Published September 25, 2024 | Version v1

Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening

  • 1. ROR icon University of Basel
  • 2. ROR icon ETH Zurich
  • 3. ROR icon Swiss Nanoscience Institute

Description

Overview

Data and Results used in the publication entitled "Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening"

Data

This folder contains the raw data used during this work.
`EvoEF.csv` contains information on the library used (sequences, number of mutations, etc.) and the fitness (energy) used as continuous mean values. `mut.csv` contains the information about the combinatorial scaling (N vs N_norm), the number of mutations (m) and the probability of each variant using different distributions (uniform and binomial) at different $p_{WT}$.
For further details on how the fitness values were calculated and how the combinatorial scale works, please refer to our prevoius [Paper](https://arxiv.org/abs/2405.05167).
 

Results

This folder contains the results (outputs) of all scripts used. Such results are included in the form of `.npy` and `.npz` files. To load such files with numpy you should include the option `allow_pickle=True`.

Files

HMLC.zip

Files (108.3 MB)

Name Size Download all
md5:05e1aa6a50e3d704f25a5811b53d0128
108.3 MB Preview Download

Additional details

Software

Repository URL
https://github.com/Nash-Lab/HMLC
Programming language
Python