Homolog.us – frontier in bioinformatics

This is a follow-up of my series on AI/ML for biologists.

This is a follow-up of yesterday’s post titled “High-schooler Builds Test Program to Understand Genomic AI Papers”.

In earlier blog posts, we explored how AI models are being used for protein folding and DNA language modeling.

Hello everyone, Last year I wrote several posts on the large language models (LLM) of DNA sequences (evo/evo2 papers), and some readers emailed me asking about the plans to continue the series.

In late September, I started posting a set of tutorials to help biologists (with some Python skills) and bioinformaticians quickly master machine learning and AI concepts.

On Saturday, I ran Alphafold for the first time.

image: https://images.unsplash.com/photo-1538332576228-eb5b4c4de6f5?

Alright class !

In part (i) of this guide, you learned to use Python library Scikit-learn for machine learning.

It is game over for geneticists and genome scientists.

These days, many biologists are trying to learn machine learning and artificial intelligence.

In a classic scene from Idiocracy, we see the citizens of a fictional United States from year 2500 repeatedly insisting that Brawndo is the “thirst mutilator” because it contains electrolytes, while water belongs only in the toilet.

This is a continuation of my earlier posts here, here and here.

This is a continuation of my earlier posts here and here.

The Internet revolution never truly arrived in scientific publishing.

Clearly the current publishing model is utterly dysfunctional.

The publication system in biology is mindblowingly ridiculous as often pointed out by Mike Eisen.

These days, corporate entities are increasingly dominating research in basic science.

I came across an interesting blog post titled Biology is Messy.

Right now everyone is either enamored of or is completely turned off by AI.

On the subject of reconstructing gene regulatory networks (GRNs) from RNAseq and scRNAseq data, I am working through a number of review papers and the techniques described therein.

In an earlier post, I divided the modern era of genetics into 18-year periods (eras).