Artificial intelligence and machine learning for protein toxicity prediction using proteomics data

2020 ◽  
Vol 96 (3) ◽  
pp. 902-920
Author(s):  
Shubham Vishnoi ◽  
Himani Matre ◽  
Prabha Garg ◽  
Shubham Kumar Pandey
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Proteomics Data ◽  
Toxicity Prediction ◽  
Protein Toxicity

scholarly journals Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

2020 ◽  
Vol 2 (12) ◽  
pp. 2000084 ◽  
Author(s):  
Ajay Vikram Singh ◽  
Daniel Rosenkranz ◽  
Mohammad Hasan Dad Ansari ◽  
Rishabh Singh ◽  
Anurag Kanase ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Material Design ◽  
Toxicity Prediction ◽  
Biomedical Material

scholarly journals Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

2020 ◽  
Vol 2 (12) ◽  
pp. 2070125
Author(s):  
Ajay Vikram Singh ◽  
Daniel Rosenkranz ◽  
Mohammad Hasan Dad Ansari ◽  
Rishabh Singh ◽  
Anurag Kanase ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Material Design ◽  
Toxicity Prediction ◽  
Biomedical Material

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Detection of Virus Using Artificial Intelligence and Machine Learning Concepts

2020 ◽  
Author(s):  
Sai Harshavardhan A.V.N ◽  
Akash Ranjan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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