Results 221 to 230 of about 416,163 (244)

High-throughput specificity profiling of antibody libraries using ribosome display and microfluidics. [PDF]

Cell Rep Methods
Wagner EK, Carter KP, Lim YW, Chau GJ, Enstrom A, Wayham NP, Hanners JM, Yeh CC, Fouet M, Leong J, Adler AS, Simons JF.   +11 more
europepmc   +1 more source

Deep Learning–Powered Robust Tactile Perception: Bridging Graphene Electronic Skin and Dynamic Decoding

Advanced Intelligent Systems, EarlyView.
This article presents a developed methodology to achieve robust tactile perception with spatially designed electronic skin (e‐skin) utilizing laser‐induced graphene (LIG). To enhance LIG‐based e‐skin, the fabrication of the e‐skin is simplified and advanced decoding algorithms are employed, effectively circumventing several intrinsic issues of ...
Gyeonghwa Heo, Jusouk Yoon, Jeonghwa Jeong, Young Woo Kwon, Suck Won Hong   +4 more
wiley   +1 more source

Pyrfume: A window to the world's olfactory data. [PDF]

Sci Data
Hamel EA, Castro JB, Gould TJ, Pellegrino R, Liang Z, Coleman LA, Patel F, Wallace DS, Bhatnagar T, Mainland JD, Gerkin RC.   +10 more
europepmc   +1 more source

Assessing Large Multimodal Models for One‐Shot Learning and Interpretability in Biomedical Image Classification

Advanced Intelligent Systems, EarlyView.
Image classification plays a pivotal role in biomedical image analysis. Herein, it is shown that large multimodal models, such as GPT‐4, achieve superior performance in one‐shot learning, generalization, interpretability, and text‐driven image classification. Applications span tissue, cell type, cellular state, and disease classification, outperforming
Wenpin Hou, Qi Liu, Huifang Ma, Yilong Qu, Zhicheng Ji   +4 more
wiley   +1 more source

Comparative analysis of AAV serotypes for transduction of olfactory sensory neurons. [PDF]

Front Neurosci
Belfort BDW, Jia JD, Garza AR, Insalaco AM, McGinnis JP, Pekarek BT, Ortiz-Guzman J, Tepe B, Chen H, aSCENT-PD Investigators, Liu Z, Arenkiel BR.   +11 more
europepmc   +1 more source

Applied Artificial Intelligence in Materials Science and Material Design

Advanced Intelligent Systems, EarlyView.
AI‐driven methods are transforming materials science by accelerating material discovery, design, and analysis, leveraging large datasets to enhance predictive modeling and streamline experimental techniques. This review highlights advancements in AI applications across spectroscopy, microscopy, and molecular design, enabling efficient material ...
Emigdio Chávez‐Angel, Martin Børstad Eriksen, Alejandro Castro‐Alvarez, Jose H. Garcia, Marc Botifoll, Oscar Avalos‐Ovando, Jordi Arbiol, Aitor Mugarza   +7 more
wiley   +1 more source

Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease. [PDF]

Sci Rep
Wang F, Liang Y, Wang QW.
europepmc   +1 more source

JCVI: A versatile toolkit for comparative genomics analysis. [PDF]

Imeta
Tang H, Krishnakumar V, Zeng X, Xu Z, Taranto A, Lomas JS, Zhang Y, Huang Y, Wang Y, Yim WC, Zhang J, Zhang X.   +11 more
europepmc   +1 more source

A Python program to merge Sanger sequences: an update. [PDF]

PeerJ
Lin S, Huang B, Zhao LL, Xu F, Pan D, Chen X, Lin S.   +6 more
europepmc   +1 more source

Assembly and characterization of the complete mitogenome of Bauhinia purpurea (Leguminosae). [PDF]

BMC Genom Data
Xie S, Chen Y, Zheng D, Luo S, Meng S, Zhong Y.   +5 more
europepmc   +1 more source