| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Austin Spadaro, Alok Sharma, Iman Dehzang. Predicting lysine methylation sites using a convolutional neural network. Methods (San Diego, Calif.). 2024-04-11. PMID:38604414. |
automated feature extraction from these representations of amino acids as well as cnn as a classifier have never been used for this problem. |
2024-04-11 |
2024-04-14 |
Not clear |
| Siuly Siuly, Smith K Khare, Enamul Kabir, Muhammad Tariq Sadiq, Hua Wan. An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network. Computers in biology and medicine. vol 174. 2024-04-10. PMID:38599069. |
to address these limitations, this study proposes a novel approach using a time-frequency representation (tfr) based alexnet convolutional neural network (cnn) model to explore eeg channel-based analysis and identify critical brain regions efficiently diagnosing pd from eeg data. |
2024-04-10 |
2024-04-13 |
Not clear |
| Min Gao, Shaohua Jiang, Weibin Ding, Ting Xu, Zhijian Ly. Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling. Journal of bioinformatics and computational biology. vol 22. issue 1. 2024-04-03. PMID:38567388. |
furthermore, the incorporation of queries, keys and values produced by the stacked convolutional neural network (cnn) enables our model to better integrate the local and global context of protein representation, leading to further improvements in the accuracy of dta prediction. |
2024-04-03 |
2024-04-05 |
Not clear |
| Keling Fei, Jianghui Wang, Lizhen Pan, Xu Wang, Baohong Che. A sleep staging model on wavelet-based adaptive spectrogram reconstruction and light weight CNN. Computers in biology and medicine. vol 173. 2024-03-28. PMID:38547654. |
these spectrograms enabled a light weight cnn to detect and extract finer details of different sleep stages, which improved the feature representation of eeg. |
2024-03-28 |
2024-03-31 |
Not clear |
| Hutuo Quan, Huicheng Lai, Guxue Gao, Jun Ma, Junkai Li, Dongji Che. Pairwise CNN-Transformer Features for Human-Object Interaction Detection. Entropy (Basel, Switzerland). vol 26. issue 3. 2024-03-28. PMID:38539717. |
the enhanced representations are superior to using cnn and transformer features individually. |
2024-03-28 |
2024-03-30 |
Not clear |
| Hadi Sedigh Malekroodi, Nuwan Madusanka, Byeong-Il Lee, Myunggi Y. Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns. Bioengineering (Basel, Switzerland). vol 11. issue 3. 2024-03-27. PMID:38534569. |
popular convolutional neural network (cnn) architectures, vgg and resnet, as well as vision transformers, swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. |
2024-03-27 |
2024-03-29 |
human |
| Hassaan Malik, Tayyaba Anee. Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds. PloS one. vol 19. issue 3. 2024-03-12. PMID:38470893. |
thus, we suggested four novel convolutional neural network (cnn) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. |
2024-03-12 |
2024-03-15 |
Not clear |
| Xinning Jin, Zhiqiang Wang, Jingyu Ma, Chuanzheng Liu, Xuerui Bai, Yubin La. Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of astragalus. Journal of the science of food and agriculture. 2024-03-09. PMID:38459895. |
the proposed model employs an improved transformer module and an improved ghost bottleneck as its backbone network, complementarily utilizing the benefits of cnn and transformer architectures for local and global feature representation. |
2024-03-09 |
2024-03-12 |
Not clear |
| Tianyuan Liu, Junyang Huang, Delun Luo, Liping Ren, Lin Ning, Jian Huang, Hao Lin, Yang Zhan. Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy. International journal of biological macromolecules. 2024-03-09. PMID:38460652. |
it incorporates a cross-attention model to globally correlate different representation vectors and a two-layer cnn module to learn local correlation features. |
2024-03-09 |
2024-03-12 |
Not clear |
| Md Sultan Mahmud, Oishy Saha, Shaikh Anowarul Fattah, Mohammad Saqui. Emotion Recognition with Reduced Channels Using CWT Based EEG Feature Representation and a CNN Classifier. Biomedical physics & engineering express. 2024-03-08. PMID:38457844. |
emotion recognition with reduced channels using cwt based eeg feature representation and a cnn classifier. |
2024-03-08 |
2024-03-11 |
Not clear |
| Dongmin Huang, Dongfang Yu, Yongshen Zeng, Xiaoyan Song, Liping Pan, Junli He, Lirong Ren, Jie Yang, Hongzhou Lu, Wenjin Wan. Generalized Camera-Based Infant Sleep-Wake Monitoring in NICUs: A Multi-Center Clinical Trial. IEEE journal of biomedical and health informatics. vol PP. 2024-03-07. PMID:38446652. |
using the face videos of 64 term and 39 preterm infants recorded in nicus, we proposed a novel sleep-wake classification strategy, called consistent deep representation constraint (cdrc), that forces the convolutional neural network (cnn) to make consistent predictions for the samples from different conditions but with the same label, to address the variances caused by infants and environments. |
2024-03-07 |
2024-03-09 |
Not clear |
| Jianning Chi, Jin Zhao, Siqi Wang, Xiaosheng Yu, Chengdong W. LGDNet: local feature coupling global representations network for pulmonary nodules detection. Medical & biological engineering & computing. 2024-03-01. PMID:38429443. |
to overcome the limited long-range dependency capabilities inherent in convolutional operations, a dual-branch module is designed to integrate the convolutional neural network (cnn) branch that extracts local features with the transformer branch that captures global representations. |
2024-03-01 |
2024-03-04 |
Not clear |
| Meghana V Palukuri, Edward M Marcott. DeepSLICEM: Clustering CryoEM particles using deep image and similarity graph representations. bioRxiv : the preprint server for biology. 2024-02-19. PMID:38370702. |
deepslicem explores 6 pretrained convolutional neural networks and one supervised siamese cnn for image representation, 10 pretrained deep graph neural networks for similarity graph node representations, and 4 methods for clustering, along with 8 methods for directly clustering the similarity graph. |
2024-02-19 |
2024-02-21 |
Not clear |
| Linshu Wang, Yuan Zho. MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features. RNA biology. vol 21. issue 1. 2024-02-15. PMID:38357904. |
we developed mrm-bert, a deep learning method that combined the pre-trained dnabert deep sequence representation module and the convolutional neural network (cnn) exploiting four traditional sequence feature encodings to improve the prediction performance. |
2024-02-15 |
2024-02-17 |
Not clear |
| Abinaya K, Sivakumar . A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer. Journal of imaging informatics in medicine. 2024-02-12. PMID:38343216. |
the proposed model leverages the capability of 3d cnn to extract spatiotemporal features from cervical images and employs the vit model to capture and learn complex feature representations. |
2024-02-12 |
2024-02-15 |
Not clear |
| Zihan Li, Yuan Zheng, Dandan Shan, Shuzhou Yang, Qingde Li, Beizhan Wang, Yuanting Zhang, Qingqi Hong, Dinggang She. ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation. IEEE transactions on medical imaging. vol PP. 2024-02-07. PMID:38324425. |
specifically, the cnn branch collaborates with the transformer branch to fuse the local features learned from cnn with the global representations obtained from transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. |
2024-02-07 |
2024-02-10 |
Not clear |
| Yuyang Sha, Weiyu Meng, Gang Luo, Xiaobing Zhai, Henry H Y Tong, Yuefei Wang, Kefeng L. MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks. Analytical chemistry. 2024-02-07. PMID:38324756. |
netomics, the second component, leverages a cnn architecture to extract more discriminative representations from the transformed samples. |
2024-02-07 |
2024-02-10 |
Not clear |
| Jiacheng Tang, Qi Kang, MengChu Zhou, Hao Yin, Siya Ya. MemeNet: Towards a Reliable Local Projection for Image Recognition via Semantic Featurization. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2024-02-02. PMID:38306266. |
first, local representations named memes are extracted from the activation map of a cnn model. |
2024-02-02 |
2024-02-05 |
Not clear |
| Xingyue Gu, Junkai Liu, Yue Yu, Pengfeng Xiao, Yijie Din. MFD-GDrug: Multimodal feature fusion-based deep learning for GPCR-drug interaction prediction. Methods (San Diego, Calif.). 2024-01-29. PMID:38286333. |
leveraging the esm pretrained model, we extract protein features and employ a cnn for protein feature representation. |
2024-01-29 |
2024-02-01 |
Not clear |
| Ihssan S Masad, Amin Alqudah, Shoroq Qaza. Automatic classification of sleep stages using EEG signals and convolutional neural networks. PloS one. vol 19. issue 1. 2024-01-26. PMID:38277364. |
the proposed methodology consists of three major steps: (i) segment the eeg signal into epochs with 30 seconds in length, (ii) convert epochs into 2d representation using time-frequency analysis, and (iii) feed the 2d time-frequency analysis to the 2d cnn. |
2024-01-26 |
2024-01-29 |
human |