scholarly journals Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcelo Saito Nogueira ◽  
Leonardo Barbosa Leal ◽  
Wena Macarini ◽  
Raquel Lemos Pimentel ◽  
Matheus Muller ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Cost Effective ◽  
Attenuated Total Reflection ◽  
Classification Model ◽  
Label Free ◽  
Nearest Neighbours ◽  
Reduction Strategies ◽  
Oropharyngeal Swab ◽  
Atr Spectroscopy

AbstractEarly diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.

EXPRESS: Disposable Coverslip for Rapid Throughput Screening of Malaria Using Attenuated Total Reflection (ATR) Spectroscopy

2021 ◽  
pp. 000370282110127
Author(s):  
Thulya Chakkumpulakkal Puthan Veettil ◽  
Kamila Kochan ◽  
Karen Edler ◽  
Paul Andrew De Bank ◽  
Philip Heraud ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Parasitic Infections ◽  
Label Free ◽  
Least Squares Regression ◽  
Early Detection Of Disease ◽  
Diagnostic Potential ◽  
Atr Spectroscopy

Malaria is considered to be one of the most catastrophic health issues in the whole world. Vibrational spectroscopy is a rapid, robust, label-free, inexpensive, highly sensitive, non-perturbative and non-destructive technique with high diagnostic potential for the early detection of disease agents. In particular, the fingerprinting capability of Attenuated Total Reflection (ATR) spectroscopy is promising as a point-of-care (POC) diagnostic tool in resource limited areas. However, improvements are required to expedite the measurements of biofluids, including the drying procedure and subsequent cleaning of the internal reflection element (IRE) to enable high throughput successive measurements. As an alternative, we propose using an inexpensive coverslip to reduce the sample preparation time by enabling multiple samples to be collectively dried together under the same temperature and conditions. In conjunction with Partial Least-Squares Regression (PLS-R), ATR spectroscopy was able to detect and quantify the parasitemia with Root Mean Square Error of Cross Validation (RMSECV) and R<sup>2</sup> values of 0.177 and 0.985, respectively. Here we characterise an inexpensive, disposable coverslip for the high throughput screening of malaria parasitic infections and thus demonstrate an alternative approach to direct deposition of the sample onto the IRE.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer

2015 ◽  
Vol 11 (12) ◽  
pp. 3362-3377 ◽  
Author(s):  
Vinay Randhawa ◽  
Anil Kumar Singh ◽  
Vishal Acharya
Keyword(s):  
Machine Learning ◽  
Oral Cancer ◽  
High Throughput ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Systematic Approach ◽  
Molecular Descriptor ◽  
Classification Model

Network-based and cheminformatics approaches identify novel lead molecules forCXCR4, a key gene prioritized in oral cancer.

scholarly journals Diagnostic Value of FTIR Spectroscopy, Metabolomic Screening and Molecular Genetics in Saliva for Early Detection of Oral Squamous Cell Carcinoma (OSCC)

2020 ◽  
Vol 6 (3) ◽  
Keyword(s):  
Early Detection ◽  
Oral Cavity ◽  
Ftir Spectroscopy ◽  
Molecular Mechanisms ◽  
Cost Effective ◽  
Diagnostic Value ◽  
Vital Role ◽  
Spectroscopic Technique ◽  
Attenuated Total Reflection ◽  
Label Free

Oral cavity cancer (OCC) has become a prevalent malignancy worldwide. Despite, the current developments of diagnoses and therapies, the 5-year survival rate has persisted at a dismal of 50% in recent decades. Histopathological evaluation remains the golden standard method for cancer detection. However, in some cases the histopathological assessment may not be able to give a definitive diagnosis due to pitfalls in the interpretation of biopsy samples. There are well identified benign conditions in the oral cavity that could mimic malignancy. Hence, it is a timely approach to understand the utility of other emerging techniques which could be used in conjunction with the histopathological assessment. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy has been employed extensively to diagnose various diseases by determining the chemical and molecular alterations. As a cost-effective, minimally invasive or non-invasive and label-free, bio spectroscopic technique this could be developed into an excellent diagnostic tool in the years to come. Besides this, OCC is known to alter the composition of metabolites in saliva. Analysis of the metabolomics in saliva of OCC patients could provide additional information that would be useful to establish a panel of biomarkers with regard to early detection. Furthermore, the rising epidemiological significance underlines the requirement of a better understanding of molecular mechanisms and the recognition of extrapolative tumor markers. Thus, gene expression analysis plays a vital role in identifying those genes related to the progression of this disease. In here, we review the potential applications of FTIR analysis in disease detection and metabolomics in verifying FTIR spectral data. Moreover, the genetic and epigenetic anomalies in OSCC will be briefly discussed along with the salivary biomarkers enabling the detection of this disease.

scholarly journals Label-free identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

2017 ◽  
Author(s):  
Jonghee Yoon ◽  
YoungJu Jo ◽  
Min-hyeok Kim ◽  
Kyoohyun Kim ◽  
SangYun Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Refractive Index ◽  
Magnetic Beads ◽  
Three Dimensional ◽  
Cell Types ◽  
Biochemical Properties ◽  
Classification Model ◽  
Label Free ◽  
Specificity And Sensitivity ◽  
Lymphocyte Cell

Identification of lymphocyte cell types is crucial for understanding their pathophysiologic roles in human diseases. Current methods for discriminating lymphocyte cell types primarily relies on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present label-free identification of non-activated lymphocyte subtypes using refractive index tomography. From the measurements of three-dimensional refractive index maps of individual lymphocytes, the morphological and biochemical properties of the lymphocytes are quantitatively retrieved. Machine learning methods establish an optimized classification model using the retrieved quantitative characteristics of the lymphocytes to identify lymphocyte subtypes at the individual cell level. We show that our approach enables label-free identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T lymphocytes) with high specificity and sensitivity. The present method will be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

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