scholarly journals Open Data to Support CANCER Science—A Bioinformatics Perspective on Glioma Research

Onco ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 219-229
Author(s):  
Fleur Jeanquartier ◽  
Claire Jean-Quartier ◽  
Sarah Stryeck ◽  
Andreas Holzinger
Keyword(s):  
Artificial Intelligence ◽  
Cancer Research ◽  
Targeted Therapies ◽  
State Of The Art ◽  
Open Data ◽  
Signal Transduction Pathways ◽  
Cell Level ◽  
Classification Analysis ◽  
Current State ◽  
Novel Biomarkers

Supporting data sharing is paramount to making progress in cancer research. This includes the search for more precise targeted therapies and the search for novel biomarkers, through cluster and classification analysis, and extends to learning details in signal transduction pathways or intra- and intercellular interactions in cancer, through network analysis and network simulation. Our work aims to support and promote the use of publicly available resources in cancer research and demonstrates artificial intelligence (AI) methods to find answers to detailed questions. For example, how targeted therapies can be developed based on precision medicine or how to investigate cell-level phenomena with the help of bioinformatical methods. In our paper, we illustrate the current state of the art with examples from glioma research, in particular, how open data can be used for cancer research in general, and point out several resources and tools that are readily available. Presently, cancer researchers are often not aware of these important resources.

scholarly journals Precision Agriculture Technology: A Literature Review

2020 ◽  
pp. 30-34
Author(s):  
C. A. Danbaki ◽  
N. C. Onyemachi ◽  
D. S. M. Gado ◽  
G. S. Mohammed ◽  
D. Agbenu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Irrigation Management ◽  
Conventional Farming ◽  
Livestock Farming ◽  
Current State ◽  
Art Methods ◽  
Increase In Productivity

This study is a survey on state-of-the-art methods based on artificial intelligence and image processing for precision agriculture on Crop Management, Pest and Disease Management, Soil and Irrigation Management, Livestock Farming and the challenges it presents. Precision agriculture (PA) described as applying current technologies into conventional farming methods. These methods have proved to be highly efficient, sustainable and profitable to the farmer hence boosting the economy. This study is a survey on the current state of the art methods applied to precision agriculture. The application of precision agriculture is expected to yield an increase in productivity which ultimately ends in profit to the farmer, to the society increase sustainability and also improve the economy.

Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20)

2021 ◽  
Vol 46 (2) ◽  
pp. 28-29
Author(s):  
Benoît Vanderose ◽  
Julie Henry ◽  
Benoît Frénay ◽  
Xavier Devroey
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Engineering Education ◽  
State Of The Art ◽  
Future Directions ◽  
The Past ◽  
Software Engineering Education ◽  
Current State ◽  
Amount Of Knowledge ◽  
Software Engineering Tools

In the past years, with the development and widespread of digi- tal technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop series addresses that challenge by look- ing at software engineering, education, and arti cial intelligence research elds to explore how they can be combined. Speci cally, this workshop brings together researchers, teachers, and practi- tioners who use advanced software engineering tools and arti cial intelligence techniques in the education eld and through a trans- generational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions. More information at https://easeai.github.io.

scholarly journals State-of-the-Art of Artificial Intelligence

2021 ◽  
Author(s):  
Ramjee Prasad ◽  
Purva Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
State Of The Art ◽  
Turing Test ◽  
Rapid Progress ◽  
Current State ◽  
Recent Developments ◽  
Historical Developments ◽  
Insight Into ◽  
Made In

Artificial Intelligence (AI) as a technology has existed for less than a century. In spite of this, it has managed to achieve great strides. The rapid progress made in this field has aroused the curiosity of many technologists around the globe and many companies across various domains are curious to explore its potential. For a field that has achieved so much in such a short duration, it is imperative that people who aim to work in Artificial Intelligence, study its origins, recent developments, and future possibilities of expansion to gain a better insight into the field. This paper encapsulates the notable progress made in Artificial Intelligence starting from its conceptualization to its current state and future possibilities, in various fields. It covers concepts like a Turing machine, Turing test, historical developments in Artificial Intelligence, expert systems, big data, robotics, current developments in Artificial Intelligence across various fields, and future possibilities of exploration.

scholarly journals Prediction of Peptide Vascularization Inhibitory Activity in Tumor Tissue as a Possible Target for Cancer Treatment

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 15
Author(s):  
Jose Liñares-Blanco ◽  
Carlos Fernandez-Lozano
Keyword(s):  
Machine Learning ◽  
Cancer Research ◽  
Cancer Treatment ◽  
Tumor Tissue ◽  
Molecular Descriptors ◽  
State Of The Art ◽  
Selection Process ◽  
Angiogenic Activity ◽  
Current State ◽  
Metabolic Activities

The prediction of metabolic activities in silico form is crucial to be able to address all research possibilities without exceeding the experimental costs. In particular, for cancer research, the prediction of certain activities can be of great help in the discovery of different treatments. In this work it has been proposed to predict, through Machine Learning, the anti-angiogenic activity of peptides is currently being used in cancer treatment and is giving hopeful results. From a list of peptide sequences, three types of molecular descriptors were obtained (AAC, DC and TC) that offered the possibility of training different ML algorithms. After a Feature Selection process, different models were obtained with a predictive value that surpassed the current state of the art. These results shown that ML is useful for the classification and prediction of the activity of new peptides, making experimental screening cheaper and faster.

On the Current State of Linked Open Data

2018 ◽  
Vol 14 (4) ◽  
pp. 110-128 ◽  
Author(s):  
Nosheen Fayyaz ◽  
Irfan Ullah ◽  
Shah Khusro
Keyword(s):  
Semantic Web ◽  
Semantic Information ◽  
State Of The Art ◽  
Research Work ◽  
Life Sciences ◽  
Open Data ◽  
Linked Open Data ◽  
Research Opportunities ◽  
Current State ◽  
Fast Pace

This article describes how Linked Open Data (LOD), under the umbrella of the Semantic Web, integrates the openly-published semantic information making it easily understandable and consumable by humans and machines. Currently, researchers have applied the principles of LOD in several domains including e-government, media, publications, geography, and life sciences. Besides the fast pace of research, the field is still an emerging one, where researchers face several prominent challenges and issues that need to resolve to exploit LOD to its fullest. In this article, the authors have identified challenges, issues, and research opportunities in the publishing, management, linking, and consumption of LOD. The research work presented here will grab the attention of researchers and may aid to the current state-of-the-art in this area.

Considering Students' Emotions in Computer-Mediated Learning Environments

2006 ◽  
pp. 122-138 ◽  
Author(s):  
Patrícia A. Jaques ◽  
Rosa M. Viccari
Keyword(s):  
Artificial Intelligence ◽  
Human Computer Interaction ◽  
Learning Environments ◽  
State Of The Art ◽  
Learning Systems ◽  
New Work ◽  
Mediated Learning ◽  
Current State ◽  
Computer Mediated ◽  
E Learning

This text aims to present the current state of the art of the e-learning systems that consider the student’s affect. It presents the perspectives adopted by researchers for the solution of problems (for example, which kind of tools we might use to recognize users’ emotions) and also some better-known works in order to exemplify. It also describes the necessary background to understand these studies, including some concepts in the fields of Artificial Intelligence, Computers in Education, and Human-Computer Interaction, and a brief introduction on the main theories about emotion. The authors conclude the chapter by presenting challenges and the main difficulties of research in affectivity in e-learning systems and ideas on some new work on the matter.

scholarly journals A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19

2021 ◽  
Vol 10 (9) ◽  
pp. 1961
Author(s):  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
Belal Alsinglawi ◽  
Ming Chin Lin ◽  
Min-Huei Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Digital Images ◽  
State Of The Art ◽  
Laboratory Data ◽  
Drug Repurposing ◽  
Current State ◽  
Immense Potential

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI’s role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges.

scholarly journals Learning Single-Cell Perturbation Responses using Neural Optimal Transport

2021 ◽  
Author(s):  
Charlotte Bunne ◽  
Stefan G Stark ◽  
Gabriele Gut ◽  
Jacobo Sarabia del Castillo ◽  
Kjong-Van Lehmann ◽  
...  
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
State Of The Art ◽  
Cell Level ◽  
Cellular Behavior ◽  
External Perturbations ◽  
Current State ◽  
Treatment Responses ◽  
Cell Data ◽  
Resolution Single

Understanding and predicting molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CellOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CellOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

scholarly journals A survey of 25 years of evaluation

2019 ◽  
Vol 25 (06) ◽  
pp. 753-767
Author(s):  
Kenneth Ward Church ◽  
Joel Hestness
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Graduate Student ◽  
State Of The Art ◽  
Reliability And Validity ◽  
Turing Test ◽  
First Principle ◽  
Current State ◽  
Language And Vision ◽  
Better Than

AbstractEvaluation was not a thing when the first author was a graduate student in the late 1970s. There was an Artificial Intelligence (AI) boom then, but that boom was quickly followed by a bust and a long AI Winter. Charles Wayne restarted funding in the mid-1980s by emphasizing evaluation. No other sort of program could have been funded at the time, at least in America. His program was so successful that these days, shared tasks and leaderboards have become common place in speech and language (and Vision and Machine Learning). It is hard to remember that evaluation was a tough sell 25 years ago. That said, we may be a bit too satisfied with current state of the art. This paper will survey considerations from other fields such as reliability and validity from psychology and generalization from systems. There has been a trend for publications to report better and better numbers, but what do these numbers mean? Sometimes the numbers are too good to be true, and sometimes the truth is better than the numbers. It is one thing for an evaluation to fail to find a difference between man and machine, and quite another thing to pass the Turing Test. As Feynman said, “the first principle is that you must not fool yourself–and you are the easiest person to fool.”

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