arXiv:2606. 11144v1 Announce Type: new Abstract: Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories.
By Abhijoy Sarkar, Aarchi Singh Thakur
arXiv:2608. 05359v1 Announce Type: new Abstract: CASCADE is an agentic framework that predicts downstream transcriptional effects of gene perturbation from precomputed ARACNe regulatory networks, exposed via MCP.
By Jose A. Bird
arXiv:2608. 17370v1 Announce Type: new Abstract: Generative models promise a route to explainable clinical AI: rather than probe a classifier, model the distributions of healthy and diseased patients and read explanations off the geometry between them.
By Lalit Kumar
arXiv:2606. 17491v1 Announce Type: cross Abstract: Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors.
By Adolphus Wagala, Mehmet Samur, Giovanni Parmigiani
arXiv:2608. 07609v1 Announce Type: cross Abstract: High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens typically use only a single replicate per test substance.
By Xiaohua Douglas Zhang
arXiv:2607. 02768v1 Announce Type: cross Abstract: Pathologic complete response and tumor shrinkage measure whether breast cancer responds to neoadjuvant therapy, but not whether that response was structurally favorable, persistent, or hidden beneath volume loss.
By Dattatreya Kantha, Murray H. Loew
arXiv:2607. 10466v1 Announce Type: new Abstract: Survival models can model time-to-event outcomes using partially observed data.
By Yanqi Xu, Hui Dai, Carlos Fernandez-Granda, Krzysztof J. Geras, Yiqiu Shen
arXiv:2608. 04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times.
By Mingi Hong
arXiv:2608. 04046v1 Announce Type: cross Abstract: Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training.
By Shashank Yadav, David M. Routman, Andrew Y. K. Foong
arXiv:2608. 03145v1 Announce Type: new Abstract: Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain largely obscured.
By Yesung Cho, Ji Hwan Park, Chanil Kim, Hyewon Kim, Honglan Li, Yumin Lee, Geongyu Lee, Sujeong Hong, Seong Min Park, Yoonyoung Lee, Hee Sool Rho, Sumin Lee, Amos Chungwon Lee, Changhwan Lee, Hwanyoung Shim, Hyunwook Kim, Hyeji Shin, Sanha Park, Jihoon Yu, Yoon Hee Shin, Sooheon Kim, Hyunjin Park, Seung Min Park, Sangwan Kim, Yujung Kim, Sung-Im Do, Eun-Young Kim, Dongmyung Shin, Jongbae Park, In-Gu Do
arXiv:2606. 30695v1 Announce Type: cross Abstract: Single-cell drug perturbation models should predict not only transcriptional response magnitude, but also whether a treatment alters the proliferative state of a cell.
By Dingping Zhao, Jie Lin
arXiv:2606. 25762v1 Announce Type: new Abstract: In oncology, access to patient-level data is often restricted.
By Octavia-Andreea Ciora, Julian Welzel, Dennis Frauen, Maresa Schr\"oder, Marie Brockschmidt, Harry Amad, Thomas Callender, Mihaela van der Schaar, Stefan Feuerriegel