Results 71 to 80 of about 43,712,030 (248)

Mining the neuroimaging literature

eLife
Automated analysis of the biomedical literature (literature mining) offers a rich source of insights. However, such analysis requires collecting a large number of articles and extracting and processing their content.
Jérome Dockès, Kendra M Oudyk, Mohammad Torabi, Alejandro I de la Vega, Jean-Baptiste Poline   +4 more
doaj   +1 more source

AI Powered Biobanks From Static Archives to Dynamic Discovery Engines

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) provide a potential framework for transforming biobanks from static data repositories into intelligent discovery engines. By enabling unified representation and analysis of multimodal biomedical data, LLM‐based systems facilitate dynamic risk prediction, biomarker identification, and mechanistic interpretation, thereby ...
Wenzhen Yin, Yutian Feng, Wenrui Ma, Yuxin Su, Li Min, Juan Liu   +5 more
wiley   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Fourier Kolmogorov-Arnold Network integrated into BioBERT-based model for Biomedical Named Entity Recognition

npj Digital Medicine
Biomedical Named Entity Recognition (BioNER) extracts entities such as diseases, drugs, and genes from biomedical texts, which are often dense in domain-specific terms and complex semantics.
Li Yelin, Wu Yan, Xie Xiaojun, Zhu Jihong, Guan Lixin, Li Mengshan   +5 more
doaj   +1 more source

A neural network approach to chemical and gene/protein entity recognition in patents

Journal of Cheminformatics, 2018
In biomedical research, patents contain the significant amount of information, and biomedical text mining has received much attention in patents recently.
Ling Luo, Zhihao Yang, Pei Yang, Yin Zhang, Lei Wang, Jian Wang, Hongfei Lin   +6 more
doaj   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

OGER++: hybrid multi-type entity recognition

Journal of Cheminformatics, 2019
Background We present a text-mining tool for recognizing biomedical entities in scientific literature. OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus ...
Lenz Furrer, Anna Jancso, Nicola Colic, Fabio Rinaldi   +3 more
doaj   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

GPDminer: a tool for extracting named entities and analyzing relations in biological literature

BMC Bioinformatics
Purpose The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition.
Yeon-Ji Park, Geun-Je Yang, Chae-Bong Sohn, Soo Jun Park   +3 more
doaj   +1 more source

NAMED ENTITY RECOGNITION FROM BIOMEDICAL TEXT -AN INFORMATION EXTRACTION TASK [PDF]

ICTACT Journal on Soft Computing, 2016
Biomedical Text Mining targets the Extraction of significant information from biomedical archives. Bio TM encompasses Information Retrieval (IR) and Information Extraction (IE).
N. Kanya, T. Ravi
doaj