Results 81 to 90 of about 43,712,030 (248)
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
Learning the Structure of Biomedical Relationships from Unstructured Text.
PLoS Computational Biology, 2015 The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses (phenotypes).
Bethany Percha, Russ B Altman
doaj +1 more source
BMC Bioinformatics, 2010 Background An increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done.
Roeder Christophe +4 more
doaj +1 more source
Co-occurrence of Cell Lines, Basal Media and Supplementation in the Biomedical Research Literature
Journal of Data and Information Science, 2020 The use of in vitro cell culture and experimentation is a cornerstone of biomedical research, however, more attention has recently been given to the potential consequences of using such artificial basal medias and undefined supplements.
Cox Jessica +3 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed +15 more
wiley +1 more source
MBA: a literature mining system for extracting biomedical abbreviations
BMC Bioinformatics, 2009 Background The exploding growth of the biomedical literature presents many challenges for biological researchers. One such challenge is from the use of a great deal of abbreviations.
Lei YiMing +4 more
doaj +1 more source
Scalable Text Mining with Sparse Generative Models [PDF]
, 2015 The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers.
Puurula, Antti
core
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 +9 more
wiley +1 more source
The @Note biomedical text mining workbench [PDF]
, 2008 [Excerpt] Biomedical Text Mining. i.e., the field that deals with the automatic: retrieval and processing of biomedical literature, is perhaps one of today's most promising research fields.
Ferreira, Eugénio C. +9 more
core
Fluorescent Hydrogel‐Based Strain Sensor With Machine Learning‐Augmented Performance
Advanced Intelligent Discovery, EarlyView.Fluorescent hydrogel strain sensor based on carbon quantum dots enabling optical readout of deformation through strain‐dependent emission changes, coupled with Random Forest analysis to capture nonlinear fluorescence‐concentration relationships and identify optimal sensing conditions. Hydrogels are ideal matrices for bio‐integrated wearable sensors due
Tailai Chen +4 more
wiley +1 more source