Results 181 to 190 of about 30,442 (292)

Artificial intelligence for adaptive neuromodulation in drug‐resistant epilepsy

Epilepsia, EarlyView.
Abstract Drug‐resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and ...
Amir Hossein Daraie, Arianna Damiani, Mahsa Khoshkhou, Luis A. Sanchez, Rachel J. Smith, Shruti H. Agashe, Jorge A. Gonzalez‐Martinez, Jennifer Hopp, Adam S. Charles, Sridevi V. Sarma, Joon‐Yi Kang   +10 more
wiley   +1 more source

Use of deep learning to predict chronic wasting disease status based on animal movement. [PDF]

Mov Ecol
Blaha RL, Silva CJ, Cunningham SA, DeVivo MT, Edmunds DR, Boudreau MR.   +5 more
europepmc   +1 more source

Frontiers in EEG as a tool for the management of pediatric epilepsy: Past, present, and future

Epilepsia Open, EarlyView.
Abstract Electroencephalography (EEG) has evolved into an indispensable tool in pediatric epilepsy, fundamentally transforming the diagnosis, classification, and management of this condition. This review chronicles the historical journey of EEG from its groundbreaking inception to its current pivotal role in delineating distinct pediatric epilepsy ...
Hiroki Nariai
wiley   +1 more source

ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms. [PDF]

Plant Genome
Raihan T, Kim CH, Shimono H, Kimura A, Iwata H.   +4 more
europepmc   +1 more source

The power of variational disentangled autoencoders: The power of variational disentangled autoencoders

Have you ever wished artificial intelligence could understand not just data, but the reasons behind it? Variational Disentangled Autoencoders offer exactly that.
Siti Hafizah Ab Hamid
core  

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

DPAS: disease-associated peptide anomaly score for identifying pathogenic peptides via one-class learning. [PDF]

Sci Rep
Khalid Z, Khalid R, Sezerman OU.
europepmc   +1 more source

The dynamics of representation learning in shallow, non-linear autoencoders

Autoencoders are the simplest neural network for unsupervised learning, and thus an ideal framework for studying feature learning. While a detailed understanding of the dynamics of linear autoencoders has recently been obtained, the study of non-linear ...
Goldt, Sebastian, Refinetti, Maria
core  

Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges

Epilepsia Open, EarlyView.
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus, Cesar Santana‐Gomez, Ruby G. Escalante, Dominique Duncan, Pedro F. Viana, Giulia Sofia Cereda, Naoto Kuroda, Aristea S. Galanopoulou   +7 more
wiley   +1 more source

FireProtASR 2.0: evolution-guided Design of Protein Ancestors and Successors with phylogenetics and machine learning. [PDF]

Brief Bioinform
Kohout P, Lacko D, Musil M, Borko S, Stepanek M, Velecky J, Kabourek P, Khan RT, Rosinska M, Damborsky J, Mazurenko S, Bednar D.   +11 more
europepmc   +1 more source