scholarly journals WITMSG: Large-scale Prediction of Human Intronic m6A RNA Methylation Sites from Sequence and Genomic Features

2020 ◽  
Vol 21 (1) ◽  
pp. 67-76 ◽  
Author(s):  
Lian Liu ◽  
Xiujuan Lei ◽  
Jia Meng ◽  
Zhen Wei
Keyword(s):  
Large Scale ◽  
Cross Validation ◽  
Rna Localization ◽  
Training Data ◽  
Biological Processes ◽  
Computational Framework ◽  
Rna Methylation ◽  
M6a Rna Methylation ◽  
First Time ◽  
Fold Cross Validation

Introduction: N6-methyladenosine (m6A) is one of the most widely studied epigenetic modifications. It plays important roles in various biological processes, such as splicing, RNA localization and degradation, many of which are related to the functions of introns. Although a number of computational approaches have been proposed to predict the m6A sites in different species, none of them were optimized for intronic m6A sites. As existing experimental data overwhelmingly relied on polyA selection in sample preparation and the intronic RNAs are usually underrepresented in the captured RNA library, the accuracy of general m6A sites prediction approaches is limited for intronic m6A sites prediction task. Methodology: A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m6A RNA methylation sites in humans has been proposed here for the first time. Based on the random forest algorithm and using only known intronic m6A sites as the training data, WITMSG takes advantage of both conventional sequence features and a variety of genomic characteristics for improved prediction performance of intron-specific m6A sites. Results and Conclusion: It has been observed that WITMSG outperformed competing approaches (trained with all the m6A sites or intronic m6A sites only) in 10-fold cross-validation (AUC: 0.940) and when tested on independent datasets (AUC: 0.946). WITMSG was also applied intronome-wide in humans to predict all possible intronic m6A sites, and the prediction results are freely accessible at http://rnamd.com/intron/.

Identification of 2’-O-methylation Site by Investigating Multi-feature Extracting Techniques

2020 ◽  
Vol 23 (6) ◽  
pp. 527-535
Author(s):  
Qin-Lai Huang ◽  
Lida Wang ◽  
Shu-Guang Han ◽  
Hua Tang
Keyword(s):  
Experimental Method ◽  
Cross Validation ◽  
Computational Method ◽  
Immune Recognition ◽  
Biological Processes ◽  
Methylation Site ◽  
Rna Methylation ◽  
Feature Extracting ◽  
Computational Workflow ◽  
Fold Cross Validation

Background: RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part of RNA methylation. It has shown that ribose 2'-O-methylation plays an important role in immune recognition and other pathogenesis. Objective: We aim to design a computational method to identify 2'-O-methylation. Methods: Different from the experimental method, we propose a computational workflow to identify the methylation site based on the multi-feature extracting algorithm. Results: With a voting procedure based on 7 best feature-classifier combinations, we achieved Accuracy of 76.5% in 10-fold cross-validation. Furthermore, we optimized features and input the optimized features into SVM. As a result, the AUC reached to 0.813. Conclusion: The RNA sample, especially the negative samples, used in this study are more objective and strict, so we obtained more representative results than state-of-arts studies.

scholarly journals Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species

2021 ◽  
Vol 17 (2) ◽  
pp. e1008767
Author(s):  
Zutan Li ◽  
Hangjin Jiang ◽  
Lingpeng Kong ◽  
Yuanyuan Chen ◽  
Kun Lang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Biological Processes ◽  
Biological Functions ◽  
Learning Framework ◽  
Wide Range ◽  
Genomic Scale ◽  
Downstream Gene Expression ◽  
Fold Cross Validation

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

scholarly journals Implementasi Algoritma C5.0 Untuk Menganalisa Gejala Prioritas Pada Anak Yang Mengalami Bullying

Repositor ◽  
2020 ◽  
Vol 2 (8) ◽  
Author(s):  
Nabillah Annisa Rahmayanti ◽  
Yufis Azhar ◽  
Gita Indah Marthasari
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Evaluation Method ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Training Data ◽  
Victims Of Bullying ◽  
Fold Cross Validation ◽  
Selection Of

AbstrakBullying sering terjadi pada anak-anak khususnya remaja dan meresahkan para orang tua. Maraknya kasus bullying di negeri ini bahkan sampai menyebabkan korban jiwa. Hal ini dapat dicegah dengan cara mengetahui gejala-gejala seorang anak yang mengalami bullying. Kondisi seorang anak yang tidak dapat mengungkapkan keluh kesahnya, tentu membuat orang tua dan juga guru di sekolah sukar dalam mengerti apa yang sedang menimpanya. Hal tersebut bisa saja dikarenakan anak sedang mengalami tindakan bullying oleh teman-temannya. Oleh karena itu peneliti memiliki tujuan untuk menghasilkan fitur yang telah terseleksi dengan menggunakan algoritma C5.0. Sehingga dengan menggunakan fitur yang telah terseleksi dapat meringankan pekerjaan dalam mengisi kuisioner dan juga mempersingkat waktu dalam menentukan seorang anak apakah terkena bullying atau tidak berdasarkan gejala yang ada di setiap pertanyaan pada kuisioner. Untuk menunjang data dalam penelitian ini, peneliti menggunakan kuisioner untuk mendapatkan jawaban dari pertanyaan yang berisi tentang gejala anak yang menjadi korban bullying. Jawaban dari responden akan diolah menjadi kumpulan data yang nantinya akan dibagi menjadi data latih dan data uji untuk selanjutnya diteliti dengan menggunakan Algoritma C5.0. Metode evaluasi yang digunakan pada penelitian ini yaitu 10 fold cross validation dan untuk menilai akurasi menggunakan confusion matrix. Penelitian ini juga melaukan perbandingan dengan beberapa algoritma klasifikasi lainnya yaitu Naive Bayes dan KNN yang bertujuan untuk melhat seberapa akurat algoritma C5.0 dalam melakukan seleksi fitur. Hasil pengujian menunjukkan bahwa algoritma C5.0 mampu melakukan seleksi fitur dan juga memiliki tingkat akurasi yang lebih baik jika dibandingkan dengan algoritma Naive Bayes dan KNN dengan hasil akurasi sebelum menggunakan seleksi fitur sebesar 92,77% dan setelah menggunakan seleksi fitur sebesar 93,33%. Abstract Bullying often occurs in children, especially teenagers and unsettles parents. The rise of cases of bullying in this country even caused casualties. This can be prevented by knowing the symptoms of a child who has bullying. The condition of a child who cannot express his complaints, certainly makes parents and teachers at school difficult to understand what is happening to them. This could be because the child is experiencing bullying by his friends. Therefore, researchers have a goal to produce selected features using the C5.0 algorithm. So using the selected features can ease the work in filling out questionnaires and also shorten the time in determining whether a child is exposed to bullying or not based on the symptoms in each question in the questionnaire. To support the data in this study, the researcher used a questionnaire to get answers to questions that contained the symptoms of children who were victims of bullying. The answer from the respondent will be processed into a data collection which will later be divided into training data and test data for further research using the C5.0 Algorithm. The evaluation method used in this study is 10 fold cross validation and to assess accuracy using confusion matrix. This study also carried out a comparison with several other classification algorithms, namely Naive Bayes and KNN which aimed to see how accurate the C5.0 algorithm was in feature selection. The test results show that the C5.0 algorithm is capable of feature selection and also has a better accuracy compared to the Naive Bayes and KNN algorithms with accuracy results before using feature selection of 92.77% and after using feature selection of 93.33%

scholarly journals CASE BASE REASONING UNTUK MENENTUKAN KEBUTUHAN BAHAN BANGUNAN RUMAH

2018 ◽  
Vol 1 (2) ◽  
pp. 70-75
Author(s):  
Abdul Rozaq
Keyword(s):  
Test Data ◽  
Building Materials ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Training Data ◽  
Consultation Process ◽  
Case Base ◽  
Case Base Reasoning ◽  
House Building ◽  
Fold Cross Validation

Building materials is an important factor to built a house, to estimate funds the needs of build a house, consumers or developers can estimate the funds needed to build a house. To solve these problems use case base reasoning (CBR) approach, which method is capable of reasoning or solving the problem based on the cases that have been there as a solution to new problems. The system built in this study is a CBR system for determine the needs of house building materials. The consultation process is done by inserting new cases compared to the old case similarity value is then calculated using the nearest neighbor. The first test by inserting test data then compared with each type of home then obtained an accuracy of 83.6%. The second test is done by K-fold Cross Validation with K = 25 with the number of data 200, the data will be divided into two parts, namely the training data and test data, training data as many as 192 data and test data as many as 8 data. K-Fold Cross Validation method. This CBR system can produce an accuracy of 85.71%

scholarly journals Predictive modeling of religiosity, prosociality, and moralizing in 295,000 individuals from European and non-European populations

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pierre O. Jacquet ◽  
Farid Pazhoohi ◽  
Charles Findling ◽  
Hugo Mell ◽  
Coralie Chevallier ◽  
...  
Keyword(s):  
Sexual Behaviors ◽  
Large Scale ◽  
Cross Validation ◽  
Psychological Explanation ◽  
Collective Actions ◽  
Sample Data ◽  
Out Of Sample ◽  
Prosocial Attitudes ◽  
High Level ◽  
Fold Cross Validation

AbstractWhy do moral religions exist? An influential psychological explanation is that religious beliefs in supernatural punishment is cultural group adaptation enhancing prosocial attitudes and thereby large-scale cooperation. An alternative explanation is that religiosity is an individual strategy that results from high level of mistrust and the need for individuals to control others’ behaviors through moralizing. Existing evidence is mixed but most works are limited by sample size and generalizability issues. The present study overcomes these limitations by applying k-fold cross-validation on multivariate modeling of data from >295,000 individuals in 108 countries of the World Values Surveys and the European Value Study. First, this methodology reveals no evidence that European and non-European religious people invest more in collective actions and are more trustful of unrelated conspecifics. Instead, the individuals’ level of religiosity is found to be weakly but positively associated with social mistrust and negatively associated with the production of behaviors, which benefit unrelated members of the large-scale community. Second, our models show that individual variation in religiosity is well explained by the interaction of increased levels of social mistrust and increased needs to moralize other people’s sexual behaviors. Finally, stratified k-fold cross-validation demonstrates that the structures of these association patterns are robust to sampling variability and reliable enough to generalize to out-of-sample data.

scholarly journals Convolutional Neural Networks for automatic image quality control and EARL compliance of PET images

2021 ◽  
Author(s):  
Elisabeth Pfaehler ◽  
Daniela Euba ◽  
Andreas Rinscheid ◽  
Otto S. Hoekstra ◽  
Josee Zijlstra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Training Data ◽  
Independent Dataset ◽  
Pet Ct ◽  
Image Quality Control ◽  
The Cross ◽  
The Impact ◽  
Pet Scanners ◽  
Fold Cross Validation

Abstract Background Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. Materials and Methods 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using 5-fold cross validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations. Results In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. Conclusion The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by e.g. adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential.

scholarly journals Traditional Chinese Medicine (TCM) Diagnosis Model Building Based on Multi-label Classification

2018 ◽  
Vol 232 ◽  
pp. 02026
Author(s):  
Lu Zhou ◽  
Guang-geng Li ◽  
Yu-mei Zhou ◽  
Dan Yin ◽  
Yan Sun ◽  
...  
Keyword(s):  
Cross Validation ◽  
Model Building ◽  
Learning Algorithm ◽  
Training Data ◽  
Average Precision ◽  
Test Dataset ◽  
Average Accuracy ◽  
Good Potential ◽  
Diagnosis Model ◽  
Fold Cross Validation

In the study, we propose a TCM diagnosis model that can be used for multi-label classification and give clear diagnosis, as well as the basis for diagnosis and differentiation when the symptoms correspond to multiple diseases or syndromes. The implementation of the model is divided into three steps. Firstly, choose the machine learning algorithm to train the TCM diagnosis model. The features of the training data are symptoms and the labels are diseases or syndromes. Secondly, give the number α (α>1, α∈Z+), the model will output the diagnoses with the top α highest probability according to the input symptoms as candidate diagnoses. Finally, the rules of differential diagnosis are designed to determine which candidate diagnoses should be reserved, thereby complete the multi-label classification. In our test dataset, by 10-fold cross-validation, the average accuracy of the single label classification was 0.882; the average precision was 0.974; the average recall was 1.000; the average f1 score was 0.967; the average accuracy of the multi-label classification was 0.706; the average micro precision was 0.934; the average micro recall was 0.941 and the average hamming loss was 0.060. Through the test we can know that this model had a good potential for auxiliary decision making in clinical diagnosis and treatment.

scholarly journals Immunosuppression, rather than inflammation, is a salient feature of sepsis in an Indian cohort

2019 ◽  
Author(s):  
Samanwoy Mukhopadhyay ◽  
Pravat K Thatoi ◽  
Bidyut K Das ◽  
Saroj K Mohapatra
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Severe Sepsis ◽  
Large Scale ◽  
Salient Feature ◽  
Biological Processes ◽  
Blood Gene Expression ◽  
Large Scale Change ◽  
Sepsis And Septic Shock ◽  
First Time

1AbstractSepsis remains a lethal ailment with imprecise treatment and ill-understood biology. A clinical transcriptomic analysis of sepsis patients was performed for the first time in India and revealed large-scale change in blood gene expression in patients of severe sepsis and septic shock admitted to ICU. Three biological processes were quantified using scores derived from the corresponding transcriptional modules. Comparison of the module scores revealed that genes associated with immune response were more suppressed compared to the inflammation-associated genes. These findings will have great implication in the treatment and prognosis of severe sepsis/septic shock if it can be translated into a bedside tool.

scholarly journals Deep Learning Based Pectoral Muscle Segmentation on MIAS Mammograms

2020 ◽  
Author(s):  
Young Jae Kim ◽  
Eun Young Yoo ◽  
Kwang Gi Kim
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Deep Learning ◽  
Cross Validation ◽  
Pectoral Muscle ◽  
Training Data ◽  
Dice Similarity Coefficient ◽  
Detection Accuracy ◽  
Pectoral Muscle Detection ◽  
Fold Cross Validation

Abstract Background: The purpose of this study was to propose a deep learning-based method for automated detection of the pectoral muscle, in order to reduce misdetection in a computer-aided diagnosis (CAD) system for diagnosing breast cancer in mammography. This study also aimed to assess the performance of the deep learning method for pectoral muscle detection by comparing it to an image processing-based method using the random sample consensus (RANSAC) algorithm. Methods: Using the 322 images in the Mammographic Image Analysis Society (MIAS) database, the pectoral muscle detection model was trained with the U-Net architecture. Of the total data, 80% was allocated as training data and 20% was allocated as test data, and the performance of the deep learning model was tested by 5-fold cross validation. Results: The image processing-based method for pectoral muscle detection using RANSAC showed 92% detection accuracy. Using the 5-fold cross validation, the deep learning-based method showed a mean sensitivity of 95.55%, mean specificity of 99.88%, mean accuracy of 99.67%, and mean Dice similarity coefficient (DSC) of 95.88%. Conclusions: The proposed deep learning-based method of pectoral muscle detection performed better than an existing image processing-based method. In the future, by collecting data from various medical institutions and devices to further train the model and improve its reliability, we expect that this model could greatly reduce misdetection rates by CAD systems for breast cancer diagnosis.

scholarly journals Predictive multivariate modelling of religiosity, prosociality and moralizing in 295,000 individuals from European and non-European populations

2020 ◽  
Author(s):  
Pierre O. Jacquet ◽  
Farid Pazhoohi ◽  
Charles Findling ◽  
Hugo Mell ◽  
Coralie Chevallier ◽  
...  
Keyword(s):  
Large Scale ◽  
Alternative Explanation ◽  
Cross Validation ◽  
Positive Association ◽  
Collective Actions ◽  
Individual Strategy ◽  
Multivariate Modelling ◽  
Prosocial Attitudes ◽  
High Level ◽  
Fold Cross Validation

Why do moral religions exist? An influential explanation is that religious beliefs in supernatural punishment is cultural group adaptation enhancing prosocial attitudes and thereby large-scale cooperation. An alternative explanation is that religiosity is an individual strategy that results from high level of mistrust and the need for individuals to control others’ behaviours through moralizing. Existing evidence is mixed but most works are limited by sample size and generalizability issues. The present study overcomes these limitations by applying k-fold cross-validation on multivariate modelling of data from more than 295,000 individuals in 108 countries of the World Values Surveys and the European Value Study. This methodology demonstrates that in European as well as in non-European samples, religious people invest less in collective actions and are more mistrustful of others. By contrast, we find a strong and positive association between higher level of mistrust, higher level of moralizing and higher level of religiosity.

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