scholarly journals Alzheimer Identification through DNA Methylation and Artificial Intelligence Techniques

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2482
Author(s):  
Gerardo Alfonso Perez ◽  
Javier Caballero Villarraso
Keyword(s):  
Dna Methylation ◽  
Classification Problem ◽  
Support Vector ◽  
Classification Rate ◽  
Robust Algorithms ◽  
Nonlinear Approach ◽  
Multiple Datasets ◽  
Non Linear ◽  
Healthy Control ◽  
The Impact

A nonlinear approach to identifying combinations of CpGs DNA methylation data, as biomarkers for Alzheimer (AD) disease, is presented in this paper. It will be shown that the presented algorithm can substantially reduce the amount of CpGs used while generating forecasts that are more accurate than using all the CpGs available. It is assumed that the process, in principle, can be non-linear; hence, a non-linear approach might be more appropriate. The proposed algorithm selects which CpGs to use as input data in a classification problem that tries to distinguish between patients suffering from AD and healthy control individuals. This type of classification problem is suitable for techniques, such as support vector machines. The algorithm was used both at a single dataset level, as well as using multiple datasets. Developing robust algorithms for multi-datasets is challenging, due to the impact that small differences in laboratory procedures have in the obtained data. The approach that was followed in the paper can be expanded to multiple datasets, allowing for a gradual more granular understanding of the underlying process. A 92% successful classification rate was obtained, using the proposed method, which is a higher value than the result obtained using all the CpGs available. This is likely due to the reduction in the dimensionality of the data obtained by the algorithm that, in turn, helps to reduce the risk of reaching a local minima.

A REAL TIME FACE CLASSIFICATION AND COUNTING SYSTEM

2008 ◽  
Vol 05 (01) ◽  
pp. 1-10 ◽  
Author(s):  
XINYU WU ◽  
YONGSHENG OU ◽  
HUIHUAN QIAN ◽  
YANGSHENG XU
Keyword(s):  
Real Time ◽  
Principal Component ◽  
Classification Problem ◽  
Component Analysis ◽  
Support Vector ◽  
Feature Generation ◽  
Training Process ◽  
Classification Rate ◽  
Counting System ◽  
Face Images

This paper introduces a face detection, classification and counting system that is robust and works in real-time. It tracks multiple people, which is useful for face counting. The classification problem is defined as differentiating and then classifying the front of a face into Asian or non-Asian categories. The first step, is principal component analysis (PCA) for feature generation and independent component analysis (ICA) for feature extraction. Then, we employ support vector machine (SVM) for the training process and combine different SVM classifiers to create new classifiers, which improves the classification rate. Based on this, we can count the number of Asians and non-Asians. Experiments show that our system achieves a classification rate of 82.5% based on a database containing 750 face images from FERET.

Machine learning for air quality prediction using meteorological and traffic related features

2020 ◽  
Vol 12 (5) ◽  
pp. 379-391
Author(s):  
Ihsane Gryech ◽  
Mounir Ghogho ◽  
Hajar Elhammouti ◽  
Nada Sbihi ◽  
Abdellatif Kobbane
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Support Vector ◽  
Air Quality Prediction ◽  
Monitoring Process ◽  
Non Linear ◽  
The Impact

The presence of pollutants in the air has a direct impact on our health and causes detrimental changes to our environment. Air quality monitoring is therefore of paramount importance. The high cost of the acquisition and maintenance of accurate air quality stations implies that only a small number of these stations can be deployed in a country. To improve the spatial resolution of the air monitoring process, an interesting idea is to develop data-driven models to predict air quality based on readily available data. In this paper, we investigate the correlations between air pollutants concentrations and meteorological and road traffic data. Using machine learning, regression models are developed to predict pollutants concentration. Both linear and non-linear models are investigated in this paper. It is shown that non-linear models, namely Random Forest (RF) and Support Vector Regression (SVR), better describe the impact of traffic flows and meteorology on the concentrations of pollutants in the atmosphere. It is also shown that more accurate prediction models can be obtained when including some pollutants’ concentration as predictors. This may be used to infer the concentrations of some pollutants using those of other pollutants, thereby reducing the number of air pollution sensors.

News Category Classification Using Distinctive Bag of Words and ANN Classifier

2018 ◽  
Vol 6 (6) ◽  
pp. 311 ◽  
Author(s):  
Amritpal Singh ◽  
Sunil Kumar Chhillar
Keyword(s):  
Text Classification ◽  
English Language ◽  
Standard Technique ◽  
Classification Problem ◽  
News Article ◽  
Support Vector ◽  
Bag Of Words ◽  
Classification Rate ◽  
Binary Classifiers ◽  
Ann Classifier

Category classification, for news, is a multi-label text classification problem. The goal is to assign one or more categories to a news article. A standard technique in multi-label text classification is to use a set of binary classifiers. For each category, a classifier is used to give a “yes” or “no” answer on if the category should be assigned to a text. Some of the standard algorithms for text classification that are used for binary classifiers include Naive Bayesian Classifiers, Support Vector Machines, artificial neural networks etc. In this distinctive bag of words have been used as feature set based on high frequency word tokens found in individual category of news. The algorithm presented in this work is based on a keyword extraction algorithm that is capable of dealing with English language in which different news categories i.e. Business, entertainment, politics, sports etc. has been considered. Intra-class news classification has been carried out in which Cricket and Football in sports category has been selected to verify the performance of the algorithm. Experimental results shows high classification rate in describing category of a news document.

scholarly journals Local Similarity-Based Fuzzy Multiple Kernel One-Class Support Vector Machine

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qiang He ◽  
Qingshuo Zhang ◽  
Hengyou Wang ◽  
Changlun Zhang
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Classification Problem ◽  
Support Vector ◽  
Local Similarity ◽  
Membership Degree ◽  
Multiple Kernel ◽  
Classification Boundary ◽  
One Class Classification ◽  
The Impact

One-class support vector machine (OCSVM) is one of the most popular algorithms in the one-class classification problem, but it has one obvious disadvantage: it is sensitive to noise. In order to solve this problem, the fuzzy membership degree is introduced into OCSVM, which makes the samples with different importance have different influences on the determination of classification hyperplane and enhances the robustness. In this paper, a new calculation method of membership degree is proposed and introduced into the fuzzy multiple kernel OCSVM (FMKOCSVM). The combined kernel is used to measure the local similarity between samples, and then, the importance of samples is determined based on the local similarity between training samples, so as to determine the membership degree and reduce the impact of noise. The proposed membership requires only positive data in the calculation process, which is consistent with the training set of OCSVM. In this method, the noise has a smaller membership value, which can reduce the negative impact of noise on the classification boundary. Simultaneously, this method of calculating membership has a higher efficiency. The experimental results show that FMKOCSVM based on proposed local similarity membership is efficient and more robust to outliers than the ordinary multiple kernel OCSVMs.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Epigenetic Studies in Psychotherapy: A Systematic Review

2020 ◽  
Vol 16 (2) ◽  
pp. 86-92
Author(s):  
Rafael Penadés ◽  
Bárbara Arias ◽  
Mar Fatjó-Vilas ◽  
Laura González-Vallespí ◽  
Clemente García-Rizo ◽  
...  
Keyword(s):  
Systematic Review ◽  
Dna Methylation ◽  
Mental Disorders ◽  
Epigenetic Modifications ◽  
Symptom Improvement ◽  
Epigenetic Changes ◽  
Main Hypothesis ◽  
Psychological Treatments ◽  
Time Period ◽  
The Impact

Background: Epigenetic modifications appear to be dynamic and they might be affected by environmental factors. The possibility of influencing these processes through psychotherapy has been suggested. Objective: To analyse the impact of psychotherapy on epigenetics when applied to mental disorders. The main hypothesis is that psychological treatments will produce epigenetic modifications related to the improvement of treated symptoms. Methods: A computerised and systematic search was completed throughout the time period from 1990 to 2019 on the PubMed, ScienceDirect and Scopus databases. Results: In total, 11 studies were selected. The studies were evaluated for the theoretical framework, genes involved, type of psychotherapy and clinical challenges and perspectives. All studies showed detectable changes at the epigenetic level, like DNA methylation changes, associated with symptom improvement after psychotherapy. Conclusion: Methylation profiles could be moderating treatment effects of psychotherapy. Beyond the detected epigenetic changes after psychotherapy, the epigenetic status before the implementation could act as an effective predictor of response.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Circulating tumour DNA methylation in hepatocellular carcinoma diagnosis using digital droplet PCR

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199296
Author(s):  
Juan Wang ◽  
Liu Yang ◽  
Yanjun Diao ◽  
Jiayun Liu ◽  
Jinjie Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Likelihood Ratio ◽  
Predictive Value ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Control Group ◽  
Control Subjects ◽  
Droplet Pcr ◽  
Healthy Control

Objective To evaluate the performance of a DNA methylation-based digital droplet polymerase chain reaction (ddPCR) assay to detect aberrant DNA methylation in cell-free DNA (cfDNA) and to determine its application in the detection of hepatocellular carcinoma (HCC). Methods The present study recruited patients with liver-related diseases and healthy control subjects. Blood samples were used for the extraction of cfDNA, which was then bisulfite converted and the extent of DNA methylation quantified using a ddPCR platform. Results A total of 97 patients with HCC, 80 healthy control subjects and 46 patients with chronic hepatitis B/C virus infection were enrolled in the study. The level of cfDNA in the HCC group was significantly higher than that in the healthy control group. For the detection of HCC, based on a cut-off value of 15.7% for the cfDNA methylation ratio, the sensitivity and specificity were 78.57% and 89.38%, respectively. The diagnostic accuracy was 85.27%, the positive predictive value was 81.91% and the negative predictive value was 87.20%. The positive likelihood ratio of 15.7% in HCC diagnosis was 7.40, while the negative likelihood ratio was 0.24. Conclusions A sensitive methylation-based assay might serve as a liquid biopsy test for diagnosing HCC.

scholarly journals Distinct epigenetic signatures between adult-onset and late-onset depression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hirotaka Yamagata ◽  
Hiroyuki Ogihara ◽  
Koji Matsuo ◽  
Shusaku Uchida ◽  
Ayumi Kobayashi ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Clinical Symptoms ◽  
Late Onset ◽  
Adult Onset ◽  
Blood Biomarkers ◽  
Major Depressive ◽  
Genome Wide ◽  
Healthy Control ◽  
Epigenetic Signatures

AbstractThe heterogeneity of major depressive disorder (MDD) is attributed to the fact that diagnostic criteria (e.g., DSM-5) are only based on clinical symptoms. The discovery of blood biomarkers has the potential to change the diagnosis of MDD. The purpose of this study was to identify blood biomarkers of DNA methylation by strategically subtyping patients with MDD by onset age. We analyzed genome-wide DNA methylation of patients with adult-onset depression (AOD; age ≥ 50 years, age at depression onset < 50 years; N = 10) and late-onset depression (LOD; age ≥ 50 years, age at depression onset ≥ 50 years; N = 25) in comparison to that of 30 healthy subjects. The methylation profile of the AOD group was not only different from that of the LOD group but also more homogenous. Six identified methylation CpG sites were validated by pyrosequencing and amplicon bisulfite sequencing as potential markers for AOD in a second set of independent patients with AOD and healthy control subjects (N = 11). The combination of three specific methylation markers achieved the highest accuracy (sensitivity, 64%; specificity, 91%; accuracy, 77%). Taken together, our findings suggest that DNA methylation markers are more suitable for AOD than for LOD patients.

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