Results 201 to 210 of about 1,858,266 (298)

Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques.

open access: yesHeliyon
Li L   +10 more
europepmc   +1 more source

Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment. [PDF]

open access: yesBiomed Eng Comput Biol
Fendzi Mbasso W   +6 more
europepmc   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

open access: yesAdvanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao   +6 more
wiley   +1 more source

Machine Learning‐Driven Design of Multicomponent Bone Inorganic Matrix Mimicking Scaffolds for Osteogenesis Enhanced by Neurogenesis

open access: yesAdvanced Science, EarlyView.
Schematic illustration of development of experimental datasets and algorithm models, screening and preparation of the scaffolds and their applications in vivo. ABSTRACT Bone defects require materials with osteogenic, neurogenic, and angiogenic activity, yet designing such materials within high‐dimensional compositional spaces remains challenging. Here,
Kunlu Lin   +9 more
wiley   +1 more source

Neuromorphic Devices and Computing for Sensing, Memory, and Control

open access: yesAdvanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu   +2 more
wiley   +1 more source

Computationally Evidence‐Grounded Sequence‐First Design of Peptide Binders

open access: yesAdvanced Science, EarlyView.
BOND‐PEP enables controllable, sequence‐first peptide binder design by grounding generation in binding evidence retrieved for each target. It uses topology‐conditioned message passing to integrate relevant peptide examples with the target protein sequence, forming a residue‐level representation that guides the generation of diverse, target‐specific ...
Wenze Ding
wiley   +1 more source

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