| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Songlin Lu, Yuanfang Huang, Wan Xiang Shen, Yu Lin Cao, Mengna Cai, Yan Chen, Ying Tan, Yu Yang Jiang, Yu Zong Che. Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification. PNAS nexus. vol 3. issue 8. 2024-08-28. PMID:39192845. |
here, we introduced novel 2d image-like dual signal and component aggregated representations by restructuring raman spectra and principal components, which enables spectroscopic dl for enhanced cell phenotype and signature identification. |
2024-08-28 |
2024-08-30 |
Not clear |
| Muhammad Usman Khalid, Malik Muhammad Nauman, Sheeraz Akram, Kamran Al. Three layered sparse dictionary learning algorithm for enhancing the subject wise segregation of brain networks. Scientific reports. vol 14. issue 1. 2024-08-17. PMID:39154133. |
it differs from existing dl methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. |
2024-08-17 |
2024-08-20 |
human |
| James Brundage, Joshua P Barrios, Geoffrey H Tison, James P Pirruccell. Genetics of Cardiac Aging Implicate Organ-Specific Variation. medRxiv : the preprint server for health sciences. 2024-08-16. PMID:39148824. |
we hypothesized that a video-based dl model provided with heart-masked mri data would capture a rich yet cardiac-specific representation of cardiac aging. |
2024-08-16 |
2024-08-18 |
human |
| Amal Alshardan, Hany Mahgoub, Nuha Alruwais, Abdulbasit A Darem, Wafa Sulaiman Almukadi, Abdullah Mohame. Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection. Scientific reports. vol 14. issue 1. 2024-08-09. PMID:39122782. |
inverse problems involve reconstructing unknown structures or parameters from observed data, and the dl model excels in learning complex representations and mappings. |
2024-08-09 |
2024-08-13 |
Not clear |
| Fulin Cai, Teresa Wu, Fleming Y M Lur. E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing. Sensors (Basel, Switzerland). vol 24. issue 14. 2024-07-27. PMID:39066018. |
however, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (tfr), challenge radar sensing applications using dl. |
2024-07-27 |
2024-07-29 |
Not clear |
| Baptiste Gross, Antonin Dauvin, Vincent Cabeli, Virgilio Kmetzsch, Jean El Khoury, Gaëtan Dissez, Khalil Ouardini, Simon Grouard, Alec Davi, Regis Loeb, Christian Esposito, Louis Hulot, Ridouane Ghermi, Michael Blum, Yannis Darhi, Eric Y Durand, Alberto Romagnon. Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data. Scientific reports. vol 14. issue 1. 2024-07-25. PMID:39048590. |
deep learning (dl) has shown potential to provide powerful representations of bulk rna-seq data in cancer research. |
2024-07-25 |
2024-07-28 |
Not clear |
| Baptiste Gross, Antonin Dauvin, Vincent Cabeli, Virgilio Kmetzsch, Jean El Khoury, Gaëtan Dissez, Khalil Ouardini, Simon Grouard, Alec Davi, Regis Loeb, Christian Esposito, Louis Hulot, Ridouane Ghermi, Michael Blum, Yannis Darhi, Eric Y Durand, Alberto Romagnon. Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data. Scientific reports. vol 14. issue 1. 2024-07-25. PMID:39048590. |
however, there is no consensus regarding the impact of design choices of dl approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. |
2024-07-25 |
2024-07-28 |
Not clear |
| Baptiste Gross, Antonin Dauvin, Vincent Cabeli, Virgilio Kmetzsch, Jean El Khoury, Gaëtan Dissez, Khalil Ouardini, Simon Grouard, Alec Davi, Regis Loeb, Christian Esposito, Louis Hulot, Ridouane Ghermi, Michael Blum, Yannis Darhi, Eric Y Durand, Alberto Romagnon. Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data. Scientific reports. vol 14. issue 1. 2024-07-25. PMID:39048590. |
to address this problem, we evaluate the performance of various design choices of dl representation learning methods using tcga and depmap pan-cancer datasets and assess their predictive power for survival and gene essentiality predictions. |
2024-07-25 |
2024-07-28 |
Not clear |
| Baptiste Gross, Antonin Dauvin, Vincent Cabeli, Virgilio Kmetzsch, Jean El Khoury, Gaëtan Dissez, Khalil Ouardini, Simon Grouard, Alec Davi, Regis Loeb, Christian Esposito, Louis Hulot, Ridouane Ghermi, Michael Blum, Yannis Darhi, Eric Y Durand, Alberto Romagnon. Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data. Scientific reports. vol 14. issue 1. 2024-07-25. PMID:39048590. |
dl representation methods, however, are the most efficient to predict the gene essentiality of cell lines. |
2024-07-25 |
2024-07-28 |
Not clear |
| Baptiste Gross, Antonin Dauvin, Vincent Cabeli, Virgilio Kmetzsch, Jean El Khoury, Gaëtan Dissez, Khalil Ouardini, Simon Grouard, Alec Davi, Regis Loeb, Christian Esposito, Louis Hulot, Ridouane Ghermi, Michael Blum, Yannis Darhi, Eric Y Durand, Alberto Romagnon. Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data. Scientific reports. vol 14. issue 1. 2024-07-25. PMID:39048590. |
our results suggest that the impact of dl representations and of pretraining are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. |
2024-07-25 |
2024-07-28 |
Not clear |
| 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 |