Algorithms on Strings, Trees, and Sequences

Dan Gusfield, 1997, Finished Reading

Technical read for people with computer science background getting into computational biology. The explanations for algorithms were quite good and built upon each other. It was clear how the algorithms covered related to biological problems.

Mastering Python for Bioinformatics

Ken Youens-Clark, 2021, Currently Reading

First impressions: The book is well presented and the information is delivered in a playful inquisitive manner. I find it straightforward due to my prior coding experience, but I am expect to be challenged more by the later chapters.

(brief) Paper Summaries

For research papers I found helpful or interesting

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

Brouwer et al. 2021

This paper is interesting because uses longitudinal data sourced from realistic environments (as opposed to controlled environments like clinical trials) to do prediction tasks. Longitudinal means the data is collected over time, this makes it harder for models to interpret since many results can be extremely relevant at certain times and irrelevant at others. Along with that challenge, the researchers used real world data which contains many missing values and potential errors, this type of data is more abundant but also less accurate. The researchers' results speak for themselves, they developed a state of the art model by dealing with novel forms of data, providing a base for future papers to improve upon.

Point of View on Outcome Prediction Models in Post-Stroke Motor Recovery

Groen et al. 2024

This paper outlines the area of stroke recovery prediction modeling at a high level, bringing up key areas for improvement and other challenges in the field. The researchers focus on upper limb (UL) recovery since it affects quality of life the most. They outline specific biomarkers that show the highest correlation with recovery, severity of the initial damage being the most important one, but they emphasize the importance of integrating data from a variety of sources to form a more accurate prediction. The researchers do a great job at breaking down key challenges that the field needs for progression, some of which being technical (such as the need to incorporate longitudinal data with dynamic models), and others being community based (like the need to establish consensus on important biomarkers for data collection)

Modeling the START transition in the budding yeast cell cycle

Janani Ravi, Kewalin Samart, Jason Zwolak 2024

This paper used a mathematical model to predict the START transition of yeast cells. This is the transition where, unironically, the cell progresses from the G1 (developmental) stage -> S (DNA synthesis) stage. It indicates that the cell has committed to proliferating, but newer research suggests it is reversible. The researchers had to consider several factors that regulate this transition in order to model it. One example is SBF which is a transcription factor, it can be activated if its inhibitor is phosphorylated (adding a phosphate group), or if one of its components Swi6 is phosphorylated. Basically, the transition is complicated and there are a lot of factors to consider, the fact that they can model all of this from ordinary differential equations makes it impressive.