Forecasting student outcomes at university-wide scale using machine learning

2017 ◽  
Author(s):  
Drew Wham
Keyword(s):  
Machine Learning ◽  
Student Outcomes ◽  
Wide Scale

scholarly journals Machine-learning annotation of human splicing branchpoints

2016 ◽  
Author(s):  
Bethany Signal ◽  
Brian S Gloss ◽  
Marcel E Dinger ◽  
Timothy R Mercer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Gene Splicing ◽  
Genetic Encoding ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
A Genome ◽  
Wide Scale ◽  
The Impact ◽  
Splicing Patterns

ABSTRACTBackgroundThe branchpoint element is required for the first lariat-forming reaction in splicing. However due to difficulty in experimentally mapping at a genome-wide scale, current catalogues are incomplete.ResultsWe have developed a machine-learning algorithm trained with empirical human branchpoint annotations to identify branchpoint elements from primary genome sequence alone. Using this approach, we can accurately locate branchpoints elements in 85% of introns in current gene annotations. Consistent with branchpoints as basal genetic elements, we find our annotation is unbiased towards gene type and expression levels. A major fraction of introns was found to encode multiple branchpoints raising the prospect that mutational redundancy is encoded in key genes. We also confirmed all deleterious branchpoint mutations annotated in clinical variant databases, and further identified thousands of clinical and common genetic variants with similar predicted effects.ConclusionsWe propose the broad annotation of branchpoints constitutes a valuable resource for further investigations into the genetic encoding of splicing patterns, and interpreting the impact of common- and disease-causing human genetic variation on gene splicing.

scholarly journals Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Masoud Arabfard ◽  
Mina Ohadi ◽  
Vahid Rezaei Tabar ◽  
Ahmad Delbari ◽  
Kaveh Kavousi
Keyword(s):  
Machine Learning ◽  
Process Comparison ◽  
Age Related ◽  
Genome Wide ◽  
Human Genes ◽  
Disease Candidate Gene ◽  
A Genome ◽  
Machine Learning Approach ◽  
Wide Scale ◽  
Biological Aspects

Abstract Background Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE). Results We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage). Conclusion We predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.

scholarly journals An Enhanced Sybil Guard to Detect Bots in Online Social Networks

2021 ◽  
Author(s):  
Nisha P. Shetty ◽  
Balachandra Muniyal ◽  
Arshia Anand ◽  
Sushant Kumar
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Networking ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Easy Access ◽  
Classifier Combination ◽  
Fake News ◽  
Wide Scale ◽  
Set Up

Sybil accounts are swelling in popular social networking sites such as Twitter, Facebook etc. owing to cheap subscription and easy access to large masses. A malicious person creates multiple fake identities to outreach and outgrow his network. People blindly trust their online connections and fall into trap set up by these fake perpetrators. Sybil nodes exploit OSN’s ready-made connectivity to spread fake news, spamming, influencing polls, recommendations and advertisements, masquerading to get critical information, launching phishing attacks etc. Such accounts are surging in wide scale and so it has become very vital to effectively detect such nodes. In this research a new classifier (combination of Sybil Guard, Twitter engagement rate and Profile statistics analyser) is developed to combat such Sybil nodes. The proposed classifier overcomes the limitations of structure based, machine learning based and behaviour-based classifiers and is proven to be more accurate and robust than the base Sybil guard algorithm.

scholarly journals A Cocurricular Program That Encourages Specific Study Skills and Habits Improves Academic Performance and Retention of First-Year Undergraduates in Introductory Biology

2021 ◽  
Vol 20 (1) ◽  
pp. ar4
Author(s):  
Whitney Hawkins ◽  
Kate Goddard ◽  
Carlita Favero
Keyword(s):  
Academic Performance ◽  
Study Skills ◽  
Student Outcomes ◽  
Low Cost ◽  
First Year ◽  
Introductory Biology ◽  
Specific Study ◽  
Gateway Courses ◽  
Wide Scale ◽  
Improved Performance

A short (8-week), low-cost intervention focused on study skills improved performance and persistence of first-year undergraduates in introductory biology. Wide-scale adoption of teaching study skills in STEM gateway courses is suggested to improve student outcomes, particularly for students who are commonly underrepresented.

scholarly journals Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5363 ◽  
Author(s):  
Rana Zia Ur Rehman ◽  
Silvia Del Din ◽  
Jian Qing Shi ◽  
Brook Galna ◽  
Sue Lord ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Support Vector ◽  
Gait Assessment ◽  
Gait Characteristics ◽  
Parkinson’S Diseases ◽  
Assessment Systems ◽  
Wide Scale ◽  
The Impact

Early diagnosis of Parkinson’s diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway—GAITRite; and an accelerometer attached at the lower back—Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 ± 7.81% vs. 80.49 ± 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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