Artificial intelligence in molecular biology: a review and assessment

1994 ◽  
Vol 344 (1310) ◽  
pp. 353-363 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Structure Prediction ◽  
Genetic Maps ◽  
Novel Technologies ◽  
The Past ◽  
Knowledge Based ◽  
Automated Learning ◽  
Learning Techniques ◽  
Computer Scientists ◽  
Active Genes

Over the past ten years, molecular biologists and computer scientists have experimented with various computational methods developed in artificial intelligence (AI). AI research has yielded a number of novel technologies, which are typified by an emphasis on symbolic (non-numerical) programming methods aimed at problems which are not amenable to classical algorithmic solutions. Prominent examples include knowledge-based and expert systems, qualitative simulation and artificial neural networks and other automated learning techniques. These methods have been applied to problems in data analysis, construction of advanced databases and modelling of biological systems. Practical results are now being obtained, notably in the recognition of active genes in genomic sequences, the assembly of physical and genetic maps and protein structure prediction. This paper outlines the principal methods, surveys the findings to date, and identifies the promising trends and current limitations.

Artificial intellugence and knowledge based systems in molecular biology

1994 ◽  
Vol 9 (3) ◽  
pp. 287-300
Author(s):  
John Fox ◽  
Christopher J. Rawlings
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Molecular Data ◽  
Genetic Maps ◽  
Machine Learning Techniques ◽  
Knowledge Based Systems ◽  
Complex Molecules ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Computer Scientists

AbstractOver the last ten years, molecular biologists and computer scientists have experimented with various artificial intelligence techniques, notably knowledge based and expert systems, qualitative simulation, natural language processing and various machine learning techniques. These techniques have been applied to problems in molecular data analysis, construction of advanced databases and modelling of biological systems. Practical results are now being obtained, notably in the representation and recognition of genetically significant structures, the assembly of genetic maps and prediction of the structure of complex molecules such as proteins. The paper outlines the principal methods used, surveys the findings to date, and identifies promising trends and current limitations.

scholarly journals Recent Development of Artificial Intelligence Applied in Echocardiography: A Review (Preprint)

2019 ◽  
Author(s):  
Lu Liu ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Tissue Segmentation ◽  
The Past ◽  
Learning Techniques ◽  
The Future

BACKGROUND Echocardiography has a pivotal role in the diagnosis and management of cardiovascular diseases since it is real-time, cost-effective, and non-invasive. The development of artificial intelligence (AI) techniques have led to more intelligent and automatic computer-aided diagnosis (CAD) systems in echocardiography over the past few years. Automatic CAD mainly includes classification, detection of anatomical structures, tissue segmentation, and disease diagnosis, which are mainly completed by machine learning techniques and the recent developed deep learning techniques. OBJECTIVE This review aims to provide a guide for researchers and clinicians on relevant aspects of AI, machine learning, and deep learning. In addition, we review the recent applications of these methods in echocardiography and identify how echocardiography could incorporate AI in the future. METHODS This paper first summarizes the overview of machine learning and deep learning. Second, it reviews current use of AI in echocardiography by searching literature in the main databases for the past 10 years and finally discusses potential limitations and challenges in the future. RESULTS AI has showed promising improvements in analysis and interpretation of echocardiography to a new stage in the fields of standard views detection, automated analysis of chamber size and function, and assessment of cardiovascular diseases. CONCLUSIONS Compared with machine learning, deep learning methods have achieved state-of-the-art performance across different applications in echocardiography. Although there are challenges such as the required large dataset, AI can provide satisfactory results by devising various strategies. We believe AI has the potential to improve accuracy of diagnosis, reduce time consumption, and decrease the load of cardiologists.

scholarly journals Use of Artificial Intelligence/Machine Learning in Cancer Research During the COVID-19 Pandemic

2020 ◽  
Vol 5 (S1) ◽  
pp. 251-253
Author(s):  
Vineet Kumar Kamal ◽  
Dolly Kumari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Patients ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
The Past ◽  
Learning Techniques ◽  
Increased Risk ◽  
The Right

The cancer patients are more vulnerable and are at increased risk of COVID-19 and related outcomes due to their weakened immune systems, specially patients with lung cancer. Amid pandemic, the diagnosis, treatment, and care of cancer patients are very difficult and challenging due to several factors. In such situations, the latest technology in artificial intelligence (AI) or machine learning algorithms (ML) have potential to provide better diagnosis, treatments and cares of cancer patients. For example, the researches may use clinical and imaging data with machine learning techniques to make differences between coronavirus-related lung changes and those caused by immunotherapy and radiotherapy. During this pandemic, AI can be used to ensure we are getting the right patients enrolled speedily and more efficiently than the traditional, and complex ways in the past in cancer clinical trials. This is the appropriate time to go beyond the “research as usual” approach and update our research via AI and ML tools to care the cancer patients and discover new and more effective treatments.

Neurologic Diagnostics in 2035

Neurology ◽  
2021 ◽  
Vol 97 (19) ◽  
pp. 902-907 ◽  
Author(s):  
Olga Ciccarelli ◽  
Massimo Pandolfo
Keyword(s):  
Artificial Intelligence ◽  
Clinical Judgment ◽  
Clinical Skills ◽  
Assistive Technologies ◽  
Diagnostic Tools ◽  
Diagnostic Process ◽  
Neurologic Diseases ◽  
Novel Technologies ◽  
The Past ◽  
Diagnostic Applications

Innovations and advances in technologies over the past few years have yielded faster and wider diagnostic applications to patients with neurologic diseases. This article focuses on the foreseeable developments of the diagnostic tools available to the neurologist in the next 15 years. Clinical judgment is and will remain the cornerstone of the diagnostic process, assisted by novel technologies, such as artificial intelligence and machine learning. Future neurologists must be educated to develop, cultivate, and rely on their clinical skills, while becoming familiar with novel, often complex, assistive technologies.

scholarly journals Artificial intelligence in reproductive medicine

Reproduction ◽  
2019 ◽  
Vol 158 (4) ◽  
pp. R139-R154 ◽  
Author(s):  
Renjie Wang ◽  
Wei Pan ◽  
Lei Jin ◽  
Yuehan Li ◽  
Yudi Geng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Medicine ◽  
Large Datasets ◽  
Machine Learning Techniques ◽  
Future Directions ◽  
Computing Power ◽  
Implementation Phase ◽  
The Past ◽  
Learning Techniques

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

Press “A” for Artificial Intelligence in Agriculture: A Review

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Yogesh Awasthi
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Significant Role ◽  
Pattern Analysis ◽  
Cost Effective ◽  
Predictive Analysis ◽  
Accurate Information ◽  
Digital Era ◽  
Learning Techniques ◽  
Crucial Information

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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