crossrefMachine Learning and Knowledge Extraction2021-09-10Cited by 23

Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were LCE2B, KNG1, IGHV7_81, TG, C6, FGB, ZNF750, CTSV, INGX, and COL4A6 for the whole set; and ARG1, MAGEA3, AKT2, IL1B, S100A7A, CLEC5A, WIF1, TREM1, DEFB1, and GAGE1 for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, n = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.

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A Novel Approach to Company Bankruptcy Prediction Using Convolutional Neural Networks and Generative Adversarial Networks

Alessia D’Ercole, Gianluigi Me

Predicting company bankruptcy is a critical task in financial risk assessment. This study introduces a novel approach using Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to enhance bankruptcy prediction accuracy. By transforming financial stateme…

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crossrefMachine Learning and Knowledge Extraction2020-01-02Cited by 11

Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models

Kaixu Yang, Tapabrata Maiti

An artificial neural network (ANN) is an automatic way of capturing linear and nonlinear correlations, spatial and other structural dependence among features. This machine performs well in many application areas such as classification and prediction from magnetic resonance imagin…

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crossrefMachine Learning and Knowledge Extraction2024-05-26Cited by 7

Fine-Tuning Artificial Neural Networks to Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan

Galiya Anarbekova, Luis Gonzaga Baca Ruiz, Akerke Akanova, Saltanat Sharipova, Nazira Ospanova

This study investigates the application of different ML methods for predicting pest outbreaks in Kazakhstan for grain crops. Comprehensive data spanning from 2005 to 2022, including pest population metrics, meteorological data, and geographical parameters, were employed to train…

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crossrefMachine Learning and Knowledge Extraction2025-09-02Cited by 2

A Novel Prediction Model for Multimodal Medical Data Based on Graph Neural Networks

Lifeng Zhang, Teng Li, Hongyan Cui, Quan Zhang, Zijie Jiang, Jiadong Li, et al.

Multimodal medical data provides a wide and real basis for disease diagnosis. Computer-aided diagnosis (CAD) powered by artificial intelligence (AI) is becoming increasingly prominent in disease diagnosis. CAD for multimodal medical data requires addressing the issues of data fus…

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crossrefMachine Learning and Knowledge Extraction2024-08-26Cited by 3

Forecasting the Right Crop Nutrients for Specific Crops Based on Collected Data Using an Artificial Neural Network (ANN)

Sairoel Amertet, Girma Gebresenbet

In farming technologies, it is difficult to properly provide the accurate crop nutrients for respective crops. For this reason, farmers are experiencing enormous problems. Although various types of machine learning (deep learning and convolutional neural networks) have been used…

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crossrefMachine Learning and Knowledge Extraction2023-10-02Cited by 32

Predicting the Long-Term Dependencies in Time Series Using Recurrent Artificial Neural Networks

Cristian Ubal, Gustavo Di-Giorgi, Javier E. Contreras-Reyes, Rodrigo Salas

Long-term dependence is an essential feature for the predictability of time series. Estimating the parameter that describes long memory is essential to describing the behavior of time series models. However, most long memory estimation methods assume that this parameter has a con…

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Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

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Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models

Fine-Tuning Artificial Neural Networks to Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan

A Novel Prediction Model for Multimodal Medical Data Based on Graph Neural Networks

Forecasting the Right Crop Nutrients for Specific Crops Based on Collected Data Using an Artificial Neural Network (ANN)

Predicting the Long-Term Dependencies in Time Series Using Recurrent Artificial Neural Networks