scholarly journals Prediction of pyrazinamide resistance inMycobacterium tuberculosisusing structure-based machine learning approaches

2019 ◽  
Author(s):  
Joshua J Carter ◽  
Timothy M Walker ◽  
A Sarah Walker ◽  
Michael G. Whitfield ◽  
Glenn P. Morlock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Performance ◽  
Active Form ◽  
Missense Mutations ◽  
Learning Approaches ◽  
Proof Of Concept ◽  
First Line ◽  
Resistance Prediction ◽  
Amino Acid Mutations ◽  
Sensitivity Specificity

SummaryPyrazinamide is one of four first-line antibiotics used to treat tuberculosis, however antibiotic susceptibility testing for pyrazinamide is problematic. Resistance to pyrazinamide is primarily driven by genetic variation inpncA,an enzyme that converts pyrazinamide into its active form. We curated a derivation dataset of 291 non-redundant, missense amino acid mutations inpncAwith associated high-confidence phenotypes from published studies and then trained three different machine learning models to predict pyrazinamide resistance based on sequence- and structure-based features of each missense mutation. The clinical performance of the models was estimated by predicting the binary pyrazinamide resistance phenotype of 2,292 clinical isolates harboring missense mutations inpncA. Overall, this work offers an approach to improve the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs such as bedaquiline.

scholarly journals Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Stephanie Portelli ◽  
Yoochan Myung ◽  
Nicholas Furnham ◽  
Sundeep Chaitanya Vedithi ◽  
Douglas E. V. Pires ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nucleic Acid ◽  
Rna Polymerase ◽  
Gold Standard ◽  
Rifampicin Resistance ◽  
Missense Mutations ◽  
Learning Approaches ◽  
Current Gold Standard ◽  
Sequence Identity ◽  
Protein Affinities

Abstract Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/.

scholarly journals Machine Learning Approaches to Predict Hepatotoxicity Risk in Patients Receiving Nilotinib

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3300
Author(s):  
Jung-Sun Kim ◽  
Ji-Min Han ◽  
Yoon-Sook Cho ◽  
Kyung-Hee Choi ◽  
Hye-Sun Gwak
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Odds Ratio ◽  
Clinical Performance ◽  
Area Under The Curve ◽  
High Dose ◽  
Learning Approaches ◽  
Factors Affecting ◽  
H2 Blockers ◽  
Increased Risk

Background: Although nilotinib hepatotoxicity can cause severe clinical conditions and may alter treatment plans, risk factors affecting nilotinib-induced hepatotoxicity have not been investigated. This study aimed to elucidate the factors affecting nilotinib-induced hepatotoxicity. Methods: This retrospective cohort study was performed on patients using nilotinib from July of 2015 to June of 2020. We estimated the odds ratio and adjusted odds ratio from univariate and multivariate analyses, respectively. Several machine learning models were developed to predict risk factors of hepatotoxicity occurrence. The area under the curve (AUC) was analyzed to assess clinical performance. Results: Among 353 patients, the rate of patients with grade I or higher hepatotoxicity after nilotinib administration was 40.8%. Male patients and patients who received nilotinib at a dose of ≥300 mg had a 2.3-fold and a 3.5-fold increased risk for hepatotoxicity compared to female patients and compared with those who received <300 mg, respectively. H2 blocker use decreased hepatotoxicity by 11.6-fold. The area under the curve (AUC) values of machine learning methods ranged between 0.61–0.65 in this study. Conclusion: This study suggests that the use of H2 blockers was a reduced risk of nilotinib-induced hepatotoxicity, whereas male gender and a high dose were associated with increased hepatotoxicity.

scholarly journals Automated Recognition of Diabetic Retinopathy using Machine Learning Techniques

2019 ◽  
Vol 8 (4S2) ◽  
pp. 602-606
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Automated Recognition ◽  
Image Contrast Enhancement ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Sensitivity Specificity

Diabetic Retinopathy (DR) is the illness due to severe polygenic disorders that result in loss of vision for the patients. The development in computer science leads to the timely recognition of DR through an automatic system that is more advantageous than the diagnosis done by a doctor. This paper reviews the DR diagnosis technique that includes deep learning, machine learning and image processing based approaches and their performance. Among the machine learning approaches, the Artificial Neural Network (ANN) classification technique results in high accuracy. The green channel extraction based image contrast enhancement has high classification accuracy, which outperforms the image processing techniques. The performance of the model is estimated by the metrics including sensitivity, specificity and accuracy. This study presents depth insights of techniques for automated DR diagnosis.

scholarly journals Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jung-Joon Cha ◽  
Tran Dinh Son ◽  
Jinyong Ha ◽  
Jung-Sun Kim ◽  
Sung-Jin Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Optical Coherence ◽  
Learning Approaches ◽  
Fractional Flow ◽  
Flow Reserve ◽  
Sensitivity Specificity ◽  
Testing Group

AbstractMachine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been investigated. Both OCT and FFR data were obtained for left anterior descending artery lesions in 125 patients. Training and testing groups were partitioned in the ratio of 5:1. The OCT-based machine learning-FFR was derived for the testing group and compared with wire-based FFR in terms of ischemia diagnosis (FFR ≤ 0.8). The OCT-based machine learning-FFR showed good correlation (r = 0.853, P < 0.001) with the wire-based FFR. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the OCT-based machine learning-FFR for the testing group were 100%, 92.9%, 87.5%, 100%, and 95.2%, respectively. The OCT-based machine learning-FFR can be used to simultaneously acquire information on both image and functional modalities using one procedure, suggesting that it may provide optimized treatments for intermediate coronary artery stenosis.

Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas

2020 ◽  
pp. 000348942095036
Author(s):  
Felix Parker ◽  
Martin B. Brodsky ◽  
Lee M. Akst ◽  
Haider Ali
Keyword(s):  
Machine Learning ◽  
Learning Technologies ◽  
Learning Approaches ◽  
Automated Classification ◽  
Proof Of Concept ◽  
Training Set ◽  
Convenience Sample ◽  
Applied Machine Learning ◽  
Subjective Evaluations ◽  
Computer Recognition

Objective: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annotated data available for training. Convolutional neural networks (CNNs) have the potential to improve image analysis and have demonstrated good performance in many settings. This study applied machine-learning technologies to laryngoscopy to determine the accuracy of computer recognition of known laryngeal lesions found in patients post-extubation. Methods: This is a proof of concept study that used a convenience sample of transnasal, flexible, distal-chip laryngoscopy images from patients post-extubation in the intensive care unit. After manually annotating images at the pixel-level, we applied a CNN-based method for analysis of granulomas and ulcerations to test potential machine-learning approaches for laryngoscopy analysis. Results: A total of 127 images from 25 patients were manually annotated for presence and shape of these lesions—100 for training, 27 for evaluating the system. There were 193 ulcerations (148 in the training set; 45 in the evaluation set) and 272 granulomas (208 in the training set; 64 in the evaluation set) identified. Time to annotate each image was approximately 3 minutes. Machine-based analysis demonstrated per-pixel sensitivity of 82.0% and 62.8% for granulomas and ulcerations respectively; specificity was 99.0% and 99.6%. Conclusion: This work demonstrates the feasibility of machine learning via CNN-based methods to add objectivity to laryngoscopy analysis, suggesting that CNN may aid in laryngoscopy analysis for other conditions in the future.

scholarly journals Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study

2021 ◽  
pp. 167-175
Author(s):  
Megan K. O’Brien ◽  
Olivia K. Botonis ◽  
Elissa Larkin ◽  
Julia Carpenter ◽  
Bonnie Martin-Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wearable Sensors ◽  
Measurement Techniques ◽  
Sensor Data ◽  
Patient Specific ◽  
Learning Approaches ◽  
Proof Of Concept ◽  
Wearable Sensor ◽  
Complex Signals ◽  
Promising Alternative

<b><i>Introduction:</i></b> Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning. <b><i>Methods:</i></b> Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls). <b><i>Results:</i></b> These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (<i>p</i> &#x3c; 0.037). <b><i>Discussion:</i></b> Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.

scholarly journals A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis

BMC Genomics ◽  
2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Wouter Deelder ◽  
Gary Napier ◽  
Susana Campino ◽  
Luigi Palla ◽  
Jody Phelan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Learning Approach ◽  
Learning Approaches ◽  
Specific Context ◽  
Genomic Variants ◽  
Drug Resistance Prediction ◽  
Resistance Prediction ◽  
Tree Approach ◽  
Disease Specific

Abstract Background Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. Results We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). Conclusion Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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