scholarly journals High performance methylated DNA markers for detection of colon adenocarcinoma

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Romy A. M. Klein Kranenbarg ◽  
Abdul Hussain Vali ◽  
Jan N. M. IJzermans ◽  
Thomas R. Pisanic ◽  
Tza-Huei Wang ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Performance ◽  
Methylation Status ◽  
Stage Iv ◽  
Gene Panel ◽  
Training Set ◽  
Test Set ◽  
Marker Panel ◽  
Methylated Dna ◽  
Fresh Frozen

Abstract Background Colon cancer (CC) is treatable if detected in its early stages. Improved CC detection assays that are highly sensitive, specific, and available at point of care are needed. In this study, we systematically selected and tested methylated markers that demonstrate high sensitivity and specificity for detection of CC in tissue and circulating cell-free DNA. Methods Hierarchical analysis of 22 candidate CpG loci was conducted using The Cancer Genome Atlas (TCGA) COAD 450K HumanMethylation database. Methylation of 13 loci was analyzed using quantitative multiplex methylation-specific PCR (QM-MSP) in a training set of fresh frozen colon tissues (N = 53). Hypermethylated markers were identified that were highest in cancer and lowest in normal colon tissue using the 75th percentile in Mann–Whitney analyses and the receiver operating characteristic (ROC) statistic. The cumulative methylation status of the marker panel was assayed in an independent test set of fresh frozen colon tissues (N = 52) using conditions defined and locked in the training set. A minimal marker panel of 6 genes was defined based on ROC area under the curve (AUC). Plasma samples (N = 20 colorectal cancers, stage IV and N = 20 normal) were tested by cMethDNA assay to evaluate marker performance in liquid biopsy. Results In the test set of samples, compared to normal tissue, a 6-gene panel showed 100% sensitivity and 90% specificity for detection of CC, and an AUC of 1.00 (95% CI 1.00, 1.00). In stage IV colorectal cancer plasma versus normal, an 8-gene panel showed 95% sensitivity, 100% specificity, and an AUC of 0.996 (95% CI 0.986, 1.00) while a 5-gene subset showed 100% sensitivity, 100% specificity, and an AUC of 1.00 (95% CI 1.00, 1.00), highly concordant with our observations in tissue. Conclusions We identified high performance methylated DNA marker panels for detection of CC. This knowledge has set the stage for development and implementation of novel, automated, self-contained CC detection assays in tissue and blood which can expeditiously and accurately detect colon cancer in both developed and underdeveloped regions of the world, enabling optimal use of limited resources in low- and middle-income countries.

A 13-immune gene set signature for prediction of colon cancer prognosis

2020 ◽  
Vol 23 ◽  
Author(s):  
Zhuo Lu ◽  
Jin Chen ◽  
JiongYi Yan ◽  
QiaoMing Liu ◽  
Fang Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
External Validation ◽  
Therapeutic Targets ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Immune Gene ◽  
Training Set ◽  
Test Set ◽  
Gene Set ◽  
Validation Set

Background: Colon cancer is one of the most common cancer worldwide and has a poor prognosis. Through the analysis of transcriptome and clinical data of colon cancer, immune gene-set signature was identified by single sample enrichment analysis (ssGSEA) scoring to predict patient survival and discover new therapeutic targets. Objective: To study the role of immune gene-set signature in colon cancer. Methods: First, RNASeq and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA). Immune gene-related gene sets were collected from ImmPort database. Genes and immunological pathways related to prognosis were screened in the training set and integrated for feature selection using random forest. Immune gene-related prognosis model was verified in the entire TCGA test set and GEO validation set and compared with immune cells scores and matrix score. Results: 1650 prognostic genes and 13 immunological pathways were identified. These genes and pathways are closely related to the development of tumors. 13-immune gene-set signature was established, which is an independent prognostic factor for patients with colon cancer. Risk stratification of samples could be carried out in the training set, test set and external validation set. The AUC of five-year survival in the training set and validation set is greater than 0.6. Immunosuppression occurs in high-risk samples. Compared with published models, Riskscore has better prediction effect. Conclusion: This study constructed 13-immune gene-set signature as a new prognostic marker to predict the survival of patients with colon cancer, and provided new diagnostic/prognostic biomarkers and therapeutic targets for colon cancer.

scholarly journals Modeling US Dollar and Nigerian Naira Exchange Rates During COVID-19 Pandemic Period: Identification of a High-performance Model for New Applications

2021 ◽  
Vol 2 (1) ◽  
pp. 40-52
Author(s):  
Chukwudi Paul Obite ◽  
Ugochinyere Ihuoma Nwosu ◽  
Desmond Chekwube Bartholomew
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Random Forest ◽  
Exchange Rates ◽  
High Performance ◽  
Performance Model ◽  
Training Set ◽  
Test Set ◽  
The Neural Network ◽  
Us Dollar

This study modeled the US Dollar and Nigerian Naira exchange rates during COVID-19 pandemic period using a classical statistical method – Autoregressive Integrated Moving Average (ARIMA) – and two machine learning methods – Artificial Neural Network (ANN) and Random Forest (RF). The data were divided into two sets namely: the training set and the test set. The training set was used to obtain the parameters of the model, and the performance of the estimated model was validated on the test set that served as new data. Though the ARIMA and random forest performed slightly better than the neural network in the training set, their performance in the test set was poor. The neural network with 5 nodes in the input layer, 5 nodes in the hidden layer and 1 node in the output layer (ANN (5,5,1)) performed better on the new data set (test set) and is chosen as the best model to forecast for future USD to NGN exchange rate. The information from the high-performance model (ANN (5, 5, 1)) for modeling the USD to NGN exchange rate will assist econometric trading of the currencies and offer both speculative and precautionary assistance to individuals, households, firms and nations who use the currencies locally and for international trade.

scholarly journals Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto’s Thyroiditis

2021 ◽  
Vol 11 ◽  
Author(s):  
Yiqing Hou ◽  
Chao Chen ◽  
Lu Zhang ◽  
Wei Zhou ◽  
Qinyang Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Hashimoto’S Thyroiditis ◽  
High Performance ◽  
Deep Neural Network ◽  
Thyroid Nodules ◽  
Hashimoto's Thyroiditis ◽  
Training Set ◽  
Test Set ◽  
The One ◽  
Benign Nodules

ObjectiveThe aim of this study is to develop a model using Deep Neural Network (DNN) to diagnose thyroid nodules in patients with Hashimoto’s Thyroiditis.MethodsIn this retrospective study, we included 2,932 patients with thyroid nodules who underwent thyroid ultrasonogram in our hospital from January 2017 to August 2019. 80% of them were included as training set and 20% as test set. Nodules suspected for malignancy underwent FNA or surgery for pathological results. Two DNN models were trained to diagnose thyroid nodules, and we chose the one with better performance. The features of nodules as well as parenchyma around nodules will be learned by the model to achieve better performance under diffused parenchyma. 10-fold cross-validation and an independent test set were used to evaluate the performance of the algorithm. The performance of the model was compared with that of the three groups of radiologists with clinical experience of <5 years, 5–10 years, >10 years respectively.ResultsIn total, 9,127 images were collected from 2,932 patients with 7,301 images for the training set and 1,806 for the test set. 56% of the patients enrolled had Hashimoto’s Thyroiditis. The model achieved an AUC of 0.924 for distinguishing malignant and benign nodules in the test set. It showed similar performance under diffused thyroid parenchyma and normal parenchyma with sensitivity of 0.881 versus 0.871 (p = 0.938) and specificity of 0.846 versus 0.822 (p = 0.178). In patients with HT, the model achieved an AUC of 0.924 to differentiate malignant and benign nodules which was significantly higher than that of the three groups of radiologists (AUC = 0.824, 0.857, 0.863 respectively, p < 0.05).ConclusionThe model showed high performance in diagnosing thyroid nodules under both normal and diffused parenchyma. In patients with Hashimoto’s Thyroiditis, the model showed a better performance compared to radiologists with various years of experience.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

scholarly journals QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors

2009 ◽  
Vol 7 (4) ◽  
pp. 846-856 ◽  
Author(s):  
Andrey Toropov ◽  
Alla Toropova ◽  
Emilio Benfenati
Keyword(s):  
Partition Coefficient ◽  
Organometallic Compounds ◽  
Applicability Domain ◽  
Training Set ◽  
Input Line ◽  
Test Set ◽  
Water Partition Coefficient ◽  
Definition Of

AbstractUsually, QSPR is not used to model organometallic compounds. We have modeled the octanol/water partition coefficient for organometallic compounds of Na, K, Ca, Cu, Fe, Zn, Ni, As, and Hg by optimal descriptors calculated with simplified molecular input line entry system (SMILES) notations. The best model is characterized by the following statistics: n=54, r2=0.9807, s=0.677, F=2636 (training set); n=26, r2=0.9693, s=0.969, F=759 (test set). Empirical criteria for the definition of the applicability domain for these models are discussed.

scholarly journals Feature-Weighted Sampling for Proper Evaluation of Classification Models

2021 ◽  
Vol 11 (5) ◽  
pp. 2039
Author(s):  
Hyunseok Shin ◽  
Sejong Oh
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Classification Model ◽  
Training Set ◽  
Test Set ◽  
Feature Importance ◽  
Proper Training ◽  
Machine Learning Applications ◽  
Test Sets ◽  
The Given

In machine learning applications, classification schemes have been widely used for prediction tasks. Typically, to develop a prediction model, the given dataset is divided into training and test sets; the training set is used to build the model and the test set is used to evaluate the model. Furthermore, random sampling is traditionally used to divide datasets. The problem, however, is that the performance of the model is evaluated differently depending on how we divide the training and test sets. Therefore, in this study, we proposed an improved sampling method for the accurate evaluation of a classification model. We first generated numerous candidate cases of train/test sets using the R-value-based sampling method. We evaluated the similarity of distributions of the candidate cases with the whole dataset, and the case with the smallest distribution–difference was selected as the final train/test set. Histograms and feature importance were used to evaluate the similarity of distributions. The proposed method produces more proper training and test sets than previous sampling methods, including random and non-random sampling.

scholarly journals Blood-Based Detection of Colorectal Cancer Using Cancer-Specific DNA Methylation Markers

Diagnostics ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 51
Author(s):  
Nam-Yun Cho ◽  
Ji-Won Park ◽  
Xianyu Wen ◽  
Yun-Joo Shin ◽  
Jun-Kyu Kang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Sensitivity And Specificity ◽  
Stage Iv ◽  
High Sensitivity ◽  
Liquid Biopsies ◽  
Blood Leukocytes ◽  
Marker Panel ◽  
A Genome ◽  
Methylight Assay

Cancer tissues have characteristic DNA methylation profiles compared with their corresponding normal tissues that can be utilized for cancer diagnosis with liquid biopsy. Using a genome-scale DNA methylation approach, we sought to identify a panel of DNA methylation markers specific for cell-free DNA (cfDNA) from patients with colorectal cancer (CRC). By comparing DNA methylomes between CRC and normal mucosal tissues or blood leukocytes, we identified eight cancer-specific methylated loci (ADGRB1, ANKRD13, FAM123A, GLI3, PCDHG, PPP1R16B, SLIT3, and TMEM90B) and developed a five-marker panel (FAM123A, GLI3, PPP1R16B, SLIT3, and TMEM90B) that detected CRC in liquid biopsies with a high sensitivity and specificity with a droplet digital MethyLight assay. In a set of cfDNA samples from CRC patients (n = 117) and healthy volunteers (n = 60), a panel of five markers on the platform of the droplet digital MethyLight assay detected stages I–III and stage IV CRCs with sensitivities of 45.9% and 95.7%, respectively, and a specificity of 95.0%. The number of detected markers was correlated with the cancer stage, perineural invasion, lymphatic emboli, and venous invasion. Our five-marker panel with the droplet digital MethyLight assay showed a high sensitivity and specificity for the detection of CRC with cfDNA samples from patients with metastatic CRC.

scholarly journals RADT-23. IMPACT OF REIRRADIATION UTILIZING STEREOTACTIC RADIOSURGERY ON RECURRENT GLIOBLASTOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii186-ii186
Author(s):  
O’Dell Patrick ◽  
H Nickols ◽  
R LaRocca ◽  
K Sinicrope ◽  
D Sun ◽  
...  
Keyword(s):  
Stereotactic Radiosurgery ◽  
Median Survival ◽  
High Performance ◽  
Performance Status ◽  
Treatment Options ◽  
Methylation Status ◽  
Initial Therapy ◽  
Recurrent Glioblastoma ◽  
Control Group ◽  
The Impact

Abstract BACKGROUND Patients who have recurrent glioblastoma have limited treatment options. We conducted a retrospective review of patients with recurrent glioblastoma treated with standard initial radiation and temozolomide with tumor treating fields to investigate whether reirradiation using radiosurgery would be associated with improved outcomes. METHODS We reviewed the records of 54 consecutively treated patients with recurrent glioblastoma with ECOG 0 or 1 at recurrence and conducted Kaplan-Meier analysis with Log-rank testing to determine significance between groups. RESULTS We identified 24 patients who were treated without radiation therapy (control) while 30 patients underwent re-irradiation using radiosurgery (ReSRS) with a median total dose of 25Gy in five fractions. All patients had completed standard initial therapy, and there was no difference in the time to recurrence between the two groups (10 months for control, 15 months for ReSRS, [P = 0.17, HR for progression 0.65 (95% CI 0.38-1.13)]. A larger proportion of patients in the control arm (54%) had subtotal or gross total resection of the recurrence compared with the ReSRS group (44%, P < 0.05). The majority of patients had recurrence confirmed with biopsy (18/22 in control group, 25/31 in the ReSRS group). MGMT methylation status did not differ between control vs ReSRS (29% vs. 27%). ReSRS was associated with improved median survival from the time of first recurrence of 11.6 months versus 3.8 months in the control arm [P< 0.0001, HR for death 0.33 (95% CI 0.18-0.6)]. CONCLUSIONS In a group of patients with high performance status diagnosed with recurrent glioblastoma, reirradiation with stereotactic radiosurgery was associated with nearly one year median survival after recurrence. Additional analyses are warranted to determine the impact of concurrent systemic therapies with irradiation and underlying tumor or patient factors to predict outcomes.

Investigating the total antioxidant capacity of gluten-free grains with miniaturized near-infrared spectrometer

NIR news ◽  
2019 ◽  
Vol 30 (5-6) ◽  
pp. 35-38
Author(s):  
Verena Wiedemair ◽  
Christian Wolfgang Huck
Keyword(s):  
Antioxidant Capacity ◽  
Total Antioxidant Capacity ◽  
High Performance ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Test Set ◽  
Total Antioxidant ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Infrared Instruments

The use of ever smaller near-infrared instruments is becoming more and more prevalent, since they are cheaper, more versatile and often advertised as high-performance spectrometer. The last claim is rarely verified by independent researchers, which is why the presented work evaluates the performance of three hand-held spectrometers in comparison to a benchtop instrument. Seventy-seven samples comprising buckwheat, millet and oat were investigated for their total antioxidant capacity using Folin–Ciocalteu and near-infrared spectroscopy. Partial least squares regression models were established using cross- and test set validation. Results showed that all instruments were able to predict total antioxidant capacity to some extent. The coefficients of determinations ranged from 0.823 to 0.951 for cross-validated and from 0.849 to 0.952 for test set validated models. Errors for cross-validated models ranged from 1.11 to 2.08 mgGAE/g and for test set validated models from 1.02 to 1.86 mgGAE/g.

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