scholarly journals Corrigendum to “Artificial Neural Network to Predict Varicocele Impact on Male Fertility through Testicular Endocannabinoid Gene Expression Profiles”

2020 ◽  
Vol 2020 ◽  
pp. 1-1
Author(s):  
Davide Perruzza ◽  
Nicola Bernabò ◽  
Cinzia Rapino ◽  
Luca Valbonetti ◽  
Ilaria Falanga ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Male Fertility ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Artificial Neural

scholarly journals Artificial Neural Network to Predict Varicocele Impact on Male Fertility through Testicular Endocannabinoid Gene Expression Profiles

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Davide Perruzza ◽  
Nicola Bernabò ◽  
Cinzia Rapino ◽  
Luca Valbonetti ◽  
Ilaria Falanga ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Male Fertility ◽  
Cannabinoid Receptors ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Artificial Neural ◽  
The Relationship

The relationship between varicocele and fertility has always been a matter of debate because of the absence of predictive clinical indicators or molecular markers able to define the severity of this disease. Even though accumulated evidence demonstrated that the endocannabinoid system (ECS) plays a central role in male reproductive biology, particularly in the testicular compartment, to date no data point to a role for ECS in the etiopathogenesis of varicocele. Therefore, the present research has been designed to investigate the relationship between testicular ECS gene expression and fertility, using a validated animal model of experimental varicocele (VAR), taking advantage of traditional statistical approaches and artificial neural network (ANN). Experimental induction of VAR led to a clear reduction of spermatogenesis in left testes ranging from a mild (Johnsen score 7: 21%) to a severe (Johnsen score 4: 58%) damage of the germinal epithelium. However, the mean number of new-borns recorded after two sequential matings was quite variable and independent of the Johnsen score. While the gene expression of biosynthetic and degrading enzymes of AEA (NAPE-PLD and FAAH, respectively) and of 2-AG (DAGLα and MAGL, respectively), as well as their binding cannabinoid receptors (CB1 and CB2), did not change between testes and among groups, a significant downregulation of vanilloid (TRPV1) expression was recorded in left testes of VAR rats and positively correlated with animal fertility. Interestingly, an ANN trained by inserting the left and right testicular ECS gene expression profiles (inputs) was able to predict varicocele impact on male fertility in terms of mean number of new-borns delivered (outputs), with a very high accuracy (average prediction error of 1%). The present study provides unprecedented information on testicular ECS gene expression patterns during varicocele, by developing a freely available predictive ANN model that may open new perspectives in the diagnosis of varicocele-associated infertility.

Artificial Neural Network Analysis of HOX Gene Expression Profiles Predicts Prognostic Subgroups in Acute Myeloid Leukemia.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2318-2318
Author(s):  
Damian P.J. Finnegan ◽  
Michael F. Quinn ◽  
Mervyn Humphreys ◽  
Terence R.J. Lappin ◽  
Mary Frances McMullin ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Hox Genes ◽  
Expression Profiles ◽  
Hox Gene ◽  
Prognostic Groups ◽  
Prognostic Group ◽  
Prognosis Group ◽  
Artificial Neural

Abstract The acute myeloid leukemias (AMLs) are a heterogeneous group of hematological malignancies with diverse clinical outcomes. Pre-treatment karyotype analysis identifies biologically distinct subgroups and is currently used as a predictor of response to induction chemotherapy and risk of relapse. Cases may be stratified into one of three prognostic groups as follows: relatively favorable prognosis [t(8;21), t(15;17) and inv(16)]; adverse prognosis [−5/del(5q), −7, abnormalities of chromosome 3q and complex karyotype]; and intermediate prognosis [remainder including normal karyotype]. HOX genes encode master transcription factors which regulate key developmental processes including differentiation, proliferation and apoptosis. Humans have 39 HOX genes and multiple lines of evidence implicate their deregulated expression in the pathogenesis of AML. Drabkin et al. (Leukemia2002; 16: 186–95) have reported that AMLs with a relatively favorable prognostic karyotype are associated with low levels of HOX gene expression whereas AMLs with an adverse prognostic karyotype have higher levels of expression. To further characterize HOX gene expression in cytogenetic prognostic groups we determined the expression profiles of 26 HOX genes by real-time quantitative PCR (Q-PCR) in diagnostic samples, representative of the three prognostic groups, from 26 patients with de novo AML. Profiles were then analyzed using Artificial Neural Network based computational approaches to identify a subset of HOX genes which could discriminate between prognostic groups in a predictive fashion. Predictive models were developed for each prognostic group. Predictive classification performance for prognostic groups based on blind data of 88%, 92%, and 97% (with equal sensitivity and specificity) were achieved for the three prognostic groups. The models were interrogated to determine the nature of the relationship between the key HOX genes identified and prognostic group. The relatively favorable prognosis group was primarily defined by downregulation of HOXA5 and upregulation of HOXC4. The intermediate prognosis group was characterized by upregulation of HOXB3 and downregulation of HOXD10 and the adverse prognosis group by downregulation of both HOXC5 and HOXD3. Although the sample size is small, the results show that Artificial Neural Network based computational approaches are capable of further characterizing HOX gene expression within AML prognostic groups as determined by presenting karyotype and that measuring the expression levels of a small number of HOX genes at diagnosis can provide useful clinical information in cases where karyotype analysis has been unsuccessful.

scholarly journals Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

2020 ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

Abstract Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

Cancer classification of single-cell gene expression data by neural network

2019 ◽  
Author(s):  
Bong-Hyun Kim ◽  
Kijin Yu ◽  
Peter C W Lee
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification ◽  
Supplementary Information ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Normal Tissues

Abstract Motivation Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). Results We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples. Availability and implementation Cancer classification by neural network. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongfang Jia ◽  
Cheng Chen ◽  
Chen Chen ◽  
Fangfang Chen ◽  
Ningrui Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Gene Expression ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Gene Expression Profiles ◽  
Diagnostic Methods ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Hub Genes

Mastering the molecular mechanism of breast cancer (BC) can provide an in-depth understanding of BC pathology. This study explored existing technologies for diagnosing BC, such as mammography, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) and summarized the disadvantages of the existing cancer diagnosis. The purpose of this article is to use gene expression profiles of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to classify BC samples and normal samples. The method proposed in this article triumphs over some of the shortcomings of traditional diagnostic methods and can conduct BC diagnosis more rapidly with high sensitivity and have no radiation. This study first selected the genes most relevant to cancer through weighted gene co-expression network analysis (WGCNA) and differential expression analysis (DEA). Then it used the protein–protein interaction (PPI) network to screen 23 hub genes. Finally, it used the support vector machine (SVM), decision tree (DT), Bayesian network (BN), artificial neural network (ANN), convolutional neural network CNN-LeNet and CNN-AlexNet to process the expression levels of 23 hub genes. For gene expression profiles, the ANN model has the best performance in the classification of cancer samples. The ten-time average accuracy is 97.36% (±0.34%), the F1 value is 0.8535 (±0.0260), the sensitivity is 98.32% (±0.32%), the specificity is 89.59% (±3.53%) and the AUC is 0.99. In summary, this method effectively classifies cancer samples and normal samples and provides reasonable new ideas for the early diagnosis of cancer in the future.

scholarly journals PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expression

2019 ◽  
Author(s):  
Palloma Porto Almeida ◽  
Cristina Padre Cardoso ◽  
Leandro Martins de Freitas
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Drug Targets ◽  
Meta Analysis ◽  
Human Protein ◽  
Ductal Adenocarcinoma ◽  
Human Protein Atlas ◽  
Artificial Neural

AbstractBackgroundAlthough the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment.MethodsWe performed a gene expression microarray meta-analysis of the tumor against healthy tissues in order to identify differentially expressed genes shared among all datasets, named core-genes (CG). We confirmed the pancreatic expressed proteins of the CG through The Human Protein Atlas. The five most expressed proteins in the tumor group were selected to train an artificial neural network to classify samples.ResultsThis microarray included 110 tumor and 77 healthy samples. We identified a CG composed of 60 genes, 58 upregulated and two downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that thirteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our artificial neural network, we used the five most expressed genes (KRT19, LAMC2, MELK, MET, TOP2A). The artificial neural network model (PDAC-ANN) classified the train samples with sensitivity of 0.95, specificity of 0.9, and f1-score of 0.93. The PDAC-ANN could classify the test samples with a sensitivity of 0.97, specificity of 0.88, and f1-score 0.94.ConclusionThe gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on mRNA expression. This software can be useful in the PDAC diagnosis.

Sign in / Sign up

Export Citation Format

Share Document