Ganegedara, T. (2025, February 2). Intuitive Guide to Latent Dirichlet Allocation. Towards Data Science.
https://towardsdatascience.com/light-on-math-machine-learning-intuitive-guide-to-latent-dirichlet-allocation-437c81220158/
some blogs
https://johaupt.github.io/blog/Topic_modeling_with_Gibbs_sampling_in_R.html https://agustinus.kristia.de/blog/lda-gibbs/
good tutorial/textbook chaper/review paper on Poisson regression :
Below are a few readings that discuss how to fit a generalized linear mixed model.
1. Breslow & Clayton (1993), JASA, Approximate Inference in
Generalized Linear Mixed Models.
https://doi.org/10.2307/2290687
A classic statistical paper introducing the Laplace approximation and penalized quasi-likelihood for GLMMs
2. Bates (2011), Mixed models in R using the lme4 package Part 5: Generalized linear mixed models
https://lme4.r-forge.r-project.org/slides/2011-03-16-Amsterdam/5GLMMH.pdf 3. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of statistical software, 67, 1-48.
https://doi.org/10.18637/jss.v067.i01
4. Bates (2025), Computational methods for mixed models
https://cran.r-project.org/web/packages/lme4/vignettes/Theory.pdf
These are written by the authors of the lme4 package, discussing the details of how a mixed-effects model (more specifically, a generalized linear mixed-effects model) is trained using the PIRLS approach.
1. Paulsson (2005) – “Models of stochastic gene expression”
https://www.sciencedirect.com/science/article/abs/pii/S1571064505000138 Nice pedagogical review of the master equation framework – covers the conceptual foundations and different analytical approaches.
2. Shahrezaei & Swain (2008) – “Analytical distributions for stochastic gene expression”
https://pubmed.ncbi.nlm.nih.gov/18988743/
This one derives the exact analytical solutions to the master equation for protein/mRNA distributions.
The Paulsson paper is probably better as a tutorial.
However, it’s a bit difficult to connect to protein & mRNA.
some reading materials on RL:
https://arxiv.org/pdf/2412.05265
https://arxiv.org/abs/2412.05265
Murphy, K. (2024, December 6). Reinforcement Learning: An Overview. arXiv.org. https://arxiv.org/abs/2412.05265
QT:{{”
Reinforcement learning or RL is a class of methods for solving various kinds of sequential decision making
tasks. In such tasks, we want to design an agent that interacts with an external environment. The agent
maintains an internal state zt, which it passes to its policy π to choose an action at = π(zt). The environment
responds by sending back an observation ot+1, which the agent uses to update its internal state using the
state-update function zt+1 = SU(zt, at, ot+1). See Figure 1.1 for an illustration.
To simplify things, we often assume that the environment is also a Markovian process, which has internal
world state wt, from which the observations ot are derived. (This is called a POMDP — see Section 1.2.1).
We often simplify things even more by assuming that the observation ot reveals the hidden environment state;
in this case, we denote the internal agent state and external environment state by the same letter, namely
st = ot = wt = zt. (This is called an MDP — see Section 1.2.2). We discuss these assumptions in more detail
in Section 1.1.3.
RL is more complicated than supervised learning (e.g., training a classifier) or self-supervised learning
(e.g., training a language model), because this framework is very general: there are many assumptions we can
make about the environment and its observations ot, and many choices we can make about the form the
agent’s internal state zt and policy π, as well the ways to update these objects as we see more data. We
will study many different combinations in the rest of this document. The right choice ultimately depends on
which real-world application you are interested in solving.1 .”}}
some tutorials on flow-matching:
https://arxiv.org/pdf/2412.06264
https://arxiv.org/pdf/2506.02070
Lipman, Y., Havasi, M., Holderrieth, P., Shaul, N., Le, M., Karrer, B., Chen, R. T. Q., Lopez-Paz, D., Ben-Hamu, H., & Gat, I. (2024, December 9). Flow matching guide and code. arXiv.org.
https://arxiv.org/abs/2412.06264
(Sometimes difficult to follow formalism)
Holderrieth, P., & Erives, E. (2025). MIT Class 6.S184: Generative AI with Stochastic Differential equations (pp. 1–52).
https://arxiv.org/pdf/2506.02070
OR
Holderrieth, P., & Erives, E. (2025, June 2). An introduction to flow matching and diffusion models. arXiv.org.
https://arxiv.org/abs/2506.02070
(Very intuitive!!!)
https://report.nih.gov/funding/nih-budget-and-spending-data-past-fiscal-years/success-rates
NIH grant statistics for different types of grants.
Form 1 in Research Project Grants and Form 4 in Training and Research career development have the total number of grants reviewed.
https://www.nature.com/articles/s41592-025-02618-1
Hallou, A., He, R., Simons, B. D., & Dumitrascu, B. (2025). A computational pipeline for spatial mechano-transcriptomics. Nature Methods. https://doi.org/10.1038/s41592-025-02618-1
Reviews:
https://www.nature.com/articles/s41580-023-00583-1#Sec35
(difficult to follow)
Combining with Spatial transcriptomics:
https://www.nature.com/articles/s41592-025-02618-1
(new thing)
these slides might be related to the event in this link:
The book with a nice explanation of suffix array and BWT is
Bioinformatics Algorithms: An Active Learning Approach by Phillip Compeau & Pavel Pevzner. https://www.bioinformaticsalgorithms.org/
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