| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| Jianing Hao, Qing Shi, Yilin Ye, Wei Zen. TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations. IEEE transactions on visualization and computer graphics. vol PP. 2023-10-27. PMID:37883273. |
recent studies have shown that the dl success is often attributed to effective data representations, fostering the fields of feature engineering and representation learning. |
2023-10-27 |
2023-11-08 |
Not clear |
| Xiaorui Wang, Chang-Yu Hsieh, Xiaodan Yin, Jike Wang, Yuquan Li, Yafeng Deng, Dejun Jiang, Zhenxing Wu, Hongyan Du, Hongming Chen, Yun Li, Huanxiang Liu, Yuwei Wang, Pei Luo, Tingjun Hou, Xiaojun Ya. Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center. Research (Washington, D.C.). vol 6. 2023-10-18. PMID:37849643. |
currently, the prediction of rcs with a dl framework is hindered by several factors, including: (a) lack of a standardized dataset for benchmarking, (b) lack of a general prediction model with powerful representation, and (c) lack of interpretability. |
2023-10-18 |
2023-11-08 |
Not clear |
| Jinzhou Wu, Yang Su, Ao Yang, Jingzheng Ren, Yi Xian. An improved multi-modal representation-learning model based on fusion networks for property prediction in drug discovery. Computers in biology and medicine. vol 165. 2023-09-10. PMID:37690287. |
accurate characterization of molecular representations plays an important role in the property prediction based on deep learning (dl) for drug discovery. |
2023-09-10 |
2023-10-07 |
Not clear |
| Jinzhou Wu, Yang Su, Ao Yang, Jingzheng Ren, Yi Xian. An improved multi-modal representation-learning model based on fusion networks for property prediction in drug discovery. Computers in biology and medicine. vol 165. 2023-09-10. PMID:37690287. |
in this study, a novel dl framework called multi-modal molecular representation learning fusion network (mmrlfn) is developed, which could simultaneously learn and integrate drug molecular features from molecular graphs and smiles sequences. |
2023-09-10 |
2023-10-07 |
Not clear |
| Jungseob Yi, Sangseon Lee, Sangsoo Lim, Changyun Cho, Yinhua Piao, Marie Yeo, Dongkyu Kim, Sun Kim, Sunho Le. Exploring chemical space for lead identification by propagating on chemical similarity network. Computational and structural biotechnology journal. vol 21. 2023-09-08. PMID:37680266. |
however, ml or dl methods rarely consider compound similarity information directly since ml and dl models use abstract representation of molecules for model construction. |
2023-09-08 |
2023-10-07 |
Not clear |
| Khawla Seddiki, Fŕed Eric Precioso, Melissa Sanabria, Michel Salzet, Isabelle Fournier, Arnaud Droi. Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification. Analytical chemistry. 2023-08-25. PMID:37624777. |
deep learning (dl) models are not only effective classifiers but are also well suited to jointly learn feature representation and classification tasks. |
2023-08-25 |
2023-09-07 |
Not clear |
| Shakir Khan, Tamanna Siddiqui, Azrour Mourade, Bayan Ibrahimm Alabduallah, Saad Abdullah Alajlan, Abrar Almjally, Bader M Albahlal, Amani Alfaif. Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture. The International journal, advanced manufacturing technology. 2023-06-26. PMID:37360660. |
dl (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. |
2023-06-26 |
2023-08-14 |
Not clear |
| Vishu Gupta, Alec Peltekian, Wei-Keng Liao, Alok Choudhary, Ankit Agrawa. Improving deep learning model performance under parametric constraints for materials informatics applications. Scientific reports. vol 13. issue 1. 2023-06-06. PMID:37277456. |
modern machine learning (ml) and deep learning (dl) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. |
2023-06-06 |
2023-08-14 |
Not clear |
| Qi Mo, Ting Zhang, Jianming Wu, Long Wang, Jiesi Lu. Identification of thrombopoiesis inducer based on a hybrid deep neural network model. Thrombosis research. vol 226. 2023-04-29. PMID:37119555. |
based on different types of molecular representations, three deep learning (dl) algorithms, namely recurrent neural networks (rnns), deep neural networks (dnns), and hybrid neural networks (rnns+dnns), were used to develop classification models to distinguish between active and inactive compounds. |
2023-04-29 |
2023-08-14 |
mouse |
| Minhaj Nur Alam, Rikiya Yamashita, Vignav Ramesh, Tejas Prabhune, Jennifer I Lim, R V P Chan, Joelle Hallak, Theodore Leng, Daniel Rubi. Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models. Scientific reports. vol 13. issue 1. 2023-04-13. PMID:37055475. |
towards this need, we have developed a self-supervised contrastive learning (cl) based pipeline for classification of referable vs non-referable dr. self-supervised cl based pretraining allows enhanced data representation, therefore, the development of robust and generalized deep learning (dl) models, even with small, labeled datasets. |
2023-04-13 |
2023-08-14 |
Not clear |