| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Pau Mora, Clara Garcia, Eugenio Ivorra, Mario Ortega, Mariano L Alcañi. Virtual Experience Toolkit: An End-to-End Automated 3D Scene Virtualization Framework Implementing Computer Vision Techniques. Sensors (Basel, Switzerland). vol 24. issue 12. 2024-06-27. PMID:38931621. |
traditionally, the creation of virtual content has fallen under one of two broad categories: manual methods crafted by graphic designers, which are labor-intensive and sometimes lack precision; traditional computer vision (cv) and deep learning (dl) frameworks that frequently result in semi-automatic and complex solutions, lacking a unified framework for both 3d reconstruction and scene understanding, often missing a fully interactive representation of the objects and neglecting their appearance. |
2024-06-27 |
2024-06-29 |
Not clear |
| Jincheng Zhang, Andrew R Willi. Bridging Formal Shape Models and Deep Learning: A Novel Fusion for Understanding 3D Objects. Sensors (Basel, Switzerland). vol 24. issue 12. 2024-06-27. PMID:38931658. |
this approach allows human-in-the-loop control over dl estimates by specifying lists of candidate objects, the shape variations that each object can exhibit, and the level of detail or, equivalently, dimension of the latent representation of the shape. |
2024-06-27 |
2024-06-29 |
Not clear |
| Benoit Dufumier, Pietro Gori, Sara Petiton, Robin Louiset, Jean-François Mangin, Antoine Grigis, Edouard Duchesna. Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry. NeuroImage. 2024-06-07. PMID:38848981. |
nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging datasets (n≈10k), along with deep ensemble, allows dl to learn robust and transferable representations to smaller-scale clinical datasets (n≤1k). |
2024-06-07 |
2024-06-10 |
Not clear |
| Benoit Dufumier, Pietro Gori, Sara Petiton, Robin Louiset, Jean-François Mangin, Antoine Grigis, Edouard Duchesna. Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry. NeuroImage. 2024-06-07. PMID:38848981. |
these findings suggest that the improvement of dl over sml in anatomical neuroimaging mainly comes from its capacity to learn meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry. |
2024-06-07 |
2024-06-10 |
Not clear |
| Stephen José Hanson, Vivek Yadav, Catherine Hanso. Dense Sample Deep Learning. Neural computation. 2024-04-26. PMID:38669696. |
despite the growing use of dl networks, little is understood about the learning mechanisms and representations that make these networks effective across such a diverse range of applications. |
2024-04-26 |
2024-04-29 |
Not clear |
| Ming Zhang, Ruimin Feng, Zhenghao Li, Jie Feng, Qing Wu, Zhiyong Zhang, Chengxin Ma, Jinsong Wu, Fuhua Yan, Chunlei Liu, Yuyao Zhang, Hongjiang We. A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation. Medical image analysis. vol 95. 2024-04-24. PMID:38657424. |
this study proposes an unsupervised and subject-specific dl method for qsm reconstruction based on implicit neural representation (inr), referred to as inr-qsm. |
2024-04-24 |
2024-04-28 |
Not clear |
| Hao Zhang, Xiaoqian Liu, Wenya Cheng, Tianshi Wang, Yuanyuan Che. Prediction of drug-target binding affinity based on deep learning models. Computers in biology and medicine. vol 174. 2024-04-12. PMID:38608327. |
this review article summarizes the available literature on dta prediction using dl models, including dta quantification metrics and datasets, and dl algorithms used for dta prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). |
2024-04-12 |
2024-04-15 |
Not clear |
| Ting Wang, Zu-Guo Yu, Jinyan L. CGRWDL: alignment-free phylogeny reconstruction method for viruses based on chaos game representation weighted by dynamical language model. Frontiers in microbiology. vol 15. 2024-04-04. PMID:38572227. |
in our method, the dynamical language (dl) model and the chaos game representation (cgr) method are used to characterize the frequency information and the context information of |
2024-04-04 |
2024-04-06 |
Not clear |
| Huiwen Wan. Prediction of protein-ligand binding affinity via deep learning models. Briefings in bioinformatics. vol 25. issue 2. 2024-03-06. PMID:38446737. |
however, the current dl models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. |
2024-03-06 |
2024-03-09 |
Not clear |
| Huiwen Wan. Prediction of protein-ligand binding affinity via deep learning models. Briefings in bioinformatics. vol 25. issue 2. 2024-03-06. PMID:38446737. |
next, we review the commonly used databases, input representations and dl models in this field. |
2024-03-06 |
2024-03-09 |
Not clear |
| Sumaya Alghamdi, Turki Turk. A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks. Scientific reports. vol 14. issue 1. 2024-02-23. PMID:38396138. |
accurate deep learning (dl) models to predict type 2 diabetes (t2d) are concerned not only with targeting the discrimination task but also with learning useful feature representation. |
2024-02-23 |
2024-02-26 |
Not clear |
| Vishu Gupta, Youjia Li, Alec Peltekian, Muhammed Nur Talha Kilic, Wei-Keng Liao, Alok Choudhary, Ankit Agrawa. Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks. Journal of cheminformatics. vol 16. issue 1. 2024-02-16. PMID:38365691. |
modern data mining techniques using machine learning (ml) and deep learning (dl) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. |
2024-02-16 |
2024-02-19 |
Not clear |
| Md Biddut Hossain, Rupali Kiran Shinde, Sukhoon Oh, Ki-Chul Kwon, Nam Ki. A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction. Sensors (Basel, Switzerland). vol 24. issue 3. 2024-02-10. PMID:38339469. |
conversely, dl methods use neural networks with hundreds of thousands of parameters and automatically learn relevant features and representations directly from the data. |
2024-02-10 |
2024-02-12 |
Not clear |
| Xiang Zhang, Hongxin Xiang, Xixi Yang, Jingxin Dong, Xiangzheng Fu, Xiangxiang Zeng, Haowen Chen, Keqin L. Dual-View Learning Based on Images and Sequences for Molecular Property Prediction. IEEE journal of biomedical and health informatics. vol PP. 2023-12-28. PMID:38153823. |
evaluation results on 14 small molecule admet datasets indicate that ismol outperforms machine learning (ml) and deep learning (dl) models based on single-modal representations. |
2023-12-28 |
2023-12-31 |
Not clear |
| Keke Huang, Hengxing Zhu, Dehao Wu, Chunhua Yang, Weihua Gu. EaLDL: Element-Aware Lifelong Dictionary Learning for Multimode Process Monitoring. IEEE transactions on neural networks and learning systems. vol PP. 2023-12-25. PMID:38145510. |
this constraint enables the continuous updating of the dictionary learning (dl) method to accommodate new modes without compromising the representation of previous modes. |
2023-12-25 |
2023-12-28 |
Not clear |
| Laura Pfaff, Julian Hossbach, Elisabeth Preuhs, Fabian Wagner, Silvia Arroyo Camejo, Stephan Kannengiesser, Dominik Nickel, Tobias Wuerfl, Andreas Maie. Self-supervised MRI denoising: leveraging Stein's unbiased risk estimator and spatially resolved noise maps. Scientific reports. vol 13. issue 1. 2023-12-19. PMID:38114575. |
recently, deep learning (dl)-based denoising methods achieved promising results by extracting complex feature representations from large data sets. |
2023-12-19 |
2023-12-23 |
Not clear |
| Pierre-Yves Libouban, Samia Aci-Sèche, Jose Carlos Gómez-Tamayo, Gary Tresadern, Pascal Bonne. The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks. International journal of molecular sciences. vol 24. issue 22. 2023-11-25. PMID:38003312. |
despite advancements in neural network architectures, system representation, and training techniques, the performance of dl affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. |
2023-11-25 |
2023-11-28 |
Not clear |
| Lavanya Umapathy, Taylor Brown, Raza Mushtaq, Mark Greenhill, J'rick Lu, Diego Martin, Maria Altbach, Ali Bilgi. Reducing annotation burden in MR: A novel MR-contrast guided contrastive learning approach for image segmentation. Medical physics. 2023-11-13. PMID:37956263. |
when working with limited annotation data, as in medical image segmentation tasks, learning domain-specific local representations can further improve the performance of dl models. |
2023-11-13 |
2023-11-20 |
Not clear |
| Yu Wang, Jingjie Zhang, Junru Jin, Leyi We. MolCAP: Molecular Chemical reActivity Pretraining and prompted-finetuning enhanced molecular representation learning. Computers in biology and medicine. vol 167. 2023-11-13. PMID:37956623. |
however, previous deep-learning (dl) methods focus excessively on learning robust inner-molecular representations by mask-dominated pretraining frameworks, neglecting abundant chemical reactivity molecular relationships that have been demonstrated as the determining factor for various molecular property prediction tasks. |
2023-11-13 |
2023-11-20 |
Not clear |
| Biaoshun Li, Mujie Lin, Tiegen Chen, Ling Wan. FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction. Briefings in bioinformatics. vol 24. issue 6. 2023-11-06. PMID:37930026. |
in this study, we propose a self-supervised pretraining deep learning (dl) framework, called functional group bidirectional encoder representations from transformers (fg-bert), pertained based on ~1.45 million unlabeled drug-like molecules, to learn meaningful representation of molecules from function groups. |
2023-11-06 |
2023-11-08 |
Not clear |