These scripts aims to calculate the Gigabase output from all sequencers we have. from interop import py_interop_run, py_interop_run_metrics, py_interop_summary def get_gigabase(bcl_path): # D...

Recently I am trying Agendo API and want to record the testing code here, because the documentation on Agendo’s site isn’t updated. import json import requests import base64 # Agendo API base pat...

In machine learning and artificial intelligence workflows, the H5 file format is widely used for storing massive datasets and matrices. H5 files, based on the HDF5 format, are efficient for both re...

In the fast-paced world of deep learning, monitoring your model’s performance during training is crucial for understanding how well it is learning, diagnosing potential issues, and optimizing the t...

When training neural networks, you might notice that batch sizes and the number of nodes in layers often align with powers of two. But why is this the case? The answer lies in how GPUs, the engines...

When building dense neural networks, one of the foundational steps you can take to ensure your model trains effectively is input normalization. This seemingly simple preprocessing step can have a p...

In recent years, the landscape for scientific computing tools has shifted, especially in research environments at large institutions and corporations. Anaconda, which was once the go-to distributio...

In today’s data-driven world, visualizing high-dimensional data is a crucial step in exploratory data analysis. One of the most powerful techniques for this is t-SNE (t-distributed Stochastic Neigh...

As a new researcher in biology, you’ve probably come across the term bioRxiv (pronounced “bio-archive”). It’s one of the most talked-about platforms in modern biology, but what exactly is it, and w...

NVIDIA is a household name in the tech world, renowned for producing some of the most powerful Graphics Processing Units (GPUs) available today. Whether you’re a gamer, a professional designer, a r...