scholarly journals A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1248
Author(s):  
Eleana Hatzidaki ◽  
Aggelos Iliopoulos ◽  
Ioannis Papasotiriou
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Cancer Screening ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Significant Information

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.

Tebyan: Fake News Detection System (Preprint)

2021 ◽  
Author(s):  
Lamya Alderywsh ◽  
Aseel Aldawood ◽  
Ashwag Alasmari ◽  
Farah Aldeijy ◽  
Ghadah Alqubisy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arab World ◽  
Detection System ◽  
Learning Algorithms ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Fake News ◽  
Typical Type ◽  
Performance Results

BACKGROUND There is a serious threat from fake news spreading in technologically advanced societies, including those in the Arab world, via deceptive machine-generated text. In the last decade, Arabic fake news identification has gained increased attention, and numerous detection approaches have revealed some ability to find fake news throughout various data sources. Nevertheless, many existing approaches overlook recent advancements in fake news detection, explicitly to incorporate machine learning algorithms system. OBJECTIVE Tebyan project aims to address the problem of fake news by developing a fake news detection system that employs machine learning algorithms to detect whether the news is fake or real in the context of Arab world. METHODS The project went through numerous phases using an iterative methodology to develop the system. This study analysis incorporated numerous stages using an iterative method to develop the system of misinformation and contextualize fake news regarding society's information. It consists of implementing the machine learning algorithms system using Python to collect genuine and fake news datasets. The study also assesses how information-exchanging behaviors can minimize and find the optimal source of authentication of the emergent news through system testing approaches. RESULTS The study revealed that the main deliverable of this project is the Tebyan system in the community, which allows the user to ensure the credibility of news in Arabic newspapers. It showed that the SVM classifier, on average, exhibited the highest performance results, resulting in 90% in every performance measure of sources. Moreover, the results indicate the second-best algorithm is the linear SVC since it resulted in 90% in performance measure with the societies' typical type of fake information. CONCLUSIONS The study concludes that conducting a system with machine learning algorithms using Python programming language allows the rapid measures of the users' perception to comment and rate the credibility result and subscribing to news email services.

scholarly journals Towards scaling Twitter for digital epidemiology of birth defects

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ari Z. Klein ◽  
Abeed Sarker ◽  
Davy Weissenbacher ◽  
Graciela Gonzalez-Hernandez
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Birth Defects ◽  
Birth Defect ◽  
Learning Algorithms ◽  
Class Imbalance ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Svm Classifier

Abstract Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes—the leading cause of infant mortality—could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms—feature-engineered and deep learning-based classifiers—that automatically distinguish tweets referring to the user’s pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F1-score of 0.65 for the “defect” class and 0.51 for the “possible defect” class. We deployed the classifier on 20,457 unlabeled tweets that mention birth defects, which helped identify 542 additional users for potential inclusion in our cohort. Contributions of this study include (1) NLP methods for automatically detecting tweets by users reporting their birth defect outcomes, (2) findings that an SVM classifier can outperform a deep neural network-based classifier for highly imbalanced social media data, (3) evidence that automatic classification can be used to identify additional users for potential inclusion in our cohort, and (4) a publicly available corpus for training and evaluating supervised machine learning algorithms.

scholarly journals Combined the SMAC mimetic and BCL2 inhibitor sensitizes neoadjuvant chemotherapy by targeting necrosome complexes in tyrosine aminoacyl-tRNA synthase-positive breast cancer

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Kyung-Min Lee ◽  
Hyebin Lee ◽  
Dohyun Han ◽  
Woo Kyung Moon ◽  
Kwangsoo Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Breast Cancer Cell Lines ◽  
Flow Cytometry Analysis ◽  
Breast Cancer Patients ◽  
Response To Chemotherapy ◽  
Positive Breast Cancer

Abstract Background Chemotherapy is the standard treatment for breast cancer; however, the response to chemotherapy is disappointingly low. Here, we investigated the alternative therapeutic efficacy of novel combination treatment with necroptosis-inducing small molecules to overcome chemotherapeutic resistance in tyrosine aminoacyl-tRNA synthetase (YARS)-positive breast cancer. Methods Pre-chemotherapeutic needle biopsy of 143 invasive ductal carcinomas undergoing the same chemotherapeutic regimen was subjected to proteomic analysis. Four different machine learning algorithms were employed to determine signature protein combinations. Immunoreactive markers were selected using three common candidate proteins from the machine-learning algorithms and verified by immunohistochemistry using 123 cases of independent needle biopsy FFPE samples. The regulation of chemotherapeutic response and necroptotic cell death was assessed using lentiviral YARS overexpression and depletion 3D spheroid formation assay, viability assays, LDH release assay, flow cytometry analysis, and transmission electron microscopy. The ROS-induced metabolic dysregulation and phosphorylation of necrosome complex by YARS were assessed using oxygen consumption rate analysis, flow cytometry analysis, and 3D cell viability assay. The therapeutic roles of SMAC mimetics (LCL161) and a pan-BCL2 inhibitor (ABT-263) were determined by 3D cell viability assay and flow cytometry analysis. Additional biologic process and protein-protein interaction pathway analysis were performed using Gene Ontology annotation and Cytoscape databases. Results YARS was selected as a potential biomarker by proteomics-based machine-learning algorithms and was exclusively associated with good response to chemotherapy by subsequent immunohistochemical validation. In 3D spheroid models of breast cancer cell lines, YARS overexpression significantly improved chemotherapy response via phosphorylation of the necrosome complex. YARS-induced necroptosis sequentially mediated mitochondrial dysfunction through the overproduction of ROS in breast cancer cell lines. Combination treatment with necroptosis-inducing small molecules, including a SMAC mimetic (LCL161) and a pan-BCL2 inhibitor (ABT-263), showed therapeutic efficacy in YARS-overexpressing breast cancer cells. Conclusions Our results indicate that, before chemotherapy, an initial screening of YARS protein expression should be performed, and YARS-positive breast cancer patients might consider the combined treatment with LCL161 and ABT-263; this could be a novel stepwise clinical approach to apply new targeted therapy in breast cancer patients in the future.

scholarly journals Assessment of circulating tumor cells in peripheral blood using flow cytometry in patients with surgery for colorectal cancer – review

2020 ◽  
Vol 28 (4) ◽  
pp. 365-379
Author(s):  
Ana-Maria Muşină ◽  
Ionuţ Huţanu ◽  
Mihaela Zlei ◽  
Mădălina Ştefan ◽  
Mihaela Mentel ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Flow Cytometry ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Peripheral Blood ◽  
Disease Free Survival ◽  
Final Analysis ◽  
Immunomagnetic Separation ◽  
Free Survival ◽  
Pubmed Database

AbstractIntroduction: Colorectal cancer (CRC) is the third most common neoplasia in the world. Circulating tumor cells (CTC) have a prognostic value and can be useful in monitoring solid neoplasia. Only one method for CTC identification has received the approval and this is the CellSearch® system based on the immunomagnetic separation. Multiple markers are used in CTC identification, as epithelial markers and cytokeratines. CTC identification in peripheral blood is associated with a worse prognostic and reduced free survival in CRC.Material and methods: We performed a systematic search in PubMed database for articles that reports the circulating tumor cells in CRC until July 2019. We selected studies in English and French and the main words used for search were ‘circulating tumor cells’, ‘colorectal cancer’, ‘colon cancer’, ‘rectal cancer’, ‘flow cytometry’, ‘peripheral blood’. We included studies with more than 10 patients, where samples were collected from the blood in relation with surgery and flow cytometry was used as analyzing technique.Results: We included 7 studies in final analysis, that showed in flow cytometry analysis a cut-off value of CTC that can vary from 2-4 CTC/ 7.5 ml peripheral blood with a sensitivity of 50.8% and specificity of 95%. Patients with positive CTC were associated with higher T stage and positive lymph nodes, with a worse overall survival (OS) and disease free survival (DFS) comparing with negative patients.Conclusion: CTC are considered to be a prognostic factor who needs more validation studies in order to be included in the clinical practice.

scholarly journals Detailed Analysis of Intrusion Detection using Machine Learning Algorithms

2020 ◽  
Vol 9 (1) ◽  
pp. 1894-1899 ◽  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Internet Users ◽  
Tree Classifier ◽  
Challenging Tasks

The number of internet users has increased exponentially over the years and so have increased intrusive activities significantly. To detect an intrusion attack in a system connected over a network is one of the most challenging tasks in today’s world. A significant number of techniques have been developed which are based on machine learning approaches to detect these intrusion attacks. Even though these techniques are good, they are not good enough to detect all kinds of attacks. In this paper, the analysis of different machine learning algorithm will be performed on the NSL-KDD dataset with pre-processing steps like One-hot encoding, feature selection and random sampling to use in different machine learning models to find the best performing model to detect these attacks. The attacks are from the datasets are classified into four types of attacks: Probe, DoS, U2R, R2L while the non- attack is the Normal. The dataset is in two parts: KDD-Train and KDD-Test. The dataset is trained and tested to find accuracy and understand the performance of different machine learning algorithms and compare them. The Machine Learning algorithms used are Naive Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, KNeighbours Classifier, Logistic Regression, SVM Classifier, Voting Classifier. These techniques are compared according to their capability to detect the attacks. This comparison will help to find the algorithm which would work the best to detect different kinds of intrusion attacks.

scholarly journals An Efficient FPGA-Based Hardware Accelerator for Convex Optimization-Based SVM Classifier for Machine Learning on Embedded Platforms

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1323
Author(s):  
Srikanth Ramadurgam ◽  
Darshika G. Perera
Keyword(s):  
Machine Learning ◽  
Convex Optimization ◽  
Learning Algorithms ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
System Level ◽  
Svm Classifier ◽  
Hardware Accelerator ◽  
Machine Learning Applications ◽  
Embedded Platforms

Machine learning is becoming the cornerstones of smart and autonomous systems. Machine learning algorithms can be categorized into supervised learning (classification) and unsupervised learning (clustering). Among many classification algorithms, the Support Vector Machine (SVM) classifier is one of the most commonly used machine learning algorithms. By incorporating convex optimization techniques into the SVM classifier, we can further enhance the accuracy and classification process of the SVM by finding the optimal solution. Many machine learning algorithms, including SVM classification, are compute-intensive and data-intensive, requiring significant processing power. Furthermore, many machine learning algorithms have found their way into portable and embedded devices, which have stringent requirements. In this research work, we introduce a novel, unique, and efficient Field Programmable Gate Array (FPGA)-based hardware accelerator for a convex optimization-based SVM classifier for embedded platforms, considering the constraints associated with these platforms and the requirements of the applications running on these devices. We incorporate suitable mathematical kernels and decomposition methods to systematically solve the convex optimization for machine learning applications with a large volume of data. Our proposed architectures are generic, parameterized, and scalable; hence, without changing internal architectures, our designs can be used to process different datasets with varying sizes, can be executed on different platforms, and can be utilized for various machine learning applications. We also introduce system-level architectures and techniques to facilitate real-time processing. Experiments are performed using two different benchmark datasets to evaluate the feasibility and efficiency of our hardware architecture, in terms of timing, speedup, area, and accuracy. Our embedded hardware design achieves up to 79 times speedup compared to its embedded software counterpart, and can also achieve up to 100% classification accuracy.

Gesture Classification of Surface Electromyography Signals Using Machine Learning Algorithms for Hand Prosthetics

2021 ◽  
Vol 11 (12) ◽  
pp. 3141-3152
Author(s):  
N. Subhashini ◽  
A. Kandaswamy
Keyword(s):  
Machine Learning ◽  
Surface Electromyography ◽  
Learning Algorithms ◽  
Ensemble Classifier ◽  
Correlation Factor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Movement Classification

The actions of humans executed by their hands play a remarkable part in controlling and handling variety of objects in their daily life activities. The effect of losing or degradation in the functioning of one hand has a greater influence in bringing down the regular activity. Hence the design of prosthetic hands which assists the individuals to enhance their regular activity seems a better remedy in this new era. This paper puts forward a classification framework using machine learning algorithms for classifying hand gesture signals. The surface electromyography (sEMG) dataset acquired for 9 wrist movements of publicly available database are utilized to identify the potential biomarkers for classification and in evaluating the efficacy of the proposed algorithm. The statistical and time domain features of the sEMG signals from 27 intact subjects and 11 trans-radial amputated subjects are extracted and the optimal features are determined implementing the feature selection approach based on correlation factor. The classifiers performance of machine learning algorithms namely support vector machine (SVM), Naïve bayes (NB) and Ensemble classifier are evaluated. The experimental results highlight that the SVM classifier can yield the maximum accuracy movement classification of 99.6% for intact and 97.56% for trans-amputee subjects. The proposed approach offers better accuracy and sensitivity compared to other approaches that have used the sEMG dataset for movement classification.

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