| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Wangping Xiong, Kaiqi Wang, Shixiong Liu, Zhaoyang Liu, Yimin Zhu, Peng Liu, Ming Yang, Xian Zho. Multiple prescription pattern recognition model based on Siamese network. Mathematical biosciences and engineering : MBE. vol 20. issue 10. 2023-12-05. PMID:38052575. |
secondly, a drug attribute tagging strategy was used to quantify the functional features of each drug in the core prescriptions; finally, a bidirectional long short-term memory network (bilstm) was used to extract the relational features of the core prescriptions, and a vector representation similarity matrix was constructed in combination with the siamese network framework to calculate the similarity between the core prescriptions and the classical prescriptions. |
2023-12-05 |
2023-12-10 |
Not clear |
| Ashfia Jannat Keya, Hasibul Hossain Shajeeb, Md Saifur Rahman, M F Mridh. FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection. PloS one. vol 18. issue 12. 2023-12-01. PMID:38039283. |
the model combines the power of pre-trained bidirectional encoder representation of transformers (bert) embeddings with a deep convolutional neural network (cnn) having skip convolution block and long short-term memory (lstm). |
2023-12-01 |
2023-12-10 |
Not clear |
| Md Muntasir Zitu, Shijun Zhang, Dwight H Owen, Chienwei Chiang, Lang L. Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records. Frontiers in pharmacology. vol 14. 2023-07-28. PMID:37502211. |
we applied classical machine learning (support vector machine (svm)), deep learning (convolutional neural network (cnn) and bidirectional long short-term memory (bilstm)), and state-of-the-art transformer-based (bidirectional encoder representations from transformers (bert) and clinicalbert) methods trained and tested in the two different corpora and compared performance among them to detect drug-ade relationships. |
2023-07-28 |
2023-08-14 |
Not clear |
| Shoaib Ahmed, Dost Muhammad Khan, Saima Sadiq, Muhammad Umer, Faisal Shahzad, Khalid Mahmood, Heba Mohsen, Imran Ashra. Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques. PeerJ. Computer science. vol 9. 2023-06-22. PMID:37346678. |
the influence of term frequency-inverse document frequency, bag of words (bow), word2vec, and combination of tf-idf and bow are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (etc), logistic regression, naïve bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (cnn), bidirectional encoder representations from transformers (bert), long short-term memory (lstm), and recurrent neural network (rnn). |
2023-06-22 |
2023-08-14 |
Not clear |
| Chengmin Zhou, Bingding Huang, Pasi Frant. Representation Learning and Reinforcement Learning for Dynamic Complex Motion Planning System. IEEE transactions on neural networks and learning systems. vol PP. 2023-04-07. PMID:37028017. |
inspired by rl and representation learning, we introduced the aln-dsac: a hybrid motion planning algorithm where attention-based long short-term memory (lstm) and novel data replay combine with discrete soft actor-critic (sac). |
2023-04-07 |
2023-08-14 |
Not clear |
| Weizhen Xie, Julio I Chapeton, Srijan Bhasin, Christopher Zawora, John H Wittig, Sara K Inati, Weiwei Zhang, Kareem A Zaghlou. The medial temporal lobe supports the quality of visual short-term memory representation. Nature human behaviour. 2023-03-02. PMID:36864132. |
the medial temporal lobe supports the quality of visual short-term memory representation. |
2023-03-02 |
2023-08-14 |
human |
| Weizhen Xie, Julio I Chapeton, Srijan Bhasin, Christopher Zawora, John H Wittig, Sara K Inati, Weiwei Zhang, Kareem A Zaghlou. The medial temporal lobe supports the quality of visual short-term memory representation. Nature human behaviour. 2023-03-02. PMID:36864132. |
collectively, these findings provide converging evidence that the mtl is critically involved in the quality of stm representation. |
2023-03-02 |
2023-08-14 |
human |
| Franziska Hakansson, Dan Børge Jense. Automatic monitoring and detection of tail-biting behavior in groups of pigs using video-based deep learning methods. Frontiers in veterinary science. vol 9. 2023-01-30. PMID:36713870. |
two secondary frameworks were utilized, being a long short-term memory (lstm) network applied to sequences of image features (cnn-lstm), and a cnn applied to image representations of sequences (cnn-cnn). |
2023-01-30 |
2023-08-14 |
Not clear |
| Minghua Zhao, Min Yuan, Yaning Yang, Steven X X. CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2022-11-29. PMID:36445995. |
in this manuscript, we integrated a graph attention network (gat) for compounds and a long short-term memory neural network (lstm) for proteins, used end-to-end representation learning for both compounds and proteins, and proposed a deep learning algorithm, cpgl (cpi with gat and lstm) to optimize the feature extraction from compounds and proteins and to improve the model robustness and generalizability. |
2022-11-29 |
2023-08-14 |
human |
| Youngseob Eum, Eun-Hye Yo. Imputation of missing time-activity data with long-term gaps: A multi-scale residual CNN-LSTM network model. Computers, environment and urban systems. vol 95. 2022-07-11. PMID:35812524. |
the method consists of two steps: (1) the continuous bag-of-words word2vec model to convert daily ta sequences into a low-dimensional numerical representation to reduce complexity; (2) a multi-scale residual convolutional neural network (cnn)-stacked long short-term memory (lstm) model to capture multi-scale temporal dependencies across historical observations and to predict the missing tas. |
2022-07-11 |
2023-08-14 |
human |
| Honglei Wang, Hui Liu, Tao Huang, Gangshen Li, Lin Zhang, Yanjing Su. EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction. BMC bioinformatics. vol 23. issue 1. 2022-06-08. PMID:35676633. |
recently, convolutional neural network (cnn) and long short-term memory (lstm) have been demonstrated achievements in modification site prediction on account of their powerful functions in representation learning. |
2022-06-08 |
2023-08-14 |
Not clear |
| Daniel R Romano, William G Kronenberger, Shirley C Henning, Caitlin J Montgomery, Allison M Ditmars, Courtney A Johnson, Hannah D Bozell, Adeline D Yates, David B Pison. Verbal Working Memory Error Patterns and Speech-Language Outcomes in Youth With Cochlear Implants. Journal of speech, language, and hearing research : JSLHR. 2021-11-11. PMID:34762810. |
the goal of this study was to use error analysis of the letter-number sequencing (lns) task to test the hypothesis that vwm delays in ci users are due, in part, to fragile, underspecified phonological representations in short-term memory. |
2021-11-11 |
2023-08-13 |
Not clear |
| Michelene Kalinyak-Fliszar, Francine Kohen, Nadine Marti. Remediation of language processing in aphasia: Improving activation and maintenance of linguistic representations in (verbal) short-term memory. Aphasiology. vol 25. issue 10. 2021-10-21. PMID:22791930. |
remediation of language processing in aphasia: improving activation and maintenance of linguistic representations in (verbal) short-term memory. |
2021-10-21 |
2023-08-12 |
human |
| Andreas Eibeck, Daniel Nurkowski, Angiras Menon, Jiaru Bai, Jinkui Wu, Li Zhou, Sebastian Mosbach, Jethro Akroyd, Markus Kraf. Predicting Power Conversion Efficiency of Organic Photovoltaics: Models and Data Analysis. ACS omega. vol 6. issue 37. 2021-09-29. PMID:34568656. |
the bidirectional long short-term memory (gfsi/bilstm), attentive fingerprints (attentive fp), and simple graph neural networks (simple gnn) as well as baseline support vector regression (svr), random forests (rf), and high-dimensional model representation (hdmr) methods are trained to both the large and computational harvard clean energy project database (cepdb) and the much smaller experimental harvard organic photovoltaic 15 dataset (hopv15). |
2021-09-29 |
2023-08-13 |
Not clear |
| A W Olthof, P M A van Ooijen, L J Cornelisse. Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance. Journal of medical systems. vol 45. issue 10. 2021-09-07. PMID:34480231. |
in this study, we investigate these factors on the performance of four types [a fully connected neural network (dense), a long short-term memory recurrent neural network (lstm), a convolutional neural network (cnn), and a bidirectional encoder representations from transformers (bert)] of deep learning-based nlp. two datasets consisting of radiologist-annotated reports of both trauma radiographs (n = 2469) and chest radiographs and computer tomography (ct) studies (n = 2255) were split into training sets (80%) and testing sets (20%). |
2021-09-07 |
2023-08-13 |
Not clear |
| Hayley Weir, Keiran Thompson, Amelia Woodward, Benjamin Choi, Augustin Braun, Todd J Martíne. ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning. Chemical science. vol 12. issue 31. 2021-08-28. PMID:34447555. |
a neural image captioning approach consisting of a convolutional neural network (cnn) encoder and a long short-term memory (lstm) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable smiles representations. |
2021-08-28 |
2023-08-13 |
Not clear |
| Minsoo Cho, Jihwan Ha, Chihyun Park, Sanghyun Par. Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition. Journal of biomedical informatics. vol 103. 2021-07-28. PMID:32004641. |
the proposed model is based on bidirectional long short-term memory (bi-lstm) with conditional random field (crf) and enhanced by integrating two different character-level representations extracted from a convolutional neural network (cnn) and bi-lstm. |
2021-07-28 |
2023-08-13 |
Not clear |
| Qiuhai Yue, Randi C Marti. Maintaining verbal short-term memory representations in non-perceptual parietal regions. Cortex; a journal devoted to the study of the nervous system and behavior. vol 138. 2021-07-09. PMID:33677329. |
we found that during the delay period of a phonological stm task, phonological representations could be decoded in the left supramarginal gyrus (smg) but not the superior temporal gyrus (stg), a speech processing region, for word stimuli. |
2021-07-09 |
2023-08-13 |
Not clear |
| Valeria Isella, Cristina Rosazza, Maria Gazzotti, Jessica Sala, Sabrina Morzenti, Cinzia Crivellaro, Ildebrando Marco Appollonio, Carlo Ferrarese, Claudio Luzzatt. A Metabolic Imaging Study of Lexical and Phonological Naming Errors in Alzheimer Disease. American journal of Alzheimer's disease and other dementias. vol 35. 2021-02-22. PMID:32356456. |
findings suggest that neurodegeneration leads to: (1) phonemic errors, by interfering with phonological short-term memory, or with control over retrieval of phonological or prearticulatory representations, within the left supramarginal gyrus; (2) semantic errors, by disrupting general semantic or visual-semantic representations at the level of the left posterior middle and inferior occipitotemporal cortex, respectively; (3) formal errors, by damaging the lexical-phonological output interface in the left mid-anterior segment of middle and superior temporal gyri. |
2021-02-22 |
2023-08-13 |
Not clear |
| Giacomo Guidali, Alberto Pisoni, Nadia Bolognini, Costanza Papagn. Keeping order in the brain: The supramarginal gyrus and serial order in short-term memory. Cortex; a journal devoted to the study of the nervous system and behavior. vol 119. 2020-11-03. PMID:31091486. |
we demonstrated, for the first time, that the left supramarginal gyrus is one of the key nodes of the stm network involved in retaining an abstract representation of serial order information, independently from the content information, namely the nature of the item to be remembered, which instead is stored separately. |
2020-11-03 |
2023-08-13 |
human |