Mike Loomans
Hey you thanks for visiting my site,
Greetings,
I am Mike Loomans, an industrious bioinformatician, aspiring to contribute significantly to my field and beyond.
My interests span the realms of academia, biostatistics, privacy, and outdoor pursuits.
While my numerous side projects may fade into obscurity once completed, this page serves as a testament to my commitment to maintaining transparency and organization throughout the entire project lifecycle—from inception to final production.
Recognizing that some of my ideas may be considered ambitious, I find it essential to document them, acknowledging the potential utility that may arise as my skills and technical capabilities continue to evolve.
These endeavors are not intended for commercial purposes but rather for personal enjoyment. I anticipate that they will incur costs rather than generate revenue, viewing this collection as a digital diary of my journey.
I have categorized my projects into sections, but most are very much intertwined.
Kind regards,
Mike Loomans
I have gone about this project before and want to set it up again, hoping to use something like this. But using water to retain the optimal humidity instead so it would not need a separate humidifier.
During then 2025 BioSB conference I met a ton of interesting people and listened to a great number of cool talks. While there I presented my package on the creation of bioprospecting models as pip package called migenpro in the form of a demo.
Genome-wide association studies are on the rise, thanks to the continuous development of novel sequencing techniques. This trend underscores the demand for appropriate tools and analyses. One promising avenue within GWAS involves phenotype prediction, wherein machine learning models can anticipate phenotypes and traits, including substances like 1,3 biopropanol. Through the utilization of various feature importance assessments, we can identify the pivotal features—in our case, protein domains and genome properties—that significantly contribute to specific traits of interest.
The global scale of pig production has reached massive proportions and continues to grow, driven by increasing incomes in the developing world.
However, intensive livestock farming and the conditions needed for profitability contribute to the spread of infectious diseases, such as Post-Weaning Diarrhea (PWD), particularly in industrial pig farming.
PWD is characterized by sudden mortality, diarrhea, dehydration, and growth inhibition in piglets.
Industrial farming disrupts natural weaning cycles, leading to early and abrupt weaning at 3 to 4 weeks compared to 15 to 22 weeks in semi-natural conditions.
This practice, along with early separation of piglets from the sow, exposes piglets to multiple stressors during a vulnerable period for the gut microbiome.
Aside from animal welfare concerns, PWD has significant economic costs, with 30-50% of piglets developing diarrhea in Denmark.
The conservative use of antimicrobials by Danish farmers, due to national regulations, increases the incidence of PWD.
While antimicrobials effectively reduce PWD, they contribute to antimicrobial resistance (AMR) in the pig gut, posing a risk to humans through pork consumption or direct contact.
To address PWD in the absence of antibiotics which were banned for use as growth promoter in 2006, the use of zinc oxide (ZnO) became popular, but its environmental impact led to restrictions.
The European Commission limited zinc-containing compounds in food-producing species to a maximum of 150 ppm/L of feed as of 2022.
Given the diminishing treatment choices for PWD the PIG-PARADIGM project started off with the aim of treating piglets and minimizing antibiotics use.
My role in this project focuses on the microbiome analysis of the piglet gut post weaning.
To accomplish this goal I envision a graph database capable of showing the intertwined nature of all genomes within the pig gut.
Using time series data, cutting edge bioinformatics tools, and the amazing people around me, I hope to set up this project very abstractly represented in the figure below.
