Modeless Japanese Input Method Using Multiple Character Sequence Features

2012 ◽  
Author(s):  
Y. Ikegami ◽  
Y. Sakurai ◽  
S. Tsuruta
Keyword(s):  
Sequence Features ◽  
Input Method ◽  
Character Sequence ◽  
Multiple Character

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

2020 ◽  
Vol 15 ◽  
Author(s):  
Dicle Yalcin ◽  
Hasan H. Otu
Keyword(s):  
Model Building ◽  
De Novo ◽  
Cpg Islands ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Global Methylation ◽  
Sequence Features ◽  
A Genome ◽  
Combined Features ◽  
Epigenetic Repression

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation are shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

scholarly journals Factors That Cause Concerns after Cardioverter Defibrillator Implantation

2021 ◽  
Vol 18 (11) ◽  
pp. 6095
Author(s):  
Olimpia Karczewska ◽  
Agnieszka Młynarska
Keyword(s):  
Observational Study ◽  
Secondary Prevention ◽  
Negative Emotions ◽  
Female Gender ◽  
Multiple Regression Model ◽  
Icd Implantation ◽  
Cardioverter Defibrillator ◽  
Input Method ◽  
Two Stages

Background and Objectives: The aim of the study was to assess the factors that influence the occurrence of concerns and their intensification after the implantation of a cardioverter defibrillator. Materials and Methods: This was a prospective and observational study including 158 patients. The study was conducted in two stages: stage I before implantable cardioverter defibrillator (ICD) implantation and stage II follow-up visit six months after ICD implantation. Standardized questionnaires were used in both stages. Results: Age and female gender were significantly correlated with the occurrence and intensity of concerns. Patients who had a device implanted for secondary prevention also experienced higher levels of concern. Additionally, a multiple regression model using the stepwise input method was performed. The model was statistically significant and explained 42% of the observed variance in the dependent variable (p = 0.0001, R2 = 0.4215). The analysis showed that age (p = 0.0036), insomnia (p = 0.0276), anxiety (p = 0.0000) and negative emotions (p = 0.0374) were important predictors of the dependent variable and enabled higher levels of the number of concerns to be predicted. Conclusions: There is a relationship between the severity of the concerns related to an implanted ICD and age, gender, anxiety, negative emotions and insomnia. Indications for ICD implantation may be associated with increased concerns about ICD.

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

Gene pyramiding and multiple character breeding

2020 ◽  
pp. 131-165 ◽  
Author(s):  
Ashutosh Kushwah ◽  
Soma Gupta ◽  
Shayla Bindra ◽  
Norah Johal ◽  
Inderjit Singh ◽  
...  
Keyword(s):  
Gene Pyramiding ◽  
Multiple Character
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