scholarly journals Machine Learning Reveals the Unique Biomarkers of Clonal Hematopoiesis in Patient With Early-Stage Colorectal Neoplasia: A Case Control Study

2021 ◽  
Author(s):  
Yin-Chen Hsu ◽  
Sin-Ming Huang ◽  
Li-Chun Chang ◽  
Yan-Ming Chen ◽  
Wei-Tzu Chiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Test ◽  
Early Stage ◽  
Colorectal Neoplasia ◽  
Learning Approach ◽  
Signature Analysis ◽  
Clonal Hematopoiesis ◽  
Mutational Signature ◽  
Machine Learning Model ◽  
Machine Learning Approach

Abstract BACKGROUND: Blood test has a better uptake for colorectal cancer screening than stool test and colonoscopy but suboptimal detection of early-stage colorectal neoplasia (CRN), including advanced adenoma and stage I cancer, limits its application. The present study aimed to evaluate whether clonal hematopoiesis (CH) from peripheral blood can be used as a biomarker for early-stage CRN screening and improve the detection of blood tests by machine-learning approach.METHODS: The CH profile was evaluated in 63 early-stage CRNs and 32 controls by error-corrected sequencing and classified by machine-learning method. Diagnostic performance was measured by receiver operator characteristic analysis. Additional 20 early-stage CRNs and 10 controls were used to validate the machine-learning model. We simultaneously used mutational signature analysis to study predictors based on CH.RESULTS: We identified 1,446 variants and clarified the uniqueness of variants from the peripheral bloods of early-stage CRNs. The machine learning model identified early-stage CRNs from controls and its AUC, sensitivity and specificity were 0.988, 94.2% and 99.3%, respectively. The CH-based CRN detection model was further verified. The accuracy, sensitivity, and specificity were 0.933 (p=0.00065), 95.0%, and 90.0%, respectively. Furthermore, the mutational signature analysis of those unique variants in CRNs revealed the influence of genetic architecture on DNA damages.CONCLUSIONS: Our results reveal the potential of CH to a mark produced by the carcinogenesis in early-stage CRN. We developed a CH-based blood test with machine learning approach, which not only increase screening uptake but also improve the detection rate of early-stage CRN.

scholarly journals Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant

2021 ◽  
Vol 12 (5) ◽  
pp. 810-817
Author(s):  
Sandhya N. dhage, Dr. Vijay Kumar Garg
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Cotton Plant ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Early Detection Of Disease ◽  
Cotton Crop ◽  
Machine Learning Approach ◽  
Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires  regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming  too late for diseases to be cured.  Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

scholarly journals Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

DYNA ◽  
2020 ◽  
Vol 87 (212) ◽  
pp. 63-72
Author(s):  
Jorge Iván Pérez Rave ◽  
Favián González Echavarría ◽  
Juan Carlos Correa Morales
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Approach ◽  
Predictive Capability ◽  
Predictive Capacity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Property Price ◽  
Object Of Study ◽  
Online Pricing

The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.

scholarly journals Can machine learning model with static features be fooled: an adversarial machine learning approach

2020 ◽  
Vol 23 (4) ◽  
pp. 3233-3253 ◽  
Author(s):  
Rahim Taheri ◽  
Reza Javidan ◽  
Mohammad Shojafar ◽  
P. Vinod ◽  
Mauro Conti
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Learning Approach ◽  
Machine Learning Model ◽  
Machine Learning Approach

An Innovative Machine Learning Approach to Diagnose Cancer at an Early Stage

2021 ◽  
pp. 313-337
Author(s):  
P. Poongodi ◽  
E. Udayakumar ◽  
K. Srihari ◽  
Nandan Mohanty Sachi
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

2018 ◽  
Author(s):  
Nathan Wan ◽  
David Weinberg ◽  
Tzu-Yu Liu ◽  
Katherine Niehaus ◽  
Daniel Delubac ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Cross Validation ◽  
Early Stage ◽  
Learning Approach ◽  
Whole Genome ◽  
Free Dna ◽  
Machine Learning Approach

AbstractBackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.MethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.ResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.ConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

scholarly journals A Machine Learning Approach for Predicting Student Performance

2019 ◽  
pp. 462-465
Author(s):  
C. Selvi ◽  
R. Shalini ◽  
V. Navaneethan ◽  
L. Santhiya
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Student Performance ◽  
Learning Model ◽  
Learning Approach ◽  
Economic Prosperity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

scholarly journals Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Kazuya Fujihara ◽  
Yasuhiro Matsubayashi ◽  
Mayuko Harada Yamada ◽  
Masahiko Yamamoto ◽  
Toshihiro Iizuka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Logistic Regression ◽  
Gold Standard ◽  
Learning Approach ◽  
Insulin Initiation ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
General Physicians

BACKGROUND Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings. OBJECTIVE The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes. METHODS Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists’ registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 for the dichotomous classification. Accuracy, recall, and area under the receiver operating characteristic curve (AUC) were calculated to compare the ability of machine learning models to make decisions regarding insulin initiation to the decision-making ability of logistic regression and general physicians. By comparing the decision-making ability of machine learning and logistic regression to that of general physicians, 7 cases were chosen based on patient information as the gold standard based on the agreement of 8 of the 9 specialists. RESULTS The AUCs, accuracy, and recall of logistic regression were higher than those of machine learning (AUCs of 0.89-0.90 for logistic regression versus 0.67-0.74 for machine learning). When the examination was limited to cases receiving insulin, discrimination by machine learning was similar to that of logistic regression analysis (recall of 0.05-0.68 for logistic regression versus 0.11-0.52 for machine learning). Accuracies of logistic regression, a machine learning model (downsampling ratio of 1:8), and general physicians were 0.80, 0.70, and 0.66, respectively, for 43 randomly selected cases. For the 7 gold standard cases, the accuracies of logistic regression and the machine learning model were 1.00 and 0.86, respectively, with a downsampling ratio of 1:8, which were higher than the accuracy of general physicians (ie, 0.43). CONCLUSIONS Although we found no superior performance of machine learning over logistic regression, machine learning had higher accuracy in prediction of insulin initiation than general physicians, defined by diabetes specialists’ choice of the gold standard. Further study is needed before the use of machine learning–based decision support systems for insulin initiation can be incorporated into clinical practice.

scholarly journals Automated doubt identification from informal reflections through hybrid sentic patterns and machine learning approach

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Siaw Ling Lo ◽  
Kar Way Tan ◽  
Eng Lieh Ouh
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Personalized Learning ◽  
Feedback Mechanism ◽  
Pattern Detection ◽  
Learning Approach ◽  
Timely Manner ◽  
Learner Centered ◽  
Machine Learning Model ◽  
Machine Learning Approach

AbstractDo my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to individual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ informal reflections. The encouraging results clearly show that the hybrid approach has the potential to be adopted in the real-world doubt detection. Using reflections as a feedback mechanism and automated doubt detection can pave the way to a promising approach for learner-centered teaching and personalized learning.

scholarly journals Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6569
Author(s):  
Maris Bauer ◽  
Raphael Hussung ◽  
Carsten Matheis ◽  
Hermann Reichert ◽  
Peter Weichenberger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Continuous Wave ◽  
Imaging System ◽  
Early Stage ◽  
Paper Industry ◽  
Terahertz Imaging ◽  
Image Resolution ◽  
Structural Defects ◽  
Learning Approach ◽  
Machine Learning Approach

We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.

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