scholarly journals Machine Learning Models Predicting Daily Affective Dynamics Via Personality and Psychopathology Traits

2020 ◽  
Author(s):  
Nicholas C. Jacobson ◽  
George Price ◽  
Minkeun Song ◽  
Zoe Wortzman ◽  
Nhi D. Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Personality Traits ◽  
Control Beliefs ◽  
Interpersonal Circumplex ◽  
Big Five Personality ◽  
Learning Models ◽  
Emotional Instability ◽  
Daily Diary Study ◽  
Affective Dynamics ◽  
Machine Learning Models

To date, numerous studies have examined personality and psychopathology indexes as predictors of affective dynamics, i.e. measures of how emotions change across time. Yet, little research has examined individual differences in personality, pathology, and affective dynamics constructs comprehensively, accounted for non-linear relationships, or examined the out-of-sample generalizations of the predictions. To address these gaps, the current research utilized machine learning models to predict affective dynamics. A large variety of baseline personality and psychopathology traits (pathological personality measures, clinical anxiety, depression, anger, sleep, affective instability scales, the big five personality traits, interpersonal circumplex measures, and control beliefs) were used to predict the affective dynamics derived from person-specific modeling of affect across a 50-day daily diary study. The results showed that baseline personality traits significantly predicted the strength of day-to-day affective dynamics for emotional variability, relative emotional variability, emotional instability, emotional inertia, and emotional cyclicality for both positive and negative affect (rs 0.152-0.444). Although broadly neglected in prior research, the results suggested that interpersonal circumplex measures most strongly predicted a number of affective dynamics.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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