scholarly journals Estimating the heritability of psychological measures in the Human Connectome Project dataset

2019 ◽  
Author(s):  
Yanting Han ◽  
Ralph Adolphs
Keyword(s):  
Machine Learning ◽  
General Intelligence ◽  
Behavioral Measures ◽  
Latent Factors ◽  
Psychological Measures ◽  
Human Connectome Project ◽  
Heritability Estimation ◽  
Extensive Family ◽  
Machine Learning Models ◽  
And Task

AbstractThe Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral measures and extensive family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times, and identify identical twins. Using machine-learning (univariate linear model, Ridge classifier and Random Forest model) we estimated the heritability of 37 behavioral measures and compared the results to those derived from twin correlations. Correlations between the standard heritability metric and each set of model weights ranged from 0.42 to 0.67, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further derived nine latent factors from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.15 to 0.38. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present an alternative method for qualitatively estimating the heritability of the behavioral measures in the HCP as a resource for other investigators, and recommend the use of machine-learning models for estimating heritability.

scholarly journals Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

2021 ◽  
Author(s):  
Gabriele Lohmann ◽  
Eric Lacosse ◽  
Thomas Ethofer ◽  
Vinod J. Kumar ◽  
Klaus Scheffler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Machine Learning Techniques ◽  
Edge Weight ◽  
Limiting Factor ◽  
General Intelligence ◽  
Individual Behaviour ◽  
Scan Time ◽  
Learning Techniques ◽  
Human Connectome Project

A number of recent studies have investigated machine learning techniques for predicting individual behaviour from fMRI. Even though encouraging results have been obtained, excessive scan times -- especially in resting state fMRI -- are a limiting factor. Here we present a new machine learning algorithm for predicting individual behaviour of healthy human subjects using both resting state (rsfMRI) as well as task-based fMRI (tfMRI). It achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight. As a proof of concept, we focus on predicting fluid, crystallized and general intelligence scores. In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50~percent in tfMRI, and of around 59~percent (R2~0.29) when results from two tasks are combined. We compare these results against a benchmark of existing methods that produced correlations below 50~percent in both rsfMRI and tfMRI. We conclude that with novel machine learning techniques applied to tfMRI it is possible to obtain significantly better prediction accuracies at a fraction of the scan time.

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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