Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis

Tripathy, Jogeswar; Dash, Rasmita; Pattanayak, Binod Kumar; Mishra, Sambit Kumar; Mishra, Tapas Kumar; Puthal, Deepak

doi:10.3390/bdcc6010024

Cited by 19 publications

(6 citation statements)

References 28 publications

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“…As demonstrated in a series of studies, this approach is applicable to the selection of the analysis methods of biological materials such as DNA, RNA, etc. [31][32][33]. The same method can be applied to the investigation of the effects of their properties on their ranks [34][35][36][37].…”

Section: Discussionmentioning

confidence: 99%

Optimization of Machine Learning Algorithms for Proteomic Analysis Using TOPSIS

Javanbakht

Chakravorty

2022

JES

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The present study focuses on a new application of the TOPSIS method for the optimization of machine learning algorithms, supervised neural networks (SNN), the quick classifier (QC), and genetic algorithm (GA) for proteomic analysis. The main hypotheses are that the change in the weights of alternatives could affect the ranking of algorithms. The obtained data confirmed this hypothesis for their ranking. Moreover, adding labor as a cost criterion to the list of criteria did not affect this ranking. This was because candidate 3 had better fuzzy membership degrees than the two other candidates concerning their criteria. This work showed the importance of the value of the fuzzy membership degrees of the cost criterion of the algorithms in their ranks. The values of the fuzzy membership degrees of the algorithms used for proteomic analysis could determine their priority according to their score differences. One of the advantages of this study was that the studied methods could be compared according to their characteristics. Another advantage was that the obtained results could be related to the new ones after improving these methods. The results of this work could be applied in engineering, where the analysis of proteins would be performed with these methods.

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Section: Discussionmentioning

confidence: 99%

Optimization of Machine Learning Algorithms for Proteomic Analysis Using TOPSIS

Javanbakht

Chakravorty

2022

JES

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“…From performance evaluation measures, TOPSIS score is obtained. TOPSIS score is being used in many decision-making processes for selecting the best model (Zaidan et al, 2015;Budhlakoti et al, 2020;Zulqarnain et al, 2020;Tripathy et al,2022) Here also Random Forest Model secured the 1 st rank with TOPSIS score of 0.938392 that establishes the model as better than other alternative models for selective sweep classification. Considering all of the above evaluation measures, Random Forest model has been selected as the best model for selective sweep prediction.…”

Section: Number Of Trees 500mentioning

confidence: 99%

An advanced approach for predicting selective sweep in the genomic regions using machine learning techniques

Sarkar,

Mishra,

Sinha

et al. 2024

Genet Resour Crop Evol

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Selective Sweep is an important phenomenon in the aspect of natural selection. It plays significant role in adaptability as well as survival of species, crop varieties etc. Various existing approaches for selective sweep analysis are mostly built on traditional rule base approach which lack the advanced approaches such as machine learning and deep learning and often result in poor prediction accuracy. In this study a new method or model for the prediction of selective sweep has been presented. This method has been initiated with simulation, preceded through feature extraction and selection and finally fed to different machine learning algorithms. Here eight different machine learning based methods have been implemented -1) Support Vector Machine (SVM), 2) Regression Tree, 3) Random Forest, 4) Naive Bayes, 5) Multiple logistic regression, 6) K-Nearest Neighbor (KNN), 7) Gradient boosting and 8) Artificial Neural Network (ANN) and results of their comparative evaluations are presented. It has been observed that random forest model outperformed to its counterparts in terms of evaluation matrices with an AUC score of 0.8448 as well as 1 st rank in TOPSIS analysis. Further, a robust model for selective sweep prediction based upon random forest has been developed. Model developed in the current study has outperformed to other existing approaches for prediction and analysis of selective sweep. This new approach for selective sweep analysis is excellent in its accuracy as well as reliability.

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“…Spyder with Tensorflow is utilized here, and the ANN application is written in Python. Spyder makes debugging considerably smoother [23,24]. Hence, we implement the model with a spyder environment.…”

Section: Implementation Of Artificial Neural Network (Ann) and Deep N...mentioning

confidence: 99%

Enhancing Cardiovascular Disease Prediction Based on AI and IoT Concepts

Debasish Swapnesh Kumar Nayak

2023

IJRITCC

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One-third of all deaths worldwide yearly are attributable to cardiovascular disease (CVD). In contrast to the 7% of the wealthy who experience premature death, 43% of the poor do. Lifestyle diseases like obesity and diabetes are to blame. The importance of early identification of heart disease was demonstrated, and premature mortality was kept to a minimum. Combining clinical and biochemical data is essential for the early diagnosis of heart illness. Numerous IoT-enabled wearable healthcare applications have been created and released in recent years. Although the ability of wearable devices to share patient health data is expanding, it remains challenging to predict and identify health problems. Security, data storage, and patient monitoring are all part of the system. Artificial intelligence (AI) therapies may one day change the face of cardiology by providing doctors with cutting-edge data analysis and therapeutic decision-making resources. As the volume and complexity of data continue to increase, AI tools like machine learning (ML) and deep learning (DL) can assist medical professionals in learning more. Suppose we want to provide medical care to the elderly and those with chronic illnesses in the comfort of their own homes. In that case, we must upgrade our communication and information technology systems. The implemented DNN model's accuracy is amazing at 95.34 % and can yield other noteworthy outcomes when used to identify CVDs. We discuss and suggest the most suitable AI-IoT models for early CVD prediction and detection to reduce computational costs and increase time efficiency.

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Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis

Cited by 19 publications

References 28 publications

Optimization of Machine Learning Algorithms for Proteomic Analysis Using TOPSIS

Optimization of Machine Learning Algorithms for Proteomic Analysis Using TOPSIS

An advanced approach for predicting selective sweep in the genomic regions using machine learning techniques

Enhancing Cardiovascular Disease Prediction Based on AI and IoT Concepts

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