Results 251 to 260 of about 3,554 (301)

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Genomic prediction accuracy for low hydrogen cyanide selection in fresh cassava roots. Comparative model analysis. [PDF]

BMC Plant Biol
Kanaabi M, Mukasa SB, Nuwamanya E, Kayondo IS, Wagaba H, Iragaba P, Baguma JK, Muhumuza N, Nanyonjo AR, Wembabazi E, Buttibwa M, Esuma W, Kawuki R.   +12 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Multi-phase dataset for bulk Ti and the Ti-6Al-4V alloy. [PDF]

Sci Data
Allen CS, Bartók AP.
europepmc   +1 more source

A class of reproducing kernels

Illinois Journal of Mathematics, 1968
openaire   +3 more sources

Spatially Informed Feature Selection and Machine Learning in Matrix‐Assisted Laser Desorption/Ionization Imaging for Cohort‐Scale Molecular Tissue Phenomics in Glioblastoma

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, Denis Abu Sammour, Dirk C. Hoffmann, Philipp Sievers, Tobias Bausbacher, Melissa Hahn, Yasemin Ucal, Iris Mildenberger, Christiane A. Opitz, Stefan Schmidt, Michael Platten, Oliver Wasenmüller, Wolfgang Wick, Andreas von Deimling, Tobias Kessler, Carsten Hopf   +15 more
wiley   +1 more source

Bayesian Signal Matching for Transfer Learning in ERP-Based Brain Computer Interface. [PDF]

J Am Stat Assoc
Ma T, Huggins JE, Kang J.
europepmc   +1 more source

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni   +11 more
wiley   +1 more source

ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction. [PDF]

Genet Sel Evol
Li J, Guo P, Zhang Y, Ma H, Zhao Z, Wang Y, Wang Z, Chen Y, Xu L, Zhang L, Gao H, Gao X, Li J, Zhu B.   +13 more
europepmc   +1 more source

Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware. [PDF]

Nat Commun
Baruzzi V, Indiveri G, Sabatini SP.
europepmc   +1 more source