scholarly journals Anti-breast cancer drug screening based on Neural Networks and QSAR model

2021 ◽  
Vol 12 (6) ◽  
pp. 401-406
Author(s):  
Bin Zhao ◽  
Renxiong Xie ◽  
Xia Jiang
Keyword(s):  
Breast Cancer ◽  
Biological Activity ◽  
Back Propagation ◽  
Qsar Model ◽  
Estrogen Receptor Α ◽  
Back Propagation Neural Network ◽  
Cancer Drug ◽  
Complex Process ◽  
Average Accuracy ◽  
Relationship Of

Breast cancer is one of the most lethal cancers, estrogen receptor α Subtype (ERα) is an important target. The compounds that able to fight ERα active may be candidates for treatment of breast cancer. The drug discovery process is a very large and complex process that often requires one selected from a large number of compounds. This paper considers the independence, coupling, and relevance of bioactivity descriptors, selects the 15 most potentially valuable bioactivity descriptors from 729 bioactivity descriptors. An optimized back propagation neural network is used for ERα, The pharmacokinetics and safety of 15 selected bioactivity descriptors were verified by gradient lifting algorithm. The results showed that these 15 biological activity descriptors could not only fit well with the nonlinear relationship of ERα activity can also accurately predict its pharmacokinetic characteristics and safety, with an average accuracy of 89.92~94.80%. Therefore, these biological activity descriptors have great medical research value.

scholarly journals Computational method for predicting Self-Interactions Protein using Recurrent Neural Network from Protein Evolutionary Information

2020 ◽  
Author(s):  
Ji-Yong An
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Biological Activities ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Scale Invariant ◽  
Average Accuracy

Abstract Self-interactions Protein (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive. How to develop effective computational approaches for identifying SIPs is a challenging task. In the paper, we presented a novelty computational method called RRN-SIFT, which combines the Recurrent Neural Network (RNN) with Scale Invariant Feature Transform (SIFT) to predict SIPs based on protein evolutionary information. The main advantage of the proposed RNN-SIFT model is that it used SIFT for extracting key feature by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM) and employed RNN classifier to carry out classification based on extracted features. Extensive experiments show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on yeast and human dataset. We also compared our performance with the Back Propagation Neural Network (BPNN), the state-of-the-art support vector machine (SVM) and other exiting methods. By comparing with experimental results, the performance of RNN-SIFT is significantly better than those of the BPNN, SVM and other previous methods in the domain. Therefore, we can come to the conclusion that the proposed RNN-SIFT model is useful tools and can execute incredibly well for predicting SIPs, as well as other bioinformatics tasks. In order to facilitate widely studies and encourage future proteomics research, a freely available web server called RNN-SIFT-SIPs was developed, and is available at http://219.219.62.123:8888/RNNSIFT/ and includes source code and SIPs datasets.

scholarly journals Back propagation neural network model for medical expenses in patients with breast cancer

2021 ◽  
Vol 18 (4) ◽  
pp. 3690-3698
Author(s):  
Feiyan Ruan ◽  
◽  
Xiaotong Ding ◽  
Huiping Li ◽  
Yixuan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Medical Expenses

scholarly journals QSAR, Ligand Based Design and Pharmacokinetic Studies of Parviflorons Derivatives as Anti-Breast Cancer Drug Compounds Against MCF-7 Cell Line

2020 ◽  
Author(s):  
Hadiza Lawal Abdulrahman ◽  
Adamu Uzairu ◽  
Sani Uba
Keyword(s):  
Breast Cancer ◽  
Cell Line ◽  
Cancer Cell Line ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Cancer Drug ◽  
Pharmacokinetic Studies ◽  
In Silico Studies ◽  
Mcf 7 ◽  
Positive Breast Cancer

AbstractThe anti-proliferative activities of Novel series of Parviflorons against MCF-7 breast cancer cell line was explored via in-silico studies like Quantitative Structure–Activity Relationship QSAR, designing new compounds and analyzing the pharmacokinetics properties of the designed compounds. From QSAR, model one emerged the best from the statistical assessments of (R2) = 0.9444, (R2adj) = 0.9273, (Q2) = 0.8945 and (R2pred) of 0.6214. The model was used in designing new derivative compounds, with higher effectiveness against estrogen positive breast cancer (MCF-7). The pharmacokinetics analysis carried out on the newly designed compounds showed that all the compounds passed the drug-likeness test and also the Lipinski rule of five, and they could further proceed to pre-clinical tests. The results indicates that the derivative compounds would serve as potent cure to estrogen positive breast cancer (MCF-7 cell line).

scholarly journals Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 74
Author(s):  
Shahab Abdulla ◽  
Mohammed Diykh ◽  
Sarmad K. D. Alkhafaji ◽  
Jonathan H. Greena ◽  
Hanan Al-Hadeethi ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Back Propagation ◽  
Epileptic Seizures ◽  
Back Propagation Neural Network ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Average Accuracy ◽  
Effective Selection ◽  
Kolmogorov Smirnov ◽  
Eeg Recordings

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.

scholarly journals Computer modeling of some anti-breast cancer compounds

2020 ◽  
Author(s):  
Hadiza Lawal Abdulrahman ◽  
Adamu Uzairu ◽  
Sani Uba
Keyword(s):  
Breast Cancer ◽  
Computer Modeling ◽  
Correlation Coefficient ◽  
Biological Activities ◽  
External Validation ◽  
Qsar Model ◽  
Cancer Drug ◽  
Breast Cancer Cell Lines ◽  
Model Parameters ◽  
Mcf 7

Abstract The research was aimed at exploring the biological activities of novel series of β-lactam derivatives against MCF-7 breast cancer cell lines via computer modeling such as quantitative structure-activity relationship (QSAR), designing new compounds and analyzing the drug likeliness of designed compounds. The QSAR model was highly robust as it also conforms to the least minimum requirement for QSAR model from the statistical assessments with a correlation coefficient squared (R2) of 0.8706, correlation coefficient adjusted squared (R2adj) of 0.8411, and cross-validation coefficient (Q2) of 0.7844. The external validation of R2pred was calculated as 0.6083 for model 4. The model parameters (MATS5i and MATS1s) were used in designing new derivative compounds with higher potency against estrogen-positive breast cancer. The pharmacokinetics test on the restructured compounds revealed that all the compounds passed the drug likeness test and they could further proceed to clinical trials. These reveal a breakthrough in medicine, in the research for breast cancer drug with higher effectiveness against the MCF-7 cell line.

Classification Approach for Breast Cancer Detection Using Back Propagation Neural Network

2020 ◽  
pp. 1410-1421 ◽  
Author(s):  
Aindrila Bhattacherjee ◽  
Sourav Roy ◽  
Sneha Paul ◽  
Payel Roy ◽  
Noreen Kausar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Confusion Matrix ◽  
Back Propagation ◽  
Breast Cancer Diagnosis ◽  
Back Propagation Neural Network ◽  
Classification Systems ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Sensitivity Specificity

According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%. According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%.

Controlling Robots Using Thai Speech Recognition

2014 ◽  
Vol 931-932 ◽  
pp. 1285-1290
Author(s):  
Tanasak Phanprasit
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
English Language ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Robotic Device ◽  
Optimum Control ◽  
Left Turn ◽  
Maximum Security ◽  
Average Accuracy

Maximum security is essential for a robotic device to achieve its optimum control. In this research, we present robotic motions, controlled by technological speech recognition techniques, using commands of Thai Speech Recognition (TSR). Examples of such speech words used are; Sai, Khwa, Na, Lang Khun, Long and Yood, which are the equivalent English language representations for; turn left, turn right, forwards, backwards, upwards, downwards, and stop, respectively. The speech commands are independent for any particular user and so, as a result, they are highly beneficial for general and practical use. The three main important parts of this paper comprise; Pre-processing, Discrete Fourier Transform (DFT), and Back Propagation Neural Network (BPN). The experimental results, when reviewed, exhibit results showing that the average accuracy percentages are equal to 71.00% for female commands, and 70.00% for male commands, respectively.

Classification Approach for Breast Cancer Detection Using Back Propagation Neural Network

2016 ◽  
pp. 210-221 ◽  
Author(s):  
Aindrila Bhattacherjee ◽  
Sourav Roy ◽  
Sneha Paul ◽  
Payel Roy ◽  
Noreen Kausar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Confusion Matrix ◽  
Back Propagation ◽  
Breast Cancer Diagnosis ◽  
Back Propagation Neural Network ◽  
Classification Systems ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Sensitivity Specificity

According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%. According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%.

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