A new complement naïve Bayesian approach for biomedical data classification

Amare, Anagaw; Chang, Yang-Lang

doi:10.1007/s12652-018-1160-1

Cited by 30 publications

(14 citation statements)

References 11 publications

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“…Classification of biomedical data is crucial in the drug discovery process, especially in the target discovery subset. NB algorithms have shown great promise as classification tools for biomedical data, often filled with non-related information and data, known as noise [ 47 ]. NB techniques could also serve important roles in predicting ligand-target interactions, which could be a massive step forward in lead discovery [ 48 ].…”

Section: Naive Bayesian (Nb)mentioning

confidence: 99%

Machine Learning Methods in Drug Discovery

et al. 2020

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The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

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Section: Naive Bayesian (Nb)mentioning

confidence: 99%

Machine Learning Methods in Drug Discovery

et al. 2020

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“…The technique presented in this article can decrease the detection time in half, to 0.5 seconds, while increasing the detection rate to 91.06 percent. In [13], A. Anagram and Y.-L. Chang, 2019, the basic goal of this research is to build a model from noisy examples. By decreasing instances and enhancing classification performance, this method reduces model training time.…”

Section: Related Workmentioning

confidence: 99%

Optimize network intrusion detection system based on PCA feature extraction and three naïve bayes classifiers

kadom

Hashem

Jafer

2022

J. Phys.: Conf. Ser.

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Ransomware has become one of the major threats to private organizations, state and local organizations in the last three years, especially since the WannaCry malware. Ransomware is a child in the malware family that hijacks users’ data and related resources using security measures such as cryptographic and then demands bitcoin in compensation for the data that has been encrypted. Network Intrusion Detection Systems (NIDS) are now needed to protect data from hackers and keep information systems and networks from getting hurt as much as possible. It examines and predicts user behavior, which is then classified as an assault or expected behavior. This study will present a suggestion to improve hybrid (anomaly-misuse) NIDS by experimenting with three different Nave Bayes (NB) classifiers: Gaussian Naive Bayes (GNB), Multinomial Naive Bayes (MNB), and Complement Naive Bayes (CNB). To improve accuracy and reduce the computational time in NIDS, Principal Component Analysis (PCA) will be exploited. The UNSW-NB15 dataset was used to evaluate the suggested system’s performance, and the outcomes of the experiments are as follows: when using all features of UNSW-NB15 Datasets the Accuracy of GNB, MNB and CNB is 97%, 92%, and %94, when using PCA feature extraction with 10 PC the Accuracy of GNB, MNB and CNB is 100%, 88% and %100, when using PCA feature extraction with 20 PC the Accuracy of GNB, MNB and CNB is 100%, 92% and %100. When using PCA feature extraction with 30 PC, GNB, MNB, and CNB is 100%, 99%, and %100. According to the findings, 30 PCs improved the accuracy of the three algorithms and gave the best results, meaning that the increase in the number of PC leads to a rise in the system’s accuracy.

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“…The k-means algorithm is part of the "hard clustering" techniques and we exploited the kmeans function in the r package "cluster". 2 It is a partition clustering algorithm that allows to subdivide a set of objects in K groups based on their attributes, by partitioning the data set into unique homogeneous clusters whose observations are similar but different from other clusters. The k-means iteratively improves the initial centroids by minimizing the total intracluster variance, i.e., maximizing the similarity between the documents.…”

Section: The Analysis Of Correlating Informationmentioning

confidence: 99%

Visualizing correlations among Parkinson biomedical data through information retrieval and machine learning techniques

Frasca

Tortora

2021

Multimed Tools Appl

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In the last few years, the integration of researches in Computer Science and medical fields has made available to the scientific community an enormous amount of data, stored in databases. In this paper, we analyze the data available in the Parkinson’s Progression Markers Initiative (PPMI), a comprehensive observational, multi-center study designed to identify progression biomarkers important for better treatments for Parkinson’s disease. The data of PPMI participants are collected through a comprehensive battery of tests and assessments including Magnetic Resonance Imaging and DATscan imaging, collection of blood, cerebral spinal fluid, and urine samples, as well as cognitive and motor evaluations. To this aim, we propose a technique to identify a correlation between the biomedical data in the PPMI dataset for verifying the consistency of medical reports formulated during the visits and allow to correctly categorize the various patients. To correlate the information of each patient’s medical report, Information Retrieval and Machine Learning techniques have been adopted, including the Latent Semantic Analysis, Text2Vec and Doc2Vec techniques. Then, patients are grouped and classified into affected or not by using clustering algorithms according to the similarity of medical reports. Finally, we have adopted a visualization system based on the D3 framework to visualize correlations among medical reports with an interactive chart, and to support the doctor in analyzing the chronological sequence of visits in order to diagnose Parkinson’s disease early.

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A new complement naïve Bayesian approach for biomedical data classification

Cited by 30 publications

References 11 publications

Machine Learning Methods in Drug Discovery

Machine Learning Methods in Drug Discovery

Optimize network intrusion detection system based on PCA feature extraction and three naïve bayes classifiers

Visualizing correlations among Parkinson biomedical data through information retrieval and machine learning techniques

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