Artificial intelligence in fighting cancer: A short review and trends

2017 ◽  
Vol 2 (2) ◽  
pp. 42
Author(s):  
Jovan David Rebolledo-Mendez
Keyword(s):  
Artificial Intelligence ◽  
Cancer Detection ◽  
Network Architecture ◽  
Short Review ◽  
Cancer Drug ◽  
Neural Network Architecture ◽  
Cancer Drug Discovery ◽  
Current Trends ◽  
Cancer Types ◽  
Personalized Cancer

<p>The current trends of artificial intelligence (AI) and machine learning, specifically the amazing success achieved by artificial refurbished neural network architecture deep learning (DL), algorithms used like in alpha go, could greatly benefit cancer detection, personalized cancer treatment, and cancer drug discovery. New models like DL have recently arrived and are offering a leap step into classifying cancer types and even potential drug discoveries. These targeted AI will continue emerging and will build up toward the final fight of cancer: When AI, with the help of advance sensors that will provide data, can diagnosis cancer in stage zero.</p>

scholarly journals 7-epi-Clusianone, a Multi-Targeting Natural Product with Potential Chemotherapeutic, Immune-Modulating, and Anti-Angiogenic Properties

Molecules ◽  
2019 ◽  
Vol 24 (23) ◽  
pp. 4415 ◽  
Author(s):  
Wesley F. Taylor ◽  
Maria Yanez ◽  
Sara E. Moghadam ◽  
Mahdi Moridi Farimani ◽  
Sara Soroury ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Targeted Therapies ◽  
Acquired Resistance ◽  
Cell Types ◽  
Treatment Modalities ◽  
Cancer Drug ◽  
Cancer Drug Discovery ◽  
Anticancer Properties ◽  
Immune Therapies ◽  
Cancer Types

Targeted therapies have changed the treatment of cancer, giving new hope to many patients in recent years. The shortcomings of targeted therapies including acquired resistance, limited susceptible patients, high cost, and high toxicities, have led to the necessity of combining these therapies with other targeted or chemotherapeutic treatments. Natural products are uniquely capable of synergizing with targeted and non-targeted anticancer regimens due to their ability to affect multiple cellular pathways simultaneously. Compounds which provide an additive effect to the often combined immune therapies and cytotoxic chemotherapies, are exceedingly rare. These compounds would however provide a strengthening bridge between the two treatment modalities, increasing their effectiveness and improving patient prognoses. In this study, 7-epi-clusianone was investigated for its anticancer properties. While previous studies have suggested clusianone and its conformational isomers, including 7-epi-clusianone, are chemotherapeutic, few cancer types have been demonstrated to exhibit sensitivity to these compounds and little is known about the mechanism. In this study, 7-epi-clusianone was shown to inhibit the growth of 60 cancer cell types and induce significant cell death in 25 cancer cell lines, while simultaneously modulating the immune system, inhibiting angiogenesis, and inhibiting cancer cell invasion, making it a promising lead compound for cancer drug discovery.

scholarly journals Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece

2019 ◽  
Vol 5 (8) ◽  
pp. eaaw7416 ◽  
Author(s):  
Z. Sabetsarvestani ◽  
B. Sober ◽  
C. Higgitt ◽  
I. Daubechies ◽  
M. R. D. Rodrigues
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Neural Network Architecture ◽  
Network Algorithms ◽  
X Ray ◽  
Wide Range ◽  
Challenging Tasks ◽  
Remarkable Progress

X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to “read.” To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts.

Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences

2018 ◽  
Vol 36 (12) ◽  
pp. 691-697 ◽  
Author(s):  
Tomoyuki Noguchi ◽  
Daichi Higa ◽  
Takashi Asada ◽  
Yusuke Kawata ◽  
Akihiro Machitori ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Mr Imaging ◽  
Network Architecture ◽  
Neural Network Architecture

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Sign in / Sign up

Export Citation Format

Share Document