The Human Race Into Space Requires Kidneys
A research and discussion group
- Updates?
2. Reading and discussion
- Current readings on kidneys or space (or both), depending on interest and attendance.
- Current kidney reading:
- The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation, Tolstyak et al., 2021, https://www.mdpi.com/2076-3417/11/21/10380. We got up to "A classifier based on a decision tree is a tree whose inner vertices are denoted by terms," and can start with that next time we do this reading.
- In the future we could evaluate some new kidney related readings listed below.
- Current space reading:
- We started on https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5. We read through "[Image 10 and 11]" but did not understand them. We could review them next time or just continue on. Kind of depends on if someone figures it out and can explain it. If no one does then we should just continue on.
- In the future we could evaluate some new kidney related readings listed below.
- Future space readings. Here are some possible future readings in order from highest to lowest score.
- 9/8/23: We could do part of: "Understanding Statistics and Experimental Design," https://link.springer.com/book/10.1007/978-3-030-03499-3. Chapter "Meta-Analysis" was evaluated at 4.
- 9/8/23: We once started: https://www.newthingsunderthesun.com/pub/4xnyepnn/release/9. We read up to "In their paper, firms use these technologies to produce a fixed amount of output every period." Working through more was evaluated at 4.
- 9/8/23: We could do this from the "talks" section of planet4589: https://planet4589.org/talks. The ones we looked at were all voted at 3 1/3.
- Attendees could suggest possible readings/viewings between now and next time, or we could take time now for each person to search for things and report back.
- Future kidney articles, some evaluated for future reading and some yet to be evaluated.
- https://towardsdatascience.com/boruta-explained-the-way-i-wish-someone-explained-it-to-me-4489d70e154a (not evaluated yet).
- Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study, Jesper Kers*, Roman D Bülow*, Barbara M Klinkhammer, Gerben E Breimer, Francesco Fontana, Adeyemi Adefidipe Abiola, Rianne Hofstraat, Garry L Corthals, Hessel Peters-Sengers, Sonja Djudjaj, Saskia von Stillfried, David L Hölscher, Tobias T Pieters, Arjan D van Zuilen, Frederike J Bemelman, Azam S Nurmohamed, Maarten Naesens, Joris J T H Roelofs, Sandrine Florquin, Jürgen Floege, Tri Q Nguyen, Jakob N Kather†, Peter Boor†, https://pubmed.ncbi.nlm.nih.gov/34794930/. 9/1/23 vote was 3 5/6.
- Artificial intelligence and kidney transplantation, Nurhan Seyahi, Seyda Gul Ozcan, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290997. 9/15/23 vote was 4.0.
- TC wrote: Authors Zhang et al. (2020) conducted a similar study for clear cell renal cell carcinoma survival prediction, however they worked with publicly available genetic data from the Genomic Data Commons Data Portal (National Cancer Institute, n.d.) and the Cancer Genome Atlas (National Center for Biotechnology Information, n.d.). Using this data as well as other clinical prognostic parameters, the authors identified five prognostic genes that they believe are better survival predictors of clear cell renal cell carcinoma than then TNM system. Considering that the authors only explored public data, there are potentially additional genes or non-publicly available variables that could be explored. [Reference:] Zhang, Z., Lin, E., Zhuang, H., Xie, L., Feng, X., Liu, J., & Yu, Y. (2020). Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma. Cancer Cell International, 20(1), 27. https://doi.org/10.1186/s12935-020-1113-6
- Long-Term Time Series Forecasting and Updates on Survival Analysis of Glioblastoma Multiforme: A 1975–2018 Population-Based Study, https://doi.org/10.1159/000522611
- These describe a method for using recent lifetime data, which is the same problem that TC is working on: (1) Brenner, H., Gefeller, O., & Hakulinen, T. (2004). Period analysis for ‘up-to-date’ cancer survival data. European Journal of Cancer, 40(3), 326–335. https://doi.org/10.1016/j.ejca.2003.10.013. (2) Brenner, H., & Hakulinen, T. (2006). Up-to-date and Precise Estimates of Cancer Patient Survival: Model-based Period Analysis. American Journal of Epidemiology, 164(7), 689–696. https://doi.org/10.1093/aje/kwj243
- TC wrote: "On the other hand, authors Solé et al. (2014) investigate the dynamics of technological innovation and the conditions under which it may lead to explosive growth or enter a linear regime. It develops a generalized model of technological evolution, focusing on two crucial properties: the number of previous technologies required to create new innovations and the rate at which technology ages. The study explores two different models of combinatorial growth, one involving long-range memory and the availability of old inventions for new innovations, and the other with aging having a characteristic time scale"
- Ref.: Solé, R. V., Amor, D. R., & Valverde, S. (2014). On singularities in combination-driven models of technological innovation (arXiv:1407.6890). arXiv. https://doi.org/10.48550/arXiv.1407.6890
- Here are some generic questions about articles (and videos):
- What is the source?
- What is the most significant advance in the human knowledge presented in the paper?
- Why is that advance important?
- What important questions arise from the paper for future research?
- What important questions would it be nice if the paper answered, but does not answer?
- What does the paper present that is novel (no one else has provided that before)?
- What is the relevance of the paper to our satellite research goals?
- Questions from the group?
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