In the biological field, having a visual and interactive representation of data is useful, particularly when there is a need to investigate a large amount of multilevel data. It is advantageous to communicate this knowledge intuitively because it helps the users to see the dynamic structure in which the correct connections are interacting and extrapolated. In this work, we propose a human-interaction system to view similarity data based on the functions of the Gene Ontology (Cellular Component, Molecular Function, and Biological Process) for Alzheimer's and Parkinson's disease proteins/genes. The similarity data was built with the Lin and Wang measures for all three areas of gene ontology. We clustered data with the K-means algorithm and then we have suggested a dynamic and interactive view based on SigmaJS with the aim of allowing customization in the interactive mode of the analysis workflow by users. In this way we have obtained a more immediate visualization to capture the most relevant information within the three vocabularies of Gene Ontology. This facilitates to obtain an omic view and the possibility of carrying out a multilevel analysis with more details which is much more useful in order to better understand the knowledge of the end user.

In the biological field, having a visual and interactive representation of data is useful, particularly when there is a need to investigate a large amount of multilevel data. It is advantageous to communicate this knowledge intuitively because it helps the users to perceive the dynamic structure in which the correct connections are present and can be extrapolated. In this work, we propose a human-interaction system to view similarity data based on the functions of the Gene Ontology (Cellular Component, Molecular Function, and Biological Process) of the proteins/genes for Alzheimer disease and Parkinson disease. The similarity data was built with the Lin and Wang measures for all three areas of Gene Ontology. We clustered data with the K-means algorithm in order to demonstrate how information derived from data can only be partial when using traditional display methods. Then, we have suggested a dynamic and interactive view based on SigmaJS with the aim of allowing customization in the interactive mode of the analysis workflow by users. To this aim, we have developed a first prototype to obtained a more immediate visualization to capture the most relevant information within the three vocabularies of Gene Ontology. This facilitates the creation of an omic view and the ability to perform a multilevel analysis with more details which is much more valuable for the understanding of knowledge by the end users.

In this paper, we suggest SENECA, a tool that attempts to assist students who follow remote classes in maintaining/capturing attention, allowing them to focus on context-driven learning. Distance education has a number of disadvantages, including a lack of physical interaction between students and teachers, emotional and motivational isolation as a result of this strategy, and a reduction in active engagement. All of these things have an impact on student learning abilities. The largest distractions at home are considered among these disadvantages of distant education, particularly for subjects with low awareness. These distractions cause a movement of the student's attention from the current lesson to disturbing events. For this reason, there is a need to experiment with new solutions also linked to Information Technology (IT) to improve the focused learning during distance education. Our tool's technical idea is to create a real-time summary of the topic treated by the teacher. The system captures the text every five minutes, generates outlines, and browses them to eliminate repetitive portions after each survey. We looked at two different sorts of filters, semantic and summary, to see if the first could distinguish between topics and the second could evaluate the topic's highlights. Natural Language Processing algorithms are used to extract categories and keywords from the general generated summary. The latter will emphasize the most important points of the speech, while the keywords will be utilized to extract the candidate literature about the discussed topics.

In this work, we proposed a tool named SENECA that aims to help the students who follow remote lessons to maintain/capture attention, allowing them to focus on learning led by the context. Among the disadvantages of distance education, especially for subjects who lack awareness, the greatest distractions at home are counted. These distractions cause a movement of the student's attention from the current lesson to disturbing events. For this reason, there is a need to experiment with new solutions also linked to Information Technology (IT) to improve the focused learning during distance education. Our tool's technical idea is to create a real-time summary of the topic treated by the teacher. The system captures the text every five minutes, generates outlines, and scratches them and browses them to eliminate repetitive portions after each survey. On the general generated summary, Natural Language Processing techniques are applied to extract categories and keywords. The latter will show the highlights of the speech.