Infer microbial composition of a sample from DNA sequences — This is the ninth post of my column Deep Learning for Life Sciences where I try to demonstrate concrete examples of applying artificial neural networks to real world projects from Computational Biology and Life Sciences. Previously, we covered a few applications of Deep Learning for Ancient DNA, Single Cell Biology…

How you can find Yersinia pestis pathogen in any random sample — This is the twenty third article from my column Mathematical Statistics and Machine Learning for Life Sciences, where I discuss in plain language some mysterious analytical techniques that are common in Computational Biology. DNA sequencing technologies applied to archeological material tremendously enriched our knowledge about the human past. For example…

How the Curse of Dimensionality complicates Genetics research — This is the twenty second article of my column Mathematical Statistics and Machine Learning for Life Sciences, where I discuss in plain language some mysterious analytical techniques that are common in Computational Biology. Genome-wide genotyping and whole-genome sequencing (WGS) brought unprecedented resolution to genetic studies in Life Sciences, but also…

DESeq2 vs. LASSO predictive capacity on gene expression — This is the twenty first article from my column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics and Computational Biology in a simple way. In my previous post Select Features for OMICs Integration I gave examples of multivariate…

Graph-based Single Cell Omics integration with UMAP — This is the twentieth article from the column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics and Computational Biology in a simple way. Data integration is an important next step for improving analysis accuracy by utilizing synergistic effects…

Linear Mixed Model via Restricted Maximum Likelihood (REML) — This is the nineteenth article from the column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics and Computational Biology in a simple way. This is the final article in the series dedicated to the Linear Mixed Model (LMM)…

Derive and code LMM using Maximum Likelihood — This is the eighteenth article from the column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics and Computational Biology in a simple way. Linear Mixed Model (also called Linear Mixed Effects Model) is widely used in Life Sciences…

And how to understand LMM through Bayesian lenses — This is the seventeenth article from my column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics and Computational Biology in a simple way. Linear Mixed Model (LMM) also known as Linear Mixed Effects Model is one of key…

At large Perplexity — This is the sixteenth article from the column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics and Computational Biology in a simple way. In my previous post, tSNE vs. UMAP: Global Structure, I touched the limit of large…