Merge branch 'master' of https://github.com/ShiuLab/ShiuLab.github.io

Author: Thilanka-lt

pages/activities_catsup_examples.md

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+### 6/20/2025: [Navigating the pitfalls of applying machine learning in genomics](https://www.nature.com/articles/s41576-021-00434-9)
+Discussing potential pitfalls and different opinions on what pitfalls are for different approaches to machine learning help improve the methods the Lab takes in its own projects. This week, the discussion revolved around what is a pitfall, what is purposeful design to answer a specific biological question, and how a scientist can communicate experimental design choices. 
+
+### 5/30/2025: [Genome Modeling and Design Across all Domains of Life with Evo2](https://www.biorxiv.org/content/10.1101/2025.02.18.638918v1)
+The Shiu Lab discussed a paper currently in preprint about identifying the complexity of genomes using a CNN framework trained on genetic sequences from all domains of life. The lab touched on how these finding reflect what people know biologically and what new findings models like this can present.
+
 ### 8/9/2024: Crash Course on Ancestral State Reconstruction 
 Due to popular demand our lab member and PhD candidate, Thilanka Ranaweera, explained different aspects of ancestral state reconstruction and an approach to determine the deviation from random chance an extant can be after the ancestral state has been determined. Thank you Thilanka!
 

pages/publications_last5.md

@@ -13,13 +13,20 @@ __Notation__: e: joint first/corresponding, u:undergrad, h: high school student,
 
 ## Pre-prints
 
-__Wang P<font color="green"><sup>e</sup></font>, Meng F__, Azodi CB, __Segura Abá K__, Casler MD, __Shiu SH__<font color="green"><sup>e</sup></font>. 2024. Impact of genome assemblies, genotyping methods, variant types, ploidy levels and population structures on genomic prediction in switchgrass. _bioRxiv_ [doi:10.1101/2024.06.17.599440v1](https://www.biorxiv.org/content/10.1101/2024.06.17.599440v1)
 
-Vardanega I, Maika JE, Demesa-Arevalo E, Lan T, Kirschner GK, Imani J, Acosta IF, Makowska K, Hensel G, **Ranaweera T, Shiu SH**, Schnurbusch T, von Korff Schmising M, Simon R. _bioRxiv_ [doi:10.1101/2024.05.28.595952](https://www.biorxiv.org/content/10.1101/2024.05.28.595952v1.abstract)
+## 2025
 
-## 2024
+__Peipei Wang__<font color="green"><sup>e</sup></font>, __Fanrui Meng__, __Christina B Del Azodi__, __Kenia Segura Aba__, Michael D. Casler, __Shin-Han Shiu__<font color="green"><sup>e</sup></font>. Optimizing genomic prediction for complex traits via investigating multiple factors in switchgrass. *Plant Physiology*, p.kiaf188 [doi](https://doi.org/10.1093/plphys/kiaf188), [pubmed](https://pubmed.ncbi.nlm.nih.gov/40331363/)
+
+Aranguiz K, Horianopoulos LC, Elkin L, __Abá KS__, Jordahl D, Overmyer KA, Wrobel RL, Coon JJ, __Shiu SH__, Rokas A, Hittinger CT. Machine learning reveals genes impacting oxidative stress resistance across yeasts. *Nature Communications*, 16(1), p.5866. [doi](https://doi.org/10.1038/s41467-025-60189-3), [pubmed](https://pubmed.ncbi.nlm.nih.gov/40592811)
+
+__Singhal R, Izquierdo P, Ranaweera T, Segura Abá K, Brown BNI, Lehti-Shiu MD, Shiu SH.__ Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops. *Philosophical Transactions B*, 380(1927), p.20240252. [doi](https://doi.org/10.1098/rstb.2024.0252), [pubmed](https://https://pubmed.ncbi.nlm.nih.gov/40439305/)
 
-Washburn JD, Varela JI, Xavier A, Chen Q, Ertl D, Gage JL, Holland JB, Lima DC, Romay MC, Lopez-Cruz M, de Los Campos G, Barber W, Zimmer C, Trucillo Silva I, Rocha F, Rincent R, Ali B, Hu H, Runcie DE, Gusev K, Slabodkin A, Bax P, Aubert J, Gangloff H, Mary-Huard T, Vanrenterghem T, Quesada-Traver C, Yates S, Ariza-Suárez D, Ulrich A, Wyler M, Kick DR, Bellis ES, Causey JL, Soriano Chavez E, Wang Y, Piyush V, Fernando GD, Hu RK, Kumar R, Timon AJ, Venkatesh R, __Segura Abá K__, Chen H, Ranaweera T, __Shiu SH, Wang P__, Gordon MJ, Amos BK, Busato S, Perondi D, Gogna A, Psaroudakis D, Chen CPJ, Al-Mamun HA, Danilevicz MF, Upadhyaya SR, Edwards D, de Leon N. Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates. _Genetics_ [doi](https://doi.org/10.1093/genetics/iyae195), [pubmed](https://pubmed.ncbi.nlm.nih.gov/39576009/)
+Washburn JD, Varela JI, Xavier A, Chen Q, Ertl D, Gage JL, Holland JB, Lima DC, Romay MC, Lopez-Cruz M, de Los Campos G, Barber W, Zimmer C, Trucillo Silva I, Rocha F, Rincent R, Ali B, Hu H, Runcie DE, Gusev K, Slabodkin A, Bax P, Aubert J, Gangloff H, Mary-Huard T, Vanrenterghem T, Quesada-Traver C, Yates S, Ariza-Suárez D, Ulrich A, Wyler M, Kick DR, Bellis ES, Causey JL, Soriano Chavez E, Wang Y, Piyush V, Fernando GD, Hu RK, Kumar R, Timon AJ, Venkatesh R, __Segura Abá K, Chen H, Ranaweera T, Shiu SH__, Wang P, Gordon MJ, Amos BK, Busato S, Perondi D, Gogna A, Psaroudakis D, Chen CPJ, Al-Mamun HA, Danilevicz MF, Upadhyaya SR, Edwards D, de Leon N. Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates. _Genetics_, 229(2), p.iyae195. [doi](https://doi.org/10.1093/genetics/iyae195), [pubmed](https://pubmed.ncbi.nlm.nih.gov/39576009/)
+
+Vardanega, I., Maika, J.E., Demesa-Arevalo, E., Lan, T., Kirschner, G.K., Imani, J., Acosta, I.F., Makowska, K., Hensel, G., __Ranaweera, T. and Shiu, S.H.__. CLAVATA signalling shapes barley inflorescence by controlling activity and determinacy of shoot meristem and rachilla. *Nature Communications*, 16(1), p.3937. [doi](https://doi.org/10.1038/s41467-025-59330-z), [pubmed](https://pubmed.ncbi.nlm.nih.gov/40287461/)
+
+## 2024
 
 __Wang P<font color="green"><sup>e</sup></font>, Lehti-Shiu MD__, Lotreck S, __Segura Abá K__, Patrick J. Krysan, __Shiu SH__<font color="green"><sup>e</sup></font>. Prediction of plant complex traits via integration of multi-omics data _Nat Commun_ 15(1):6856 [pubmed](https://pubmed.ncbi.nlm.nih.gov/39127735/)