Predictor performance with stratified data and imbalanced classes

Stone, Eric A.

doi:10.1038/nmeth.3045

Cited by 8 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We then conducted a receiver operating characteristics (ROC) curve analysis for % dfi and %ASA to elucidate their ability to distinguish between disease and neutral phenotypes of nsSNVs. A randomly generated test set consisting of 10% of the entire data set (which only includes nsSNVs at interfaces) was used and the remaining 90% was used for training . The area under the curve (AUC) for dfi is 0.71 and 0.56 for ASA [Fig.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association

et al. 2015

View full text Add to dashboard Cite

Recent studies have shown that the protein interface sites between individual monomeric units in biological assemblies are enriched in disease-associated non-synonymous single nucleotide variants (nsSNVs). In order to elucidate the mechanistic underpinning of this observation, we investigated the conformational dynamic properties of protein interface sites through a site-specific structural dynamic flexibility metric (dfi) for 333 multimeric protein assemblies. dfi measures the dynamic resilience of a single residue to perturbations that occurred in the rest of the protein structure and identifies sites contributing the most to functionally critical dynamics. Analysis of dfi profiles of over a thousand positions harboring variation revealed that amino acid residues at interfaces have lower average dfi (31%) than those present at non-interfaces (50%), which means that protein interfaces have less dynamic flexibility. Interestingly, interface sites with disease-associated nsSNVs have significantly lower average dfi (23%) as compared to those of neutral nsSNVs (42%), which directly relates structural dynamics to functional importance. We found that less conserved interface positions show much lower dfi for disease nsSNVs as compared to neutral nsSNVs. In this case, dfi is better as compared to the accessible surface area metric, which is based on the static protein structure. Overall, our proteome-wide conformational dynamics analysis indicates that certain interface sites play a critical role in functionally related dynamics (i.e., those with low dfi values), therefore mutations at those sites are more likely to be associated with disease.

show abstract

Section: Resultsmentioning

confidence: 99%

“…A randomly generated test set consisting of 10% of the entire data set (which only includes nsSNVs at interfaces) was used and the remaining 90% was used for training. 4,49 The area under the curve (AUC) for dfi is 0.71 and 0.56 for ASA [ Fig. 5(B)].…”

Section: Figurementioning

confidence: 99%

Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The train-test split is obtained by randomly shuffling the data and stratifying the two classes in both training and test portions. Given the significantly lower number of accident events relative to non-accident events, stratification by class percentage is used to maintain a similar class ratio in both training and test portions while shuffling the dataset during split (Sechidis et al , 2011; Stone, 2014). In doing so, the same sequence of random numbers is used to ensure similar splitting of the dataset in every analysis run.…”

Section: Data Processing and Preparationmentioning

confidence: 99%

A feedforward neural network for drone accident prediction from physiological signals

Sakib

Chaspari

Behzadan

2021

SASBE

View full text Add to dashboard Cite

PurposeAs drones are rapidly transforming tasks such as mapping and surveying, safety inspection and progress monitoring, human operators continue to play a critical role in ensuring safe drone missions in compliance with safety regulations and standard operating procedures. Research shows that operator's stress and fatigue are leading causes of drone accidents. Building upon the authors’ past work, this study presents a systematic approach to predicting impending drone accidents using data that capture the drone operator's physiological state preceding the accident.Design/methodology/approachThe authors collect physiological data from 25 participants in real-world and virtual reality flight experiments to design a feedforward neural network (FNN) with back propagation. Four time series signals, namely electrodermal activity (EDA), skin temperature (ST), electrocardiogram (ECG) and heart rate (HR), are selected, filtered for noise and used to extract 92 time- and frequency-domain features. The FNN is trained with data from a window of length t = 3…8 s to predict accidents in the next p = 3…8 s.FindingsAnalysis of model performance in all 36 combinations of analysis window (t) and prediction horizon (p) combinations reveals that the FNN trained with 8 s of physiological signal (i.e. t = 8) to predict drone accidents in the next 6 s (i.e. p = 6) achieved the highest F1-score of 0.81 and AP of 0.71 after feature selection and data balancing.Originality/valueThe safety and integrity of collaborative human–machine systems (e.g. remotely operated drones) rely on not only the attributes of the human operator or the machinery but also how one perceives the other and adopts to the evolving nature of the operational environment. This study is a first systematic attempt at objective prediction of potential drone accident events from operator's physiological data in (near-) real time. Findings will lay the foundation for creating automated intervention systems for drone operations, ultimately leading to safer jobsites.

show abstract

Adaptive Multi-objective Swarm Crossover Optimization for Imbalanced Data Classification

Fong

Meng

et al. 2016

Advanced Data Mining and Applications

View full text Add to dashboard Cite

Predictor performance with stratified data and imbalanced classes

Cited by 8 publications

References 4 publications

Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association

Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association

A feedforward neural network for drone accident prediction from physiological signals

Adaptive Multi-objective Swarm Crossover Optimization for Imbalanced Data Classification

Contact Info

Product

Resources

About