scholarly journals An AI-powered blood test to detect cancer using nanoDSF

2020 ◽  
Author(s):  
Philipp O. Tsvetkov ◽  
Rémi Eyraud ◽  
Stéphane Ayache ◽  
Anton A. Bougaev ◽  
Soazig Malesinski ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Blood Test ◽  
Diagnostic Method ◽  
Machine Learning Algorithms ◽  
Healthy Controls ◽  
Monitoring Tool ◽  
Cancer Diagnostic ◽  
Differential Scanning Fluorimetry ◽  
Pan Cancer

AbstractWe describe a novel cancer diagnostic method based on plasma denaturation profiles obtained by a non-conventional use of Differential Scanning Fluorimetry. We show that 84 glioma patients and 63 healthy controls can be automatically classified using denaturation profiles with the help of machine learning algorithms with 92% accuracy. Proposed high throughput workflow can be applied to any type of cancer and could become a powerful pan-cancer diagnostic and monitoring tool from a simple blood test.

scholarly journals An AI-Powered Blood Test to Detect Cancer Using NanoDSF

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1294
Author(s):  
Philipp O. Tsvetkov ◽  
Rémi Eyraud ◽  
Stéphane Ayache ◽  
Anton A. Bougaev ◽  
Soazig Malesinski ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Blood Test ◽  
Detection Method ◽  
Primary Brain Tumor ◽  
Machine Learning Algorithms ◽  
Monitoring Tool ◽  
Disease Evolution ◽  
Differential Scanning Fluorimetry ◽  
Pan Cancer ◽  
Deep Location

Glioblastoma is the most frequent and aggressive primary brain tumor. Its diagnosis is based on resection or biopsy that could be especially difficult and dangerous in the case of deep location or patient comorbidities. Monitoring disease evolution and progression also requires repeated biopsies that are often not feasible. Therefore, there is an urgent need to develop biomarkers to diagnose and follow glioblastoma evolution in a minimally invasive way. In the present study, we described a novel cancer detection method based on plasma denaturation profiles obtained by a non-conventional use of differential scanning fluorimetry. Using blood samples from 84 glioma patients and 63 healthy controls, we showed that their denaturation profiles can be automatically distinguished with the help of machine learning algorithms with 92% accuracy. Proposed high throughput workflow can be applied to any type of cancer and could become a powerful pan-cancer diagnostic and monitoring tool requiring only a simple blood test.

scholarly journals OS1.7 Plasmatic differential scanning fluorimetry profiles discriminate glioma patients from healthy controls

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii7-iii7
Author(s):  
E Tabouret ◽  
P Tsvetkov ◽  
R Eyraud ◽  
T Peel ◽  
S Malesinski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Denaturation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Healthy Controls ◽  
Wild Type ◽  
Protein Fluorescence ◽  
Adaptive Boosting ◽  
Differential Scanning Fluorimetry

Abstract BACKGROUND differential scanning fluorimetry (DSF) has been recently proposed to be used to perform high throughput biofluids profiling by following protein denaturation. Our objective was to discriminate patients with glioma from healthy controls using plasmatic DSF profiles. MATERIAL AND METHODS We included 78 glioma patients and 44 healthy controls. Plasmas were collected using EDTA tubes and analyzed in duplicate using nanoDSF Prometheus NT.Plex instrument (Nanotemper). The following DSF data were analyzed: protein fluorescence at 330 and 350 nm, first derivation of the ratio of fluorescence at 330 and 350 and the absorbance at 350 nm. Then we ran several machine learning algorithms to differentiate gliomas from healthy controls: Logistic Regression (LR), Support Vector Machine (SVM), Neural Networks (NN), Random Forest (RF) and Adaptive Boosting (AdaBoost). All these methods have been tested using a leave-one-out approach where each datum is used once as test while the other are used to train the automatic classifiers. RESULTS We included 78 patients with a median age of 57.8 years (range, 21.8 - 89.9). Thirteen patients (17%) presented with a 1p/19q codeleted IDH mutated oligodendroglioma, 12 patients (15%) with an IDH mutated astrocytoma and 53 patients (68%) with an IDH wild-type astrocytoma, including 26 patients with a recurrent grade IV IDH wild-type astrocytoma. DSF data were independent from classical prognostic factors or patient characteristics: age, Karnofsky Performans Status, IDH mutation status, 1p19q codeletion, grade, initial or recurrent setting, steroid doses, patient size and weight, tumor size and location. The different datasets of the DFS output were tested independently and in combination as input of the machine learning algorithms. Results were obtained using the tuned best parameters on 157 data. The best obtained accuracy was 95.54% with 2% of fake positives and 5% of fake negatives (algorithm: SVM). Others achieved ≥ 90% of correct classification: LR accuracy was 89.17%, NN accuracy was 92.99%, RF accuracy was 91.72% and Adaboost accuracy was 92.36%. CONCLUSION DSF profiles analyzed by machine learning algorithms could allow us to identify glioma patients from healthy controls with an accuracy of more than 95%. These results suggest that DSF of biofluid could be a useful and non-invasive tool to monitor glioma patients. Further investigations, including longitudinal profile analyses are ongoing.

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

scholarly journals Machine learning as the new approach in understanding biomarkers of suicidal behavior

2020 ◽  
Author(s):  
Alja Videtič Paska ◽  
Katarina Kouter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
High Throughput ◽  
Suicidal Behavior ◽  
Technological Development ◽  
Machine Learning Algorithms ◽  
Classical Approach ◽  
New Approach

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore ‘omic’ studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

scholarly journals Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure

2018 ◽  
Author(s):  
Romain F. Laine ◽  
Gemma Goodfellow ◽  
Laurence J. Young ◽  
Jon Travers ◽  
Danielle Carroll ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Vaccine Development ◽  
Functional Performance ◽  
Rapid Assessment ◽  
Super Resolution ◽  
Machine Learning Algorithms ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
High Throughput Analysis

AbstractOptical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches

2021 ◽  
Vol 35 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Chun-Hung Chang ◽  
Chieh-Hsin Lin ◽  
Chieh-Yu Liu ◽  
Chih-Sheng Huang ◽  
Shaw-Ji Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forest ◽  
Naive Bayes ◽  
Mmse Score ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Healthy Controls ◽  
Peripheral Plasma

Background: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. Aims: This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. Methods: Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. Results: The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels ( r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score ( B = 0.003, 95% confidence interval 0.002–0.005, p < 0.001). Conclusions: Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.

scholarly journals O033 Predicting subjective sleep quality using multi-day actigraphy data: A machine learning approach

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A14-A15
Author(s):  
C Kao ◽  
A D’Rozario ◽  
N Lovato ◽  
D Bartlett ◽  
S Postnova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Quality ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Poor Sleep ◽  
Healthy Controls ◽  
Subjective Sleep Quality ◽  
Subjective Sleep ◽  
Quality Ratings ◽  
Insomnia Disorder

Abstract Objectives Insomnia is diagnosed using clinical interview but actigraphy is often used as a consecutive multi-day measurement of activity-rest cycles to quantify sleep-wake periods. However, discrepancies between subjective complaints of insomnia and objective actigraphy measurement exist. The aims of the current study were to (i) predict subjective sleep quality using actigraphic data and, (ii) identify features of actigraphy that are associated with poor subjective sleep quality. Methods Actigraphy data were collected for 14-consecutive days with corresponding subjective sleep quality ratings from participants with Insomnia Disorder and healthy controls. We fitted multiple machine learning algorithms to determine the best performing method with the highest accuracy of predicting subjective quality rating using actigraphic data. Results We analysed a total of 1278 days of actigraphy and corresponding subjective sleep quality ratings from 86 insomnia disorder patients and 20 healthy controls. The k-neighbors classifier provided the best performance in predicting subjective sleep quality with an overall accuracy, sensitivity and specificity of 83%, 74% and 87% respectively, and an average AUC-ROC of 0.88. We also found that activity recorded in the early morning (04:00-08:00) and overnight periods (00:00-04:00) had the greatest influence on sleep quality scores, with poor sleep quality related to these periods.. Conclusions A machine learning model based on actigraphy time-series data successfully predicted self-reported sleep quality. This approach could facilitate clinician’s diagnostic capabilities and provide an objective marker of subjective sleep disturbance.

scholarly journals THYROID DETECTION USING MACHINE LEARNING

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Chandan R ◽  
Chetan Vasan ◽  
Chethan MS ◽  
Devikarani H S
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Blood Test ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Disease Prediction ◽  
Thyroid Disorders ◽  
Nearest Neighbours ◽  
Web App

The Thyroid gland is a vascular gland and one of the most important organs of a human body. This gland secretes two hormones which help in controlling the metabolism of the body. The two types of Thyroid disorders are Hyperthyroidism and Hypothyroidism. When this disorder occurs in the body, they release certain type of hormones into the body which imbalances the body’s metabolism. Thyroid related Blood test is used to detect this disease but it is often blurred and noise will be present. Data cleansing methods were used to make the data primitive enough for the analytics to show the risk of patients getting this disease. Machine Learning plays a very deciding role in the disease prediction. Machine Learning algorithms, SVM - support vector machine, decision tree, logistic regression, KNN - K-nearest neighbours, ANNArtificial Neural Network are used to predict the patient’s risk of getting thyroid disease. Web app is created to get data from users to predict the type of disease.

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