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 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.

CancerEMC: frontline non-invasive cancer screening from circulating protein biomarkers and mutations in cell-free DNA

2021 ◽  
Author(s):  
Saifur Rahaman ◽  
Xiangtao Li ◽  
Jun Yu ◽  
Ka-Chun Wong
Keyword(s):  
Test Data ◽  
Cancer Detection ◽  
Blood Test ◽  
Class Imbalance ◽  
Cancer Type ◽  
Protein Biomarkers ◽  
Class Imbalance Problem ◽  
Multiple Cancer ◽  
Cell Free Dna ◽  
Free Dna

Abstract Motivation The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge. Methods A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from the blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques. Results Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK published in Science, 2018) for cancer detection. The results reveal that our proposed method (i.e., CancerEMC) can achieve the best performance result for both binary cancer classification with 99.1748% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.1214% accuracy (AUC = 0.938). For addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.4966% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data. Availability https://github.com/saifurcubd/Cancer-Detection

An ultrasensitive method for noninvasive pan-cancer early detection based on targeted methylation sequencing of cell-free DNA.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10544-10544
Author(s):  
Tiancheng Han ◽  
Yuanyuan Hong ◽  
Pei Zhihua ◽  
Song Xiaofeng ◽  
Jianing Yu ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Cancer Detection ◽  
Real World ◽  
Early Stage ◽  
Validation Dataset ◽  
Cpg Sites ◽  
Free Dna ◽  
Cancer Types ◽  
Discovery Dataset ◽  
Pan Cancer

10544 Background: Screening the biomarkers from the cell-free DNA (cfDNA) of peripheral blood is a non-invasive and promising method for cancer diagnosis. Among diverse types of biomarkers, epigenetic biomarkers have been reported to be one of the most promising ones. Epigenetic modifications are widespread on the human genome and generally have strong signals due to the similar methylation patterns shared by adjacent CpG sites. Although some epigenetic diagnostic methods have been developed based on cfDNAs, few of them could be applied to pan-cancer and their sensitivities are barely satisfactory for early cancer detection. Methods: Targeted methylation sequencing was performed using our in-house-designed panel targeting regions with abundant cancer-specific methylation CpGs. The cfDNA samples from 80 healthy individuals and 549 cancer patients of 14 cancer types were separately sequenced. The dataset was randomly split into one discovery dataset and one validation dataset. Moreover, cfDNA samples from four cancer patients were diluted with the healthy cfDNAs to generate 12 in vitro simulated samples with low circulating tumor DNA (ctDNA) fraction. Additionally, DNAs extracted from 130 unmatched tumor formalin fixation and paraffin embedding (FFPE) samples of 10 cancer types were sequenced to screen the diagnostic biomarkers. Adjacent CpG sites were first merged into methylation-correlated blocks (MCB) according to their correlations of methylation levels in tumor DNAs. The MCBs with higher methylation levels in tumor DNAs than that of healthy cfDNAs (from the discovery dataset) were defined as our hypermethylation biomarkers. For each cfDNA sample, a hypermethylation score (HM-score) was computed to measure the overall methylation level difference of selected biomarkers. The performance of our method was evaluated with the real-world dataset, while the limit of detection was estimated using the simulated low-ctDNA samples. Results: Our model based on 37 hypermethylation MCB biomarkers achieved an area under the curve (AUC) of 0.89 and 0.86 in the real-world pan-cancer discovery and validation cfDNA datasets, respectively. Furthermore, the overall specificity and sensitivity are 100% and 76.19% in the discovery dataset, and 96.67% and 72.86% in the validation dataset. In the validation dataset, 28/40 (70%) of early-stage colorectal cancer patients and 10/20 (50%) of non-small-cell lung cancer patients were successfully diagnosed. Additionally, all the simulated samples with theoretical ctDNA factions over 0.5% were predicted as diseased, demonstrating the ability of our method to detect tumor signals at early stages. Conclusions: Our cfDNA-based epigenetic method outperforms currently available methods in various cancer types, and is promising to be applied to early-stage cancer detection and samples with low ctDNA fractions.

scholarly journals Can we measure beauty? Computational evaluation of coral reef aesthetics

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1390 ◽  
Author(s):  
Andreas F. Haas ◽  
Marine Guibert ◽  
Anja Foerschner ◽  
Tim Co ◽  
Sandi Calhoun ◽  
...  
Keyword(s):  
Coral Reef ◽  
Coral Reefs ◽  
Local Population ◽  
Machine Learning Algorithms ◽  
Monitoring Tool ◽  
Photographic Images ◽  
Aesthetic Appearance ◽  
The Aesthetic ◽  
Monitoring Protocols ◽  
Aesthetic Values

The natural beauty of coral reefs attracts millions of tourists worldwide resulting in substantial revenues for the adjoining economies. Although their visual appearance is a pivotal factor attracting humans to coral reefs current monitoring protocols exclusively target biogeochemical parameters, neglecting changes in their aesthetic appearance. Here we introduce a standardized computational approach to assess coral reef environments based on 109 visual features designed to evaluate the aesthetic appearance of art. The main feature groups include color intensity and diversity of the image, relative size, color, and distribution of discernable objects within the image, and texture. Specific coral reef aesthetic values combining all 109 features were calibrated against an established biogeochemical assessment (NCEAS) using machine learning algorithms. These values were generated for ∼2,100 random photographic images collected from 9 coral reef locations exposed to varying levels of anthropogenic influence across 2 ocean systems. Aesthetic values proved accurate predictors of the NCEAS scores (root mean square error < 5 forN≥ 3) and significantly correlated to microbial abundance at each site. This shows that mathematical approaches designed to assess the aesthetic appearance of photographic images can be used as an inexpensive monitoring tool for coral reef ecosystems. It further suggests that human perception of aesthetics is not purely subjective but influenced by inherent reactions towards measurable visual cues. By quantifying aesthetic features of coral reef systems this method provides a cost efficient monitoring tool that targets one of the most important socioeconomic values of coral reefs directly tied to revenue for its local population.

scholarly journals DISMIR: a deep learning-based cancer-detection method by integrating DNA sequence and methylation information of individual cell-free DNA reads

2021 ◽  
Author(s):  
Jiaqi Li ◽  
Lei Wei ◽  
Xianglin Zhang ◽  
Wei Zhang ◽  
Haochen Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Cancer Detection ◽  
High Throughput Sequencing ◽  
Detection Method ◽  
Low Cost ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Free Dna ◽  
Novel Approach

ABSTRACTDetecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel non-invasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise prediction with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as “switching region” to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state, and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultra-low sequencing depths. Analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites’ methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

scholarly journals Permission Sensitivity-Based Malicious Application Detection for Android

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yubo Song ◽  
Yijin Geng ◽  
Junbo Wang ◽  
Shang Gao ◽  
Wei Shi
Keyword(s):  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Private Data ◽  
Weight Allocation

Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.

Progressive and predictive markers of disease evolution: platelet transcriptome in chronic myeloproliferative neoplasms

2021 ◽  
Author(s):  
Zhu Shen ◽  
Wenfei Du ◽  
Cecelia Perkins ◽  
Lenn Fechter ◽  
Vanita Natu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Disease Risk ◽  
Myeloproliferative Neoplasms ◽  
Penalized Regression ◽  
Machine Learning Algorithms ◽  
Disease Evolution ◽  
Chronic Myeloproliferative Neoplasms ◽  
Age Related ◽  
Wide Range

Predicting disease natural history remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the spectrum of chronic myeloproliferative neoplasms (MPNs), as a model, we identify the blood platelet transcriptome as a generalizable strategy for highly sensitive progression biomarkers that also enable prediction via machine learning algorithms. Using RNA sequencing (RNA seq), we derive disease relevant gene expression and alternative splicing in purified platelets from 120 peripheral blood samples constituting two independently collected and mutually validating patient cohorts of the three MPN subtypes: essential thrombocythemia, ET (n=24), polycythemia vera, PV (n=33), and primary or post ET/PV secondary myelofibrosis, MF (n=42), as well as healthy donors (n=21). The MPN platelet transcriptome discriminates each clinical phenotype and reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy. Leveraging this dataset, in particular the progressive expression gradient noted across MPN, we develop a machine learning model (Lasso-penalized regression) predictive of the advanced subtype MF at high accuracy (AUC-ROC of 0.95-0.96) with validation under two conditions: i) temporal, with training on the first cohort (n=71) and independent testing on the second (n=49) and ii) 10 fold cross validation on the entire dataset. Lasso-derived signatures offer a robust core set of < 10 MPN progression markers. Mechanistic insights from our data highlight impaired protein homeostasis as a prominent driver of MPN evolution, with persistent integrated stress response. We also identify JAK inhibitor-specific signatures and other interferon, proliferation, and proteostasis associated markers as putative targets for MPN-directed therapy. Our platelet transcriptome snapshot of chronic MPNs establishes a methodological foundation for deciphering disease risk stratification and progression beyond genetic data alone, thus presenting a promising avenue toward potential utility in a wide range of age-related disorders.

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