scholarly journals Machine-Learning-Based m5C Score for the Prognosis Diagnosis of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haijie Zhang ◽  
Peipei Xu ◽  
Yichang Song
Keyword(s):  
Machine Learning ◽  
Adherens Junction ◽  
Prognostic Index ◽  
Clinical Information ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Cell Substrate ◽  
Gene Data ◽  
Metastatic Malignant Tumor

Background. Osteosarcoma is a common and highly metastatic malignant tumor, and m5C RNA methylation regulates various biological processes. The purpose of this study was to explore the prognostic role of m5C in osteosarcoma using machine learning. Methods. Osteosarcoma gene data and the corresponding clinical information were downloaded from the GEO database. Machine learning methods were used to screen m5C-related genes and construct m5C scores. In addition, the clusterProfiler package was used to predict the m5C-related functional pathways. xCell and CIBERSORT were used to calculate the immune microenvironment cells. GSVA was applied to analyze different categories of m5C genes, and the correlation between the GSVA and m5C scores was evaluated. Results. Twenty m5C genes were identified, and 54 related genes were screened. The m5C score was constructed based on the PCA score. With an increase in the m5C score, the expression of m5C genes and their related genes changed. Functional analysis indicated that the focal adhesion, cell-substrate adherens junction, cell adhesion molecule binding, and E2F targets might change with the m5C score. The naive B cells and CD4+ memory T cell also changed with the m5C score. The results of the correlation analysis showed that the m5C score was significantly correlated with the reader and eraser genes. Conclusion. The m5C score might be a prognostic index for osteosarcoma.

scholarly journals Neural Network Models for Empirical Finance

2020 ◽  
Vol 13 (11) ◽  
pp. 265
Author(s):  
Hector F. Calvo-Pardo ◽  
Tullio Mancini ◽  
Jose Olmo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Asset Allocation ◽  
Ensemble Methods ◽  
Network Models ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Out Of Sample

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to alleviate the problem of overfitting. We also review other features of machine learning methods, such as the selection of hyperparameters, the role of the architecture of a deep neural network for model prediction, or the importance of using different optimization routines for model selection. The review also considers the issue of model uncertainty and presents state-of-the-art methods for constructing prediction intervals using ensemble methods, such as bootstrap and Monte Carlo dropout. These methods are illustrated in an out-of-sample empirical forecasting exercise that compares the performance of machine learning methods against conventional time series models for different financial indices. These results are confirmed in an asset allocation context.

Recent Development of Computational Predicting Bioluminescent Proteins

2020 ◽  
Vol 25 (40) ◽  
pp. 4264-4273 ◽  
Author(s):  
Dan Zhang ◽  
Zheng-Xing Guan ◽  
Zi-Mei Zhang ◽  
Shi-Hao Li ◽  
Fu-Ying Dao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Light Emission ◽  
Biotechnological Application ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Technological Advances ◽  
Living Organisms ◽  
Role Of Light

Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.

THE ROLE OF INDUCTIVE LOGIC PROGRAMMING IN SOME MACHINE LEARNING APPLICATIONS

2018 ◽  
Author(s):  
M. Awwad
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Learning Methods ◽  
Search Techniques ◽  
Machine Learning Methods ◽  
Machine Learning Applications

The purpose of this paper is to show that inductive logic programming (ILP) is still relevant in contemporary machine learning applications. We mainly emphasize three modern applications where the use of ILP approach is particularly effective comparing to other machine learning methods. These applications are precisely related to search techniques, game strategies, and user behaviours on mobile areas.

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