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Received β€” 9 January 2026 ⏭ Favourites

Leveraging Genetic Instrumental Variables and Sequencing Analysis to Identify a Prognostic Signature Based on Epithelial Cell Markers in Lung Adenocarcinoma

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Jiaye Lao Β· Ziqing Han Β· Xinjing Lou Β· Jinxuan Ye Β· Chen Gao Β· Linyu Wu
7 January 2026 at 19:00

Thorac Cancer. 2026 Jan;17(1):e70244. doi: 10.1111/1759-7714.70244.

ABSTRACT

MAIN PROBLEM: The treatment and prognosis of lung adenocarcinoma (LUAD) remain challenging. The study aimed to identify prognostic genes and construct a prognostic model for LUAD.

METHODS: After identifying malignant alveolar type II (AT2) cells using InferCNV, we applied CytoTRACE, pseudo-time analysis, Mendelian randomization (MR), and univariate Cox regression analysis to identify prognostic genes. A prognostic model was then developed using an optimized subset of these genes, selected through the least absolute shrinkage and selection operator (LASSO) algorithm. Further analyses included Gene Ontology enrichment analysis and the construction of a protein-protein interaction (PPI) network.

RESULTS: Pseudo-time analysis identified 3526 dynamically expressed genes during malignant AT2 cell dedifferentiation. Subsequent multi-omics integration refined the gene selection, yielding four prognostic genes for the final predictive model. The resulting model achieved area under the receiver operating characteristic (ROC) curve (AUC) values of 0.649, 0.675, and 0.654 for predicting 1, 2, and 3-year overall survival (OS) in the training set, respectively, and was successfully validated in two external cohorts at the corresponding time points. Moreover, survival analysis demonstrated that patients in the high-risk group had significantly poorer OS than those in the low-risk group, both in the training set and the validation sets (p < 0.01).

CONCLUSIONS: The study developed a novel signature based on genes dynamically expressed during malignant AT2 cell dedifferentiation, capable of predicting the prognosis of LUAD patients, and offered four accurate prognostic biomarkers (ADM, MARK4, PARVA, and RPS6KA1).

PMID:41500831 | DOI:10.1111/1759-7714.70244

Received β€” 17 November 2025 ⏭ Favourites

From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Chao Wu Β· Baoheng Li Β· Mingchen Gao Β· Zhenyi Wang
17 November 2025 at 13:00
arXiv:2511.10788v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have made reasoning a central benchmark for evaluating intelligence. While prior surveys focus on efficiency by examining how to shorten reasoning chains or reduce computation, this view overlooks a fundamental challenge: current LLMs apply uniform reasoning strategies regardless of task complexity, generating long traces for trivial problems while failing to extend reasoning for difficult tasks. This survey reframes reasoning through the lens of {adaptivity}: the capability to allocate reasoning effort based on input characteristics such as difficulty and uncertainty. We make three contributions. First, we formalize deductive, inductive, and abductive reasoning within the LLM context, connecting these classical cognitive paradigms with their algorithmic realizations. Second, we formalize adaptive reasoning as a control-augmented policy optimization problem balancing task performance with computational cost, distinguishing learned policies from inference-time control mechanisms. Third, we propose a systematic taxonomy organizing existing methods into training-based approaches that internalize adaptivity through reinforcement learning, supervised fine-tuning, and learned controllers, and training-free approaches that achieve adaptivity through prompt conditioning, feedback-driven halting, and modular composition. This framework clarifies how different mechanisms realize adaptive reasoning in practice and enables systematic comparison across diverse strategies. We conclude by identifying open challenges in self-evaluation, meta-reasoning, and human-aligned reasoning control.
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