Simon Duerr duerrsimon

## heroicons_finetune.py

import itertools
from dataclasses import dataclass
from pathlib import Path

import modal
from fastapi import FastAPI
from fastapi.responses import FileResponse


## ipsae.py
# ipsae.py
# script for calculating the ipSAE score for scoring pairwise protein-protein interactions in AlphaFold2 and AlphaFold3 models
# https://www.biorxiv.org/content/10.1101/2025.02.10.637595v1

# Also calculates:
#    pDockQ: Bryant, Pozotti, and Eloffson. https://www.nature.com/articles/s41467-022-28865-w
#    pDockQ2: Zhu, Shenoy, Kundrotas, Elofsson. https://academic.oup.com/bioinformatics/article/39/7/btad424/7219714
#    LIS: Kim, Hu, Comjean, Rodiger, Mohr, Perrimon. https://www.biorxiv.org/content/10.1101/2024.02.19.580970v1

# Roland Dunbrack

## index.html
<!doctype html>
<html lang="en">
	<head>
		<meta charset="utf-8">
		<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">

		<title>reveal.js</title>

		<link rel="stylesheet" href="dist/reset.css">
		<link rel="stylesheet" href="dist/reveal.css">

## script.js


const App = new Vue({
  el: '#app',
  components: {
  },
  data: {
    title: "",
    dynamic_para: "This sentence is generated dynamically.",
    selected_items: [],

## chimeramovie.cxc
# this script shows a few residues and then records a movie changing the isovalue for the loaded cube file

show #1:187
show #1:92
show #1:94
show #1:117
show #1:185
show #1:8
show #1:162
show #1:2

## apt.txt
libgl1-mesa-dev
xvfb

## environment.yml
name: plotmol-example
channels:
- conda-forge
- simonboothroyd
dependencies:
- plotmol

## environment.yml
name: plotmol-example
channels:
- conda-forge
- simonboothroyd
dependencies:
- plotmol

## environment.yml
name: p3d

dependencies:
  - py3Dmol

## BaggingCV_ToyModel
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import BaggingRegressor


#Integrate pipeline to preprocess data in features and Y and perform regression

# Either predict: individual Scores such as 1.UPPER_EXTREMITY_REFLEX_ACTIVITY
# or predict the UPPER EXTREMITY SCORE (FMA-UE)(/66)

	import itertools
	from dataclasses import dataclass
	from pathlib import Path

	import modal
	from fastapi import FastAPI
	from fastapi.responses import FileResponse
	# ipsae.py
	# script for calculating the ipSAE score for scoring pairwise protein-protein interactions in AlphaFold2 and AlphaFold3 models
	# https://www.biorxiv.org/content/10.1101/2025.02.10.637595v1

	# Also calculates:
	# pDockQ: Bryant, Pozotti, and Eloffson. https://www.nature.com/articles/s41467-022-28865-w
	# pDockQ2: Zhu, Shenoy, Kundrotas, Elofsson. https://academic.oup.com/bioinformatics/article/39/7/btad424/7219714
	# LIS: Kim, Hu, Comjean, Rodiger, Mohr, Perrimon. https://www.biorxiv.org/content/10.1101/2024.02.19.580970v1

	# Roland Dunbrack
	<!doctype html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">

	<title>reveal.js</title>

	<link rel="stylesheet" href="dist/reset.css">
	<link rel="stylesheet" href="dist/reveal.css">


	const App = new Vue({
	el: '#app',
	components: {
	},
	data: {
	title: "",
	dynamic_para: "This sentence is generated dynamically.",
	selected_items: [],
	# this script shows a few residues and then records a movie changing the isovalue for the loaded cube file

	show #1:187
	show #1:92
	show #1:94
	show #1:117
	show #1:185
	show #1:8
	show #1:162
	show #1:2
	name: plotmol-example
	channels:
	- conda-forge
	- simonboothroyd
	dependencies:
	- plotmol
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.linear_model import LinearRegression
	from sklearn.ensemble import BaggingRegressor


	#Integrate pipeline to preprocess data in features and Y and perform regression

	# Either predict: individual Scores such as 1.UPPER_EXTREMITY_REFLEX_ACTIVITY
	# or predict the UPPER EXTREMITY SCORE (FMA-UE)(/66)