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Patterns (New York, N.Y.)|December 18, 2023
Revealing factors influencing polymer degradation with rank-based machine learningWeilin Yuan, Yusuke Hibi, Ryo Tamura, et al.
Patterns (New York, N.Y.)|December 18, 2023
DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matricesMaria Masotti, Nathaniel Osher, Joel Eliason, et al.
Patterns (New York, N.Y.)|November 30, 2023
Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discoveryXiaoning Qian, Byung-Jun Yoon, Raymundo Arróyave, et al.
Patterns (New York, N.Y.)|March 15, 2024
DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography imagesBo Qian, Hao Chen, Xiangning Wang, et al.
Patterns (New York, N.Y.)|March 15, 2024
Spaco: A comprehensive tool for coloring spatial data at single-cell resolutionZehua Jing, Qianhua Zhu, Linxuan Li, et al.
Patterns (New York, N.Y.)|February 19, 2024
shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learningLi-Ju Wang, Michael Ning, Tapsya Nayak, et al.
Patterns (New York, N.Y.)|February 19, 2024
Density physics-informed neural networks reveal sources of cell heterogeneity in signal transductionHyeontae Jo, Hyukpyo Hong, Hyung Ju Hwang, et al.
Patterns (New York, N.Y.)|February 19, 2024
GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysisMichael R Waters, Matthew Inkman, Kay Jayachandran, et al.
Patterns (New York, N.Y.)|May 27, 2024
MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidanceSeyednami Niyakan, Jianting Sheng, Yuliang Cao, et al.
Patterns (New York, N.Y.)|May 27, 2024
DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq dataJiawei Huang, Yuxuan Du, Andres Stucky, et al.
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