scholarly journals Application of Machine Learning Classifier to Candida auris Drug Resistance Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Dingchen Li ◽  
Yaru Wang ◽  
Wenjuan Hu ◽  
Fangyan Chen ◽  
Jingya Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Phylogenetic Tree ◽  
Resistance Mutations ◽  
Candida Auris ◽  
Test Set ◽  
Machine Learning Classifiers ◽  
Drug Resistance Mutations ◽  
Learning Classifier ◽  
Learning Classifiers

Candida auris (C. auris) is an emerging fungus associated with high morbidity. It has a unique transmission ability and is often resistant to multiple drugs. In this study, we evaluated the ability of different machine learning models to classify the drug resistance and predicted and ranked the drug resistance mutations of C. auris. Two C. auris strains were obtained. Combined with other 356 strains collected from the European Bioinformatics Institute (EBI) databases, the whole genome sequencing (WGS) data were analyzed by bioinformatics. Machine learning classifiers were used to build drug resistance models, which were evaluated and compared by various evaluation methods based on AUC value. Briefly, two strains were assigned to Clade III in the phylogenetic tree, which was consistent with previous studies; nevertheless, the phylogenetic tree was not completely consistent with the conclusion of clustering according to the geographical location discovered earlier. The clustering results of C. auris were related to its drug resistance. The resistance genes of C. auris were not under additional strong selection pressure, and the performance of different models varied greatly for different drugs. For drugs such as azoles and echinocandins, the models performed relatively well. In addition, two machine learning algorithms, based on the balanced test and imbalanced test, were designed and evaluated; for most drugs, the evaluation results on the balanced test set were better than on the imbalanced test set. The mutations strongly be associated with drug resistance of C. auris were predicted and ranked by Recursive Feature Elimination with Cross-Validation (RFECV) combined with a machine learning classifier. In addition to known drug resistance mutations, some new resistance mutations were predicted, such as Y501H and I466M mutation in the ERG11 gene and R278H mutation in the ERG10 gene, which may be associated with fluconazole (FCZ), micafungin (MCF), and amphotericin B (AmB) resistance, respectively; these mutations were in the “hot spot” regions of the ergosterol pathway. To sum up, this study suggested that machine learning classifiers are a useful and cost-effective method to identify fungal drug resistance-related mutations, which is of great significance for the research on the resistance mechanism of C. auris.

Automating the Clinical Assessment of Independent Wheelchair Sitting Pivot Transfer Techniques

2021 ◽  
Vol 27 (3) ◽  
pp. 1-11
Author(s):  
Lin Wei ◽  
Cheng-Shiu Chung ◽  
Alicia M. Koontz
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Objective Assessment ◽  
Assessment Tools ◽  
Assessment Instrument ◽  
Decision Threshold ◽  
Full Time ◽  
Test Set ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Background: Using proper transfer technique can help to reduce forces and prevent secondary injuries. However, current assessment tools rely on the ability to subjectively identify harmful movement patterns. Objectives: The purpose of the study was to determine the accuracy of using a low-cost markerless motion capture camera and machine learning methods to evaluate the quality of independent wheelchair sitting pivot transfers. We hypothesized that the algorithms would be able to discern proper (low risk) and improper (high risk) wheelchair transfer techniques in accordance with component items on the Transfer Assessment Instrument (TAI). Methods: Transfer motions of 91 full-time wheelchair users were recorded and used to develop machine learning classifiers that could be used to discern proper from improper technique. The data were labeled using the TAI item scores. Eleven out of 18 TAI items were evaluated by the classifiers. Motion variables from the Kinect were inputted as the features. Random forests and k-nearest neighbors algorithms were chosen as the classifiers. Eighty percent of the data were used for model training and hyperparameter turning. The validation process was performed using 20% of the data as the test set. Results: The area under the receiver operating characteristic curve of the test set for each item was over 0.79. After adjusting the decision threshold, the precisions of the models were over 0.87, and the model accuracies were over 71%. Conclusion: The results show promise for the objective assessment of the transfer technique using a low cost camera and machine learning classifiers.

scholarly journals Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 928
Author(s):  
Janggoon Lee ◽  
Chanhee Park ◽  
Heejun Roh
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
State Of The Art ◽  
Low Cost ◽  
Frequency Hopping ◽  
Classification Techniques ◽  
Machine Learning Classifiers ◽  
Learning Classifier ◽  
Learning Classifiers ◽  
And Training

Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.

scholarly journals Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase

2021 ◽  
Author(s):  
Yunzhuo Zhou ◽  
Stephanie Portelli ◽  
Megan Pat ◽  
Carlos H.M. Rodrigues ◽  
Thanh-Binh Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Resistance Mutations ◽  
Drug Resistance Mutations

scholarly journals Using machine learning and big data to explore the drug resistance landscape in HIV

2021 ◽  
Author(s):  
Luc Blassel ◽  
Anna Tostevin ◽  
Christian Julian Villabona-Arenas ◽  
Martine Peeters ◽  
Stéphane Hué ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Binding Pocket ◽  
Resistance Mutations ◽  
Genetic Barrier ◽  
Drug Resistance Mutations ◽  
The Uk ◽  
Treatment Pressure ◽  
New Mutations

AbstractDrug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs.We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs.When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, we did not find any significant signal of epistasis, beyond the standard resistance scheme associating major DRMs to auxiliary mutations.Author summaryAlmost all drugs to treat HIV target the Reverse Transcriptase (RT) and Drug resistance mutations (DRMs) appear in HIV under treatment pressure. Resistant strains can be transmitted and limit treatment options at the population level. Classically, multiple statistical testing is used to find DRMs, by comparing virus sequences of treated and naive populations. However, with this method, each mutation is considered individually and we cannot hope to reveal any interaction (epistasis) between them. Here, we used machine learning to discover new DRMs and study potential epistasis effects. We applied this approach to a very large UK dataset comprising ≈ 55, 000 RT sequences. Results robustness was checked on different UK and African datasets.Six new mutations associated to resistance were found. All six have a low genetic barrier and show high correlations with known DRMs. Moreover, all these mutations are close to either the active site or the regulatory binding pocket of RT. Thus, they are good candidates for further wet experiments to establish their role in drug resistance. Importantly, our results indicate that epistasis seems to be limited to the classical scheme where primary DRMs confer resistance and associated mutations modulate the strength of the resistance and/or compensate for the fitness cost induced by DRMs.

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