Results 151 to 160 of about 22,074,380 (291)

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

A two-stage workflow for vitiligo diagnosis: clinical characteristic classification and large language model (LLM)-based report generation. [PDF]

Front Immunol
He K, Xu T, Feng Y, Lu Y, Wang X, Jiang L, Guo S, He Y, Dai W, Zhang W, Zhang J, Lu H, Huang D, Li S.   +13 more
europepmc   +1 more source

Eliciting Metaknowledge in Large Language Models

Advances in Natural Language Processing led to the introduction of Large Language Models (LLMs), that have been found endowed of enriched capabilities and improved performance results when increased in size.
Misael Mongiovì, Antonio Lieto, Luana Bulla, Carmelo Fabio Longo   +3 more
core  

LLM-R2: a large language model enhanced rule-based rewrite system for boosting query efficiency

Query rewrite, which aims to improve query efficiency by altering an SQL query’s structure without changing its result, has been an important research problem.
Li, Zhaodonghui, Bing, Lidong, Wang, Huiming, Yuan, Haitao, Cong, Gao   +4 more
core   +1 more source

Artificial Intelligence‐Driven Network Pharmacology: A Methodological Paradigm Shift Bridging Traditional Wisdom and Modern Science

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang, Sile Hu, Rigahai Ji, Riguna A, Siqige Hao, Ma Zhuo, Yi‐Hung Chen, Bisihalatu Hu, Ulana Bai, Xigui Hada   +9 more
wiley   +1 more source

Feasibility and challenges of large language model (LLM)-generated neonatal resuscitation simulations: a multicenter exploratory study. [PDF]

Scand J Trauma Resusc Emerg Med
Xu C, Zhou M, Wang D, Zhang Q, Liu JQ, Fung G, Wong M, Xue Y, Schmölzer GM, Cheung PY.   +9 more
europepmc   +1 more source

Accelerating the Discovery of Proton Conducting Electrolytes via Machine Learning‐Enabled Literature Mining

Advanced Intelligent Discovery, EarlyView.
An end‐to‐end knowledge discovery framework is established to automate high‐precision property extraction from small, specialized literature corpora. Utilizing a domain‐specific bidirectional encoder representation from a transformer model and data augmentation, the system accurately extracts and structures electrolyte performance data, ultimately ...
Gaheun Shin, Seongho Lee, Seulchan Kim, Ho‐Il Ji, Joonhee Kang   +4 more
wiley   +1 more source

A Reproducible Protocol and Framework for Large Language Model (LLM)-Assisted Estrogen and Progesterone Receptor (ER/PR) Scoring. [PDF]

Cureus
Bardarov S, Zarineh A.
europepmc   +1 more source

Defining the roles of large language models (LLM) agents in the model of design [PDF]

The subject of the study is the capabilities and limitations that large language models (LLM) demonstrate when they are implemented in intellectual, technical and creative processes, in particular in design. The goal of the work is to determine the place
Yaloveha I., Novakovskyi A.
core  

Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network

Advanced Intelligent Discovery, EarlyView.
We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang, Bing Sun, Zhigang Chen, Chenyu Zhao, Eugene Bakker, Yaojie Lei, Lingfei Zhao, Shijian Wang, Chao Liu, Virginia Xiaojun He, Bahram Shirzadi, Hao Liu, Xin Xi, Ruiliu Liu, Dongqing Wu, Guodong Long, Guoxiu Wang   +16 more
wiley   +1 more source