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Jeffrey R. Lapides, Ph.D.

I work with data of all kinds to find hard to identify patterns and relationships. I specialize in using unsupervised machine learning algorithms, Monte Carlo techniques and graph theoretics. Principally, I focus on life sciences and medical data, especially data from microbiome and genome measurements. I also have worked extensively with scientific document classification.

I have analyzed the microbiomes of over 7,000 human subjects and hundreds of animals, the medical records of over 70,000 patients, and tens of thousands of scientific documents. I have also recently worked with transcriptomic data of animals with viral infections. 

The techniques that I use are not limited to the types of data described above. They are applicable to everything.

I will be happy to share examples of my work that could be relevant to your analytical challenges. Please feel free to get in touch.

Our paper on the Microbiome of Alzheimer’s Disease was published on September 13, 2023 by Frontiers in Cellular and Infection Microbiology (doi: 10.3389/fcimb.2023.1123228). The paper explores the microbiomes of the brains of Alzheimer’s patients and reports finding a particular set of bacteria associated with the illness. 

The results depended on two capabilities, advanced bacterial DNA sequencing to identify the bacteria and novel machine learning algorithms adapted from computational linguistics by myself to find the Alzheimer’s patterns.

A lay summary can be found here.


Moné Y, Earl JP, Krol JE, Ahmed A, Sen B, Ehrlich GD and Lapides JR (2023) Evidence supportive of a bacterial component in the etiology for Alzheimer’s disease and for a temporal-spatial development of a pathogenic microbiome in the brain. Front. Cell. Infect. Microbiol. 13:1123228. doi: 10.3389/fcimb.2023.1123228

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