": "R-api.ipynb",
"repository": "kipoi/kipoi",
"query": "transformed_from_existing",
"size": 62325,
"sha": ""
} |
# Deseq2_R.ipynb
Repository: Mangul-Lab-USC/RNA-SEQ-Tutorial-PART1
<code>
?system
version
</code>
---
Analyze gene count data using Deseq2
---
<code>
install.packages("rgl", repos = "http://cran.rstudio.com/")
install.packages("ConsRank", repos = "http://cran.rstudio.com/")
library("ConsRank")
</code>
<code>... | {
"filename": "Deseq2_R.ipynb",
"repository": "Mangul-Lab-USC/RNA-SEQ-Tutorial-PART1",
"query": "transformed_from_existing",
"size": 214471,
"sha": ""
} |
# Get_ICD_11.ipynb
Repository: dkisselev-zz/mmc-pipeline
<a href="https://colab.research.google.com/github/dkisselev-zz/mmc-pipeline/blob/main/Get_ICD_11.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<code>
!pip install pymed google-generativeai ... | {
"filename": "Get_ICD_11.ipynb",
"repository": "dkisselev-zz/mmc-pipeline",
"query": "transformed_from_existing",
"size": 107876,
"sha": ""
} |
# Slicing.ipynb
Repository: sagar87/spatialproteomics
# Subselecting Data
<code>
%reload_ext autoreload
%autoreload 2
import spatialproteomics
import pandas as pd
import xarray as xr
xr.set_options(display_style="text")
</code>
One of the key features of `spatialproteomics` is the ability to slice our image data q... | {
"filename": "Slicing.ipynb",
"repository": "sagar87/spatialproteomics",
"query": "transformed_from_existing",
"size": 39343,
"sha": ""
} |
# Make_a_chatbot_from_scratch.ipynb
Repository: insaid2018/project-gallery
<center><img src="https://github.com/insaid2018/Term-1/blob/master/Images/INSAID_Full%20Logo.png?raw=true" width="25%" /></center>
# <center><b>Making of a simple interactive chatbot<b></center>
# **Table of Contents**
---
**1.** [**Problem ... | {
"filename": "Make_a_chatbot_from_scratch.ipynb",
"repository": "insaid2018/project-gallery",
"query": "transformed_from_existing",
"size": 107147,
"sha": ""
} |
# feature_selection.ipynb
Repository: shumshersubashgautam/Single-Cell-Mapping-Computational-Biology
(pre-processing:feature-selection)=
# Feature selection
## Motivation
We now have a normalized data representation that still preserves biological heterogeneity but with reduced technical sampling effects in gene exp... | {
"filename": "feature_selection.ipynb",
"repository": "shumshersubashgautam/Single-Cell-Mapping-Computational-Biology",
"query": "transformed_from_existing",
"size": 144632,
"sha": ""
} |
# T5_1.ipynb
Repository: LaurentVeyssier/Abstractive-Summarization-using-colab-and-T5-model
<code>
!pip install transformers
!pip install tensorflow==2.1
from transformers import pipeline
</code>
<code>
summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
summarizer("The US h... | {
"filename": "T5_1.ipynb",
"repository": "LaurentVeyssier/Abstractive-Summarization-using-colab-and-T5-model",
"query": "transformed_from_existing",
"size": 72928,
"sha": ""
} |
# Pubmed.ipynb
Repository: Aitslab/BioNLP
<code>
from docria import Document, DataTypes as T, NodeSpan, set_large_screen, MsgpackCodec, MsgpackDocument
from docria.storage import MsgpackDocumentIO, MsgpackDocumentReader, MsgpackDocumentWriter
from lxml import etree
import regex as re
</code>
## Import
<code>
%%sh
zc... | {
"filename": "Pubmed.ipynb",
"repository": "Aitslab/BioNLP",
"query": "transformed_from_existing",
"size": 100709,
"sha": ""
} |
# EnrichrConsensus_1.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... | {
"filename": "EnrichrConsensus_1.ipynb",
"repository": "MaayanLab/appyter-catalog",
"query": "transformed_from_existing",
"size": 23367,
"sha": ""
} |
# main.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 spac... | {
"filename": "main.ipynb",
"repository": "ZenVInnovations/9.-enhancing-text-analytics-data-quality-with-nlp---d24f3a13",
"query": "transformed_from_existing",
"size": 35326,
"sha": ""
} |
# Comparison_in_single-cell_data-checkpoint_3.ipynb
Repository: Velcon-Zheng/DL-mo
# 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 joi... | {
"filename": "Comparison_in_single-cell_data-checkpoint_3.ipynb",
"repository": "Velcon-Zheng/DL-mo",
"query": "transformed_from_existing",
"size": 12497,
"sha": ""
} |
# Report.ipynb
Repository: 27410/group-assingment-team11
# Biosynthesis of Isoamyl Acetate by Saccharomyces cerevisiae
## 1. Introduction
### 1.1 Literature review of the compound (<500 words)
### Overview on the product
<figure style="float: right; margin-left: 20px; text-align: center;">
<img src="Pictures/is... | {
"filename": "Report.ipynb",
"repository": "27410/group-assingment-team11",
"query": "transformed_from_existing",
"size": 34472,
"sha": ""
} |
# CopyNumberEstimation-checkpoint.ipynb
Repository: dpeerlab/MitoEJ-paper-analysis
# Copy Number Estimation
This notebook estimates mtDNA copy number using the method developed at SAIL. It is based on the assumption that scATAC-seq samples open nuclear DNA at the same rate as mitchondrial DNA, which is open everywher... | {
"filename": "CopyNumberEstimation-checkpoint.ipynb",
"repository": "dpeerlab/MitoEJ-paper-analysis",
"query": "transformed_from_existing",
"size": 97739,
"sha": ""
} |
# Adder.ipynb
Repository: BDR-Pro/QuiziWiki
<code>
# Connect to MongoDB
from pymongo.mongo_client import MongoClient
from pymongo.server_api import ServerApi
from dotenv import load_dotenv
import wikipedia as wp
import os
# Load environment variables from .env file
load_dotenv()
# Access an environment variable
pass... | {
"filename": "Adder.ipynb",
"repository": "BDR-Pro/QuiziWiki",
"query": "transformed_from_existing",
"size": 29087,
"sha": ""
} |
# v2_10k Immunomes Source Code .ipynb
Repository: buttelab/10kimmunomes
<font size=8 color="gray">10k Immunomes Source Code</font>
**Welcome to the 10k Immunomes Project**
This source code creates the [10k immunomes website](http://10kimmunomes.ucsf.edu/).
This `.ipynb` file is the **only** place where code is writ... | {
"filename": "v2_10k Immunomes Source Code .ipynb",
"repository": "buttelab/10kimmunomes",
"query": "transformed_from_existing",
"size": 120670,
"sha": ""
} |
# Code.ipynb
Repository: wozniakw2002/WB-2024
<code>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
import random
import warnings
warnings.filterwarnings('ignore')
# from points_io import save_points_as_pdb
# pd.options.mode.copy_on_write = True
</cod... | {
"filename": "Code.ipynb",
"repository": "wozniakw2002/WB-2024",
"query": "transformed_from_existing",
"size": 59598,
"sha": ""
} |
# Plant_Pathogen_Atlas_imputation_part0.ipynb
Repository: amonell/Spatial
<code>
library(Seurat)
library(Matrix)
</code>
<code>
scrnaseq_r <- readRDS('../../data/AvrRpt2_alone2.rds')
</code>
<code>
dior::write_h5(scrnaseq_r, file="../../data/AvrRpt2_alone2.h5", object.type = 'seurat')
</code>
<code>
DefaultAssay(sc... | {
"filename": "Plant_Pathogen_Atlas_imputation_part0.ipynb",
"repository": "amonell/Spatial",
"query": "transformed_from_existing",
"size": 2295,
"sha": ""
} |
# 251124sepsis Panels.ipynb
Repository: TanyaJohary/sepsis-diagnosis
## Pathogen-based panels:
The article "In vitro diagnosis of sepsis: a review" focuses on the advancements and challenges in diagnosing sepsis through in vitro diagnostic methods. It addresses the limitations of traditional culture-based approaches ... | {
"filename": "251124sepsis Panels.ipynb",
"repository": "TanyaJohary/sepsis-diagnosis",
"query": "transformed_from_existing",
"size": 17292,
"sha": ""
} |
# 01_genes.ipynb
Repository: galicae/comandos
# genes
> Prepare and annotate genes and gene sets.
<code>
# | default_exp genes
</code>
<code>
# | hide
%load_ext autoreload
%autoreload 2
</code>
<code>
# | export
import os
from typing import Union
import anndata as ad
import numpy as np
import pandas as pd
</code... | {
"filename": "01_genes.ipynb",
"repository": "galicae/comandos",
"query": "transformed_from_existing",
"size": 29453,
"sha": ""
} |
# OLINK_preproc.ipynb
Repository: eduff/BIACOB
<code>
import pickle as pkl
import pandas
import pandas as pd
from pandas.api.types import is_numeric_dtype
from pandas.api.types import is_bool_dtype
import numpy as np
import pickle as pkl
import os,re
import matplotlib.pyplot as plt
import scipy.stats as stats
import ... | {
"filename": "OLINK_preproc.ipynb",
"repository": "eduff/BIACOB",
"query": "transformed_from_existing",
"size": 10351,
"sha": ""
} |
# Annotation_challenging_marker_gene_heatmaps.ipynb
Repository: AllonKleinLab/paper-data
# Recreate heatmaps of the Immunity paper with newly defined population (but SAME genes as in that paper)
## Import statements
<code>
import os,sys
import datetime
</code>
<code>
import scanpy as sc
sc.logging.print_versions()... | {
"filename": "Annotation_challenging_marker_gene_heatmaps.ipynb",
"repository": "AllonKleinLab/paper-data",
"query": "transformed_from_existing",
"size": 263326,
"sha": ""
} |
# 4_1.ipynb
Repository: IsidoraJevremovic/osnovi-astronomije
<a href="https://colab.research.google.com/github/IsidoraJevremovic/osnovi-astronomije/blob/main/4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<code>
pip install ephem
</code>
<code>... | {
"filename": "4_1.ipynb",
"repository": "IsidoraJevremovic/osnovi-astronomije",
"query": "transformed_from_existing",
"size": 3483,
"sha": ""
} |
# Brain_Mice_testing_functions_1.ipynb
Repository: shappiron/Aging
<code>
# If you are a developer, you may want to reload the packages on a fly.
# Jupyter has a magic for this particular purpose:
%load_ext autoreload
%autoreload 2
#data analysis libs
import numpy as np
import pandas as pd
pd.set_option('display.max... | {
"filename": "Brain_Mice_testing_functions_1.ipynb",
"repository": "shappiron/Aging",
"query": "transformed_from_existing",
"size": 220086,
"sha": ""
} |
# project_index_1.ipynb
Repository: qwjaklj/scholarship
## Introduction
As a cloud of climate change continues getting closer every year, understanding the complex dynamics behind regional temperature disparities becomes more important. This paper sets out to unravel them by taking an area specific to the forecast bet... | {
"filename": "project_index_1.ipynb",
"repository": "qwjaklj/scholarship",
"query": "transformed_from_existing",
"size": 4163,
"sha": ""
} |
# Career_2.ipynb
Repository: annanya-mathur/Career-Prediction
<code>
!pip install pandas
!pip install sklearn
!pip install xlrd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegresso... | {
"filename": "Career_2.ipynb",
"repository": "annanya-mathur/Career-Prediction",
"query": "transformed_from_existing",
"size": 36414,
"sha": ""
} |
# CustomDB_MTG_Taxa_Profiling_v1.0-checkpoint_1.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/article... | {
"filename": "CustomDB_MTG_Taxa_Profiling_v1.0-checkpoint_1.ipynb",
"repository": "new-atlantis-labs/Metagenomics",
"query": "transformed_from_existing",
"size": 22671,
"sha": ""
} |
# Workflows_WERONIKA_JASKOWIAK_day_1_2.ipynb
Repository: weronikajaskowiak/Comp
# Computational Workflows for biomedical data
Welcome to the course Computational Workflows for Biomedical Data. Over the next two weeks, you will learn how to leverage nf-core pipelines to analyze biomedical data and gain hands-on experi... | {
"filename": "Workflows_WERONIKA_JASKOWIAK_day_1_2.ipynb",
"repository": "weronikajaskowiak/Comp",
"query": "transformed_from_existing",
"size": 283504,
"sha": ""
} |
# RAG_1.ipynb
Repository: robbarto2/GenAI-Foundations
# Retrieval augmented generation (RAG)
## Loading Documents
A first step in RAG is to load document. You need a loader that supports the document type you are interested in. We use in this example Langchain, because it includes a collection of 60+ libraries for mu... | {
"filename": "RAG_1.ipynb",
"repository": "robbarto2/GenAI-Foundations",
"query": "transformed_from_existing",
"size": 43733,
"sha": ""
} |
# 6a_Run_ChromVAR_new_snATAC_2.ipynb
Repository: Gaulton-Lab/non-diabetic-islet-multiomics
#### Summary:
This is a notebook to run chromvar on peaks derived from scATAC-seq stored in a Seurat object. This can be reworked to use peaks not entered into a Seurat object. ChromVAR needs 3 inputs. 1) A count matrix tha... | {
"filename": "6a_Run_ChromVAR_new_snATAC_2.ipynb",
"repository": "Gaulton-Lab/non-diabetic-islet-multiomics",
"query": "transformed_from_existing",
"size": 80757,
"sha": ""
} |
# stat.ipynb
Repository: ElinaZhang0721/DeepTarget-NLP
## Data Prepare and Clean
<code>
import pandas as pd
import re
# Load the XLSX file
file_path = 'C:/Users/yufei/Programming/DeepTarget/web_scrape/scraped_data.xlsx'
data = pd.read_excel(file_path)
data.head()
</code>
<code>
from bs4 import BeautifulSoup
# Rem... | {
"filename": "stat.ipynb",
"repository": "ElinaZhang0721/DeepTarget-NLP",
"query": "transformed_from_existing",
"size": 50596,
"sha": ""
} |
# Job_Analysis_Doc2Vec_Analysis.ipynb
Repository: jgroth1/NLP
<code>
import json
import os
from bs4 import BeautifulSoup
import re
from nltk.stem import WordNetLemmatizer
from nltk import sent_tokenize, wordpunct_tokenize, pos_tag
from nltk.corpus import stopwords
import nltk
from gensim.models.doc2vec import TaggedDo... | {
"filename": "Job_Analysis_Doc2Vec_Analysis.ipynb",
"repository": "jgroth1/NLP",
"query": "transformed_from_existing",
"size": 12990,
"sha": ""
} |
# C.elegans_demo_1.ipynb
Repository: kunwang34/PhyloVelo
# Run PhyloVelo in C.elegans data
We next sought to benchmark PhyloVelo by applying to the phylogeny-resolved scRNA-seq data of C. elegans. The embryonic lineage tree of C. elegans is entirely known. Moreover, time-course single-cell RNA-seq data from C. elegan... | {
"filename": "C.elegans_demo_1.ipynb",
"repository": "kunwang34/PhyloVelo",
"query": "transformed_from_existing",
"size": 247265,
"sha": ""
} |
# x.ipynb
Repository: ArhamNaeem/Timetable-Project
<code>
import pandas as pd
</code>
<code>
teachers = pd.read_csv('teachers.csv')
rooms= pd.read_csv('room.csv')
classes = pd.read_csv('class.csv')
</code>
<code>
teachers
</code>
<code>
rooms
</code>
<code>
classes
</code>
<code>
timetable = {}
</code>
| {
"filename": "x.ipynb",
"repository": "ArhamNaeem/Timetable-Project",
"query": "transformed_from_existing",
"size": 10524,
"sha": ""
} |
# analysis_Ratz2022Clonal_2.ipynb
Repository: yuanzhiyuan/SODB
Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics
ShortName: Ratz2022Clonal
Steps of processing the data from raw to Anndata:
<code>
# 1, Download the raw data from GSE153424
</code>
<code>
# 2, Unzip the .tar file... | {
"filename": "analysis_Ratz2022Clonal_2.ipynb",
"repository": "yuanzhiyuan/SODB",
"query": "transformed_from_existing",
"size": 2832,
"sha": ""
} |
# in_BioMedical_Ask_BioRxiv_1.ipynb
Repository: compu-flair/LLMs
<code>
# installs
#pip install langchain tiktoken openai langchainhub chromadb
</code>
<code>
import requests
import json
from bs4 import BeautifulSoup
import os
</code>
<code>
## change the
query = "single cell RNA sequencing"
print(type(query))
... | {
"filename": "in_BioMedical_Ask_BioRxiv_1.ipynb",
"repository": "compu-flair/LLMs",
"query": "transformed_from_existing",
"size": 15844,
"sha": ""
} |
# Submodule08_Differential_Analysis_Proteomics_2.ipynb
Repository: NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery
<img src="images/RIINBRE-Logo.jpg" width="400" height="400"><img src="images/MIC_Logo.png" width="600" height="600">
# Analysis of Biomedical Data for Biomarker Discovery
<a id="top8"></a>
## S... | {
"filename": "Submodule08_Differential_Analysis_Proteomics_2.ipynb",
"repository": "NIGMS/Analysis-of-Biomedical-Data-for-Biomarker-Discovery",
"query": "transformed_from_existing",
"size": 50848,
"sha": ""
} |
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