DEEP LEARNING FOR LIFE SCIENCES

Image source: Human Microbiome Project portal

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


Thoughts and Theory, MATHEMATICAL STATISTICS AND MACHINE LEARNING FOR LIFE SCIENCES

Pieter Bruegel the Elder “The Triumph of Death”, Museo del Prado, Madrid, image source

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…


Making Sense of Big Data, MATHEMATICAL STATISTICS AND MACHINE LEARNING FOR LIFE SCIENCES

Modified from Wikipedia Emperor’s New Clothes

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…


MATHEMATICAL STATISTICS AND MACHINE LEARNING FOR LIFE SCIENCES

Image by Author

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…


Mathematical Statistics and Machine Learning for Life Sciences

Image by Author

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…


Mathematical Statistics and Machine Learning for Life Sciences

Image by Author

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)…


Mathematical Statistics and Machine Learning for Life Sciences

Image source

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…


Mathematical Statistics and Machine Learning for Life Sciences

Image source: Wikipedia Simpson’s Paradox

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…


Mathematical Statistics and Machine Learning for Life Sciences

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…


Mathematical Statistics and Machine Learning for Life Sciences

Image source

This is the fifteenth 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. Dimension reduction techniques such as tSNE and UMAP are absolutely central for many types of…

Nikolay Oskolkov

Bioinformatician, SciLifeLab, Sweden

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