Results 71 to 80 of about 1,932,979 (312)

Performance of networks of artificial neurons: The role of clustering

, 2004
The performance of the Hopfield neural network model is numerically studied on various complex networks, such as the Watts-Strogatz network, the Barab{\'a}si-Albert network, and the neuronal network of the C. elegans.
B. M. Forrest, Beom Jun Kim, C. Cherniak, D. J. Watts, D. Stauffer, J. Hertz, J. Karbowski, L. da Fontoura Costa, O. Sporns, O. Sporns, S. N. Dorogovtsev   +10 more
core   +1 more source

Data‐driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Molecular Oncology, EarlyView.
This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh, Nikola Tom, Anna Schrøder Lassen, Helene Brasch Lind Petersen, Ulrik Kristoffer Stoltze, Karin Wadt, Kjeld Schmiegelow, Matteo Tiberti, Elena Papaleo   +8 more
wiley   +1 more source

GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search

Applied Sciences
Biological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to ...
Yi Wang, Feng Zhan, Cuiyu Huang, Yiran Huang   +3 more
doaj   +1 more source

Binarized Neural Network with Silicon Nanosheet Synaptic Transistors for Supervised Pattern Classification

Scientific Reports, 2019
In the biological neural network, the learning process is achieved through massively parallel synaptic connections between neurons that can be adjusted in an analog manner.
Sungho Kim, Bongsik Choi, Jinsu Yoon, Yongwoo Lee, Hee-Dong Kim, Min-Ho Kang, Sung-Jin Choi   +6 more
doaj   +1 more source

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

Nature Communications, 2020
Perception is thought to be shaped by the environments for which organisms are optimized. These influences are difficult to test in biological organisms but may be revealed by machine perceptual systems optimized under different conditions.
Mark R. Saddler, Ray Gonzalez, Josh H. McDermott   +2 more
semanticscholar   +1 more source

Blind Nonnegative Source Separation Using Biological Neural Networks [PDF]

Neural Computation, 2017
Blind source separation—the extraction of independent sources from a mixture—is an important problem for both artificial and natural signal processing. Here, we address a special case of this problem when sources (but not the mixing matrix) are known to ...
Cengiz Pehlevan, S. Mohan, D. Chklovskii
semanticscholar   +1 more source

Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case‐control study

Molecular Oncology, EarlyView.
This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima, Yuko Takami, Ken Kawabe, Kazuhiro Tanabe, Chihiro Hayashi, Mikio Mikami, Tetsuya Kusumoto   +6 more
wiley   +1 more source

Correlation Analysis-Based Neural Network Self-Organizing Genetic Evolutionary Algorithm

IEEE Access, 2019
Recent years, there has been an ever increasing interest and investment on Artificial Intelligence (AI), both academic and industrial. As the hotspots in AI, Artificial Neural Networks (ANNs) have already been applied to a lot of different applications ...
Zenghao Chai, Xu Yang, Zhilin Liu, Yunlin Lei, Wenhao Zheng, Mengyao Ji, Jinfeng Zhao   +6 more
doaj   +1 more source

Decrypting cancer's spatial code: from single cells to tissue niches

Molecular Oncology, EarlyView.
Spatial transcriptomics maps gene activity across tissues, offering powerful insights into how cancer cells are organised, switch states and interact with their surroundings. This review outlines emerging computational, artificial intelligence (AI) and geospatial approaches to define cell states, uncover tumour niches and integrate spatial data with ...
Cenk Celik, Shi Pan, Eloise Withnell, Hou Wang Lam, Maria Secrier   +4 more
wiley   +1 more source

An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence [PDF]

, 2022
Shengjie Zheng, Lang Qian, Pingsheng Li, Chenggang He, Xiaoqin Qin, Xiaojian Li   +5 more
openalex   +1 more source