Neural Networks with Emotion Associations, Topic Modeling and Supervised Term Weighting for Sentiment Analysis

Hájek, Petr; Barushka, Aliaksandr; Munk, Michal

doi:10.1142/s0129065721500131

Cited by 22 publications

(4 citation statements)

References 69 publications

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“…Additionally, molecular fingerprints, substructure fingerprints, and 2D compound images generated by the RDKit package were utilized as input features 42 , 43 . These features were then used to train both traditional machine learning algorithms such as support vector machines (SVMs) 44 , 45 , k-nearest neighbors (kNNs) 46 , 47 , random forests 48 , 49 , and naive Bayes classifiers 50 – 52 , as well as deep learning methods including dense neural networks (DNNs) 53 , 54 , 1D convolutional neural networks (CNNs), and 2D CNNs 21 , 38 , 55 .…”

Section: Introductionmentioning

confidence: 99%

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Huang,

Yang,

Liao

et al. 2024

Sci Rep

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Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood–brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E−05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

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Section: Introductionmentioning

confidence: 99%

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Huang,

Yang,

Liao

et al. 2024

Sci Rep

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“…With the development of technologies that can collect medical data, the need to educate experts who will be able to work in the field of computer medicine is also growing. Based on the CRISP-DM methodology [2], the tasks of computer medicine fall under the tasks of classification and prediction [3,4]. Thus, the ability to solve tasks such as the classification of cancer diseases is closely related to how the student understands the models that can be used to achieve the goal.…”

Section: Introductionmentioning

confidence: 99%

Could You Understand Me? The Relationship among Method Complexity, Preprocessing Complexity, Interpretability, and Accuracy

2023

Self Cite

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The need to train experts who will be able to apply machine learning methods for knowledge discovery is increasing. Building an effective machine learning model requires understanding the principle of operation of the individual methods and their requirements in terms of data pre-preparation, and it is also important to be able to interpret the acquired knowledge. This article presents an experiment comparing the opinion of the 42 students of the course called Introduction to Machine Learning on the complexity of the method, preprocessing, and interpretability of symbolic, subsymbolic and statistical methods with the correctness of individual methods expressed on the classification task. The methodology of the implemented experiment consists of the application of various techniques in order to search for optimal models, the accuracy of which is subsequently compared with the results of a knowledge test on machine learning methods and students’ opinions on their complexity. Based on the performed non-parametric and parametric statistic tests, the null hypothesis, which claims that there is no statistically significant difference in the evaluation of individual methods in terms of their complexity/demandingness, the complexity of data preprocessing, the comprehensibility of the acquired knowledge and the correctness of the classification, is rejected.

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“…Current automatic recognition systems that contain all three phases (detection, extraction and classification) work with different methods. One of the currently most used methods is the application of neural networks [1,2].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net

et al. 2022

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In this paper, we compare three different models of multilayer neural networks in terms of their success in the classification phase. These models were designed for EmguCV, ML.NET and Tensorflow.Net libraries, which are currently among the most widely used libraries in the implementation of an automatic recognition system. Using the EmguCV library, we achieved a success rate in the classification of human faces of 81.95% and with ML.NET, which was based on the pre-trained ResNet50 model using convolution layers, up to 91.15% accuracy. The result of the success of the classification process was influenced by the time required for training and also the time required for the classification itself. The Tensorflow.Net model did not show sufficient classification ability when classifying using vector distances; the highest success rate of classification was only 13.31%. Neural networks were trained on a dataset with 1454 photographs of faces involving 43 people. At a time when neural networks are becoming more and more used for applications of different natures, it is necessary to choose the right model in the classification process that will be able to achieve the required accuracy with the minimum time required for training. The application created by us allows the insertion of images and the creation of their own datasets, on the basis of which the user can train a model with its own parameters. Models can then be saved and integrated into other applications.

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Neural Networks with Emotion Associations, Topic Modeling and Supervised Term Weighting for Sentiment Analysis

Cited by 22 publications

References 69 publications

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Could You Understand Me? The Relationship among Method Complexity, Preprocessing Complexity, Interpretability, and Accuracy

Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net

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