A coefficient comparison of weighted similarity extreme learning machine for drug screening

Kudisthalert, Wasu; Pasupa, Kitsuchart

doi:10.1109/kst.2016.7440525

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…There are some evidences that showed the potential of using WELM classification method on one of the most challenging datasets, Maximum Unbiased Validation Dataset, which is dramatic imbalance between classes and highly diverse in structure. The experiment result showed that WELM give better performance than other standard methods, i.e., SVM, Random Forest, and Similarity Searching [38]- [40]. In this study, we demonstrated a list of contribution as follow:…”

Section: Related Workmentioning

confidence: 80%

Counting and Classification of Malarial Parasite From Giemsa-Stained Thin Film Images

2020

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Malaria is a life-threatening disease causing by an infection of the protozoan parasite Plasmodium. Plasmodium falciparum is the deadliest and most common human infected parasites hosted by anopheles mosquito vector. To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained parasites is commonly done. The microscopy diagnosis is somewhat time consuming and requires well-trained malaria experts to interpret what they see under the microscope. To address this limitation, an automated malaria infected diagnosis is needed. This work proposed a computer-aided automated diagnosis system that can perform remote field diagnosis with high accuracy while requiring less computational demands. The proposed framework consists of two main parts that are red blood cell counting and parasite life-cycle stage classification. The counting process is performed by computer vision techniques, namely Hough transform. Different machine learning techniques, i.e., Multilayer Perceptron, Linear Discriminant Analysis, Support Vector Machine, and Weighted Similarity Extreme Learning Machine, are employed in the classification task. We also demonstrated that combining hand-crafted and deep-learned features can enhance the overall performance of the framework. The experimental results showed that the proposed methods could correctly count and classify at 97.94% and 98.12% accuracy, respectively. The overall proposal system can achieve at 96.18% accuracy. This is achieved by WELM in conjunction with deep-learned (AlexNet_FC7) and the hand-crafted (color) features. INDEX TERMS Combining features, Giemsa-stained thin film, malaria.

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Section: Related Workmentioning

confidence: 80%

Counting and Classification of Malarial Parasite From Giemsa-Stained Thin Film Images

2020

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“…In the regularization method, the mean square sum of network weights is added to the typical objective function, and the network performance function is improved to the formula (11) and formula (12) ζ 2, the training emphasizes the reduction of weight, leading to a relatively great network error, which eventually makes the output of the neural network smoother and prevents the occurrence of minimum points [30].…”

Section: Bayesian Regularization Algorithmmentioning

confidence: 99%

“…Hence, the traditional SVM is not suitable for multi-classification. Meanwhile, apart from the low accuracy, too much iterative calculation and tuning need to be consumed for SLFN, leading to a long training time [11]. In this regard, Kudisthalert et al discovered Extreme Learning Machine (ELM) prediction model, using single-layer feedforward neural network structure.…”

Section: Introductionmentioning

confidence: 99%

Efficient Compound Activity Prediction Model Based on Bayesian Regularization Method for Candidate Drugs

et al. 2022

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In breast cancer diagnoses, antagonist compounds are used to resist α Subtype of Estrogen Receptor (ERα) bioactivity. However, those compounds are difficult to be obtained in the process of drug screening. In this paper, an efficient bioactivity prediction model is proposed with consideration of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties as an auxiliary prediction to achieve the minimization of the damage caused to human body whereas maximizing the efficacy of estrogen activity inhibitors. In the proposed prediction model, Pearson correlation analysis and Bayesian regularization algorithm under neural network are specifically used to train and analyze the data of bioactivity. To calculate the loss of neural network, cross-entropy and supervise learning are applied. The results show that the accuracy of our proposed compound prediction model can reach up to 92.2%, 94.3%, and 90.2% for Caco2, CYP3A4, and hERG, respectively. Moreover, the Area Under the Curve (AUC) value of our proposed model is 0.9752, which is 13-14% higher than that of SVM method. In addition, the Matthews Correlation Coefficient (MCC) can reach up to 0.93 ± 0.03, 37-40% higher than that of SVM method. Due to correlation analysis and valid data screening, our model can shorten the prediction time by 15%. Due to its advantages in accuracy and speed of prediction, this research can contribute to the upgrading of diagnosis and treatment of breast cancer.INDEX TERMS Deep neural networks, drug discovery, machine learning, virtual screening.

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Improved Identification of Imbalanced Multiple Annotation Intent Labels with a Hybrid BLSTM and CNN Model and Hybrid Loss Function

Vatathanavaro

Pasupa

Sirirattanajakarin³

et al. 2021

Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track

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A coefficient comparison of weighted similarity extreme learning machine for drug screening

Cited by 5 publications

References 7 publications

Counting and Classification of Malarial Parasite From Giemsa-Stained Thin Film Images

Counting and Classification of Malarial Parasite From Giemsa-Stained Thin Film Images

Efficient Compound Activity Prediction Model Based on Bayesian Regularization Method for Candidate Drugs

Improved Identification of Imbalanced Multiple Annotation Intent Labels with a Hybrid BLSTM and CNN Model and Hybrid Loss Function

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