The Analytic Garden

The SARS-CoV-2 variants BA.2.86 and its subvariant BA.2.86.1 have been showing up in the news over the last several weeks. The cause for concern is that the virus contains a number of new mutations in the spike protein compared to the Omicron variants. However, it's unclear what effect these mutations have on severity or disease transmission. I downloaded 105 FASTA sequences with Pango lineage of BA.2.86 or BA.2.86.1 and a metafile with 15,962,305 records from GISAID on September 11, 2023. After filtering to remove the sequences containing incomplete genomes, I was left with 98 sequences. The number of BA.2.86 and BA.2.86.1 sequences has been growing slowly over the past few months. The code for this plot can be found here . Mutation Pipeline To study the mutations in the BA.2.86 and BA.2.86.1 sequences compared to the Wuhan reference sequence, I used the following pipeline. It's not a fully automated pipeline, but a series of steps. First, the metafile was filtered to find ...

TabPFN is a transformer NN  for unsupervised classification of small tabular datasets. It's described in this paper  and at this GitHub site . It's based on PyTorch. TabPFN does not fit a new model from scratch each time. Instead, it uses a large transformer that has been pre-trained to solve artificially generated classification tasks from a prior dataset.  The TabPFN details are described in the paper. Rather than going into the details of how it works, I want to test it on some simple datasets. You can read the paper for the details and how the authors evaluated the method. Diabetes Data Table. The first set is a collection of data originally from  the National Institute of Diabetes and Digestive and Kidney Diseases. The dataset is was designed to predict whether or not a patient has diabetes, based on certain measurements. All patients included in the data were females at least 21 years old of Pima Indian heritage. The data table is included in the R library mlb...