scholarly journals In Silico Model for Lung Cancer Prediction Based on TP53 mutations Using Neural Network

2018 ◽  
Vol 00 (1) ◽  
pp. 196-201
Author(s):  
Zahraa Naser Shahweli ◽  
◽  
Ban Nadeem Dhannoon ◽  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
In Silico ◽  
Tp53 Mutations ◽  
Cancer Prediction ◽  
In Silico Model

scholarly journals Early Lung Cancer Prediction Using Neural Network with Cross-Validation

2020 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Cross Validation ◽  
Gradient Boosting ◽  
Predictive Tool ◽  
Cancer Disease ◽  
Counter Measures ◽  
Cancer Prediction ◽  
Validation Technique ◽  
Automated Tool

Lung cancer is known as lung carcinoma. It is a disease which is malignant tumor leading to the uncontrolled cell growth in the lung tissue. Lung Cancer disease is one of the most prominent cause of death in all over world. Early detection of this disease can assist medical care unit as well as physicians to provide counter measures to the patients. The objective of this paper is to approach an automated tool that takes influential causes of lung cancer as input and detect patients with higher probabilities of being affected by this disease. A neural network classifier accompanied by cross-validation technique is proposed in this paper as a predictive tool. Later, this proposed method is compared with another baseline classifier Gradient Boosting Classifier in order to justify the prediction performance.

scholarly journals Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model

2013 ◽  
Vol 8 (2) ◽  
pp. 351-365 ◽  
Author(s):  
Anna T. Stratmann ◽  
David Fecher ◽  
Gaby Wangorsch ◽  
Claudia Göttlich ◽  
Thorsten Walles ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Biological Tissue ◽  
In Silico ◽  
Cancer Model ◽  
Model Based ◽  
In Silico Model ◽  
Lung Cancer Model ◽  
Tissue Matrix

scholarly journals Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Emmanuel Adetiba ◽  
Oludayo O. Olugbara
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Early Stage ◽  
Molecular Therapy ◽  
Training Dataset ◽  
Experimental Comparison ◽  
Support Vector ◽  
Histogram Of Oriented Gradient ◽  
Genomic Features ◽  
Cancer Prediction

This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization

2018 ◽  
Vol 32 (9) ◽  
pp. 4373-4386 ◽  
Author(s):  
Ramani Selvanambi ◽  
Jaisankar Natarajan ◽  
Marimuthu Karuppiah ◽  
SK Hafizul Islam ◽  
Mohammad Mehedi Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Recurrent Neural Network ◽  
Higher Order ◽  
Swarm Optimization ◽  
Cancer Prediction ◽  
Glowworm Swarm Optimization

Role of 4-O Galloylchlorogenic Acid in Lung Cancer- An Insilico Approach

2017 ◽  
Vol 9 (5) ◽  
Author(s):  
Jaynthy C. ◽  
N. Premjanu ◽  
Abhinav Srivastava
Keyword(s):  
Lung Cancer ◽  
Cell Proliferation ◽  
In Silico ◽  
Computational Study ◽  
Regulation Mechanism ◽  
Down Regulation ◽  
Fruit Extract ◽  
In Vitro Techniques ◽  
Discovery Studio

Cancer is a major disease with millions of patients diagnosed each year with high mortality around the world. Various studies are still going on to study the further mechanisms and pathways of the cancer cell proliferation. Fucosylation is one of the most important oligosaccharide modifications involved in cancer and inflammation. In cancer development increased core fucosylation by FUT8 play an important role in cell proliferation. Down regulation of FUT8 expression may help cure lung cancer. Therefore the computational study based on the down regulation mechanism of FUT8 was mechanised. Sapota fruit extract, containing 4-Ogalloylchlorogenic acid was used as the inhibitor against FUT-8 as target and docking was performed using in-silico tool, Accelrys Discovery Studio. There were several conformations of the docked result, and conformation 1 showed 80% dock score between the ligand and the target. Further the amino acids of the inhibitor involved in docking were studied using another tool, Ligplot. Thus, in-silico analysis based on drug designing parameters shows that the fruit extract can be studied further using in-vitro techniques to know its pharmacokinetics.

Usefulness of collaboration between mathematical models and cell engineering for elucidating complex disease mechanisms and discover effective drugs

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
H Kohjitani ◽  
A Kashiwa ◽  
T Makiyama ◽  
F Toyoda ◽  
Y Yamamoto ◽  
...  
Keyword(s):  
Mathematical Model ◽  
In Silico ◽  
Complex Disease ◽  
Ion Selectivity ◽  
Public Institution ◽  
Cell Engineering ◽  
Funding Source ◽  
Patch Clamp Technique ◽  
In Silico Model

Abstract Background A missense mutation, CACNA1C-E1115K, located in the cardiac L-type calcium channel (LTCC), was recently reported to be associated with diverse arrhythmias. Several studies reported in-vivo and in-vitro modeling of this mutation, but actual mechanism and target drug of this disease has not been clarified due to its complex ion-mechanisms. Objective To reveal the mechanism of this diverse arrhythmogenic phenotype using combination of in-vitro and in-silico model. Methods and results Cell-Engineering Phase: We generated human induced pluripotent stem cell (hiPSC) from a patient carrying heterozygous CACNA1C-E1115K and differentiated into cardiomyocytes. Spontaneous APs were recorded from spontaneously beating single cardiomyocytes by using the perforated patch-clamp technique. Mathematical-Modeling Phase: We newly developed ICaL-mutation mathematical model, fitted into experimental data, including its impaired ion selectivity. Furthermore, we installed this mathematical model into hiPSC-CM simulation model. Collaboration Phase: Mutant in-silico model showed APD prolongation and frequent early afterdepolarization (EAD), which are same as in-vitro model. In-silico model revealed this EAD was mostly related to robust late-mode of sodium current occurred by Na+ overload and suggested that mexiletine is capable of reducing arrhythmia. Afterward, we applicated mexiletine onto hiPSC-CMs mutant model and found mexiletine suppress EADs. Conclusions Precise in-silico disease model can elucidate complicated ion currents and contribute predicting result of drug-testing. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists

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