About ReadingFrame
Who I Am
I'm a high school researcher based in Bentonville, Arkansas. I got into ML and bioinformatics a few years ago and it kind of took over — graph neural networks, gene expression, computational approaches to problems that are still genuinely hard.
Mostly Python, Google Colab, GitHub. No university affiliation. Just someone who got pulled in and kept going.
Why ReadingFrame
The name comes from biology — a reading frame is how a nucleotide sequence gets parsed into codons. Shift by one base and you're reading something completely different. That's roughly the idea here: the same paper, read by someone who's actually running the models, hits differently than a press release or an abstract.
I started this because the middle ground didn't really exist. Journalism tends to smooth things over. Academic commentary is written for academics. I wanted to write about methods, limitations, and actual significance, without either of those constraints.
One post a month. No ads, no affiliate links.
Research Interests
Cancer Biology & AI
ML models applied to oncology — drug response, subtype classification, treatment outcomes. Most of what I read ends up here.
Graph Neural Networks
GNNs for biological networks: protein interactions, gene regulatory graphs, pathway structure. The geometry of biology is underexplored.
Gene Expression Analysis
RNA-seq pipelines, differential expression. What transcriptomic data actually tells you, and where it stops being reliable.
ML for Biomedicine
Transformers, GCNs, survival models on clinical and genomic data. The validation gap between papers and clinical use is the part that keeps me up.
Bioinformatics
Python pipelines, Colab notebooks, TCGA and GEO datasets. Reproducibility is harder than it sounds.
Contact
Not taking guest posts, but happy to hear from other student researchers or anyone working in Bio/AI.