Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results

Hierarchical analysis and multi-scale modelling of rat cortical and trabecular bone

The aim of this study was to explore the hierarchical arrangement of structural properties in cortical and trabecular bone and to determine a mathematical model that accurately predicts the tissue's mechanical properties as a function of these indices. By using a variety of analytical techniques, we were able to characterize the structural and compositional properties of cortical and trabecular bones, as well as to determine the suitable mathematical model to predict the tissue's mechanical properties using a continuum micromechanics approach. Our hierarchical analysis demonstrated that the differences between cortical and trabecular bone reside mainly at the micro- and ultrastructural levels. By gaining a better appreciation of the similarities and differences between the two bone types, we would be able to provide a better assessment and understanding of their individual roles, as well as their contribution to bone health overall

The energy and economic target optimization of a naphtha production unit by implementing energy pinch technology

In this paper, energy pinch analysis is implemented on the Naphtha Production Unit as an operating process plant. It endeavors to approach the current system to the maximum energy recovery (MER) design by providing several scenarios in the heat exchangers network. Furthermore, the economic analysis considering the initial investment and operating costs of the system was conducted in each scenarios to achieve a cost-effective system. The results obtained just from the pinch analysis optimization technique, indicate that reducing the heating and cooling loads do not reduce the total costs. Therefore, two objective optimization of minimization of the levelized cost of product (LCOP) as well as maximization of the overall energy performance of the system are concerned to find out the optimal scenario of the energy network layout. Here, retrofit of the maximum energy recovery design is performed to represent the energetical and economical optimal layout. Keywords: Pinch analysis, Maximum energy recovery, Levelized cost of energy, Energy optimizatio

Artificial intelligence and machine learning in energy systems: A bibliographic perspective

Economic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems, such a feat is by no means easy. Here is where computer-aided decision-making can be very game-changing in determining the optimum point for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans' learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential areas in many different research fields. Naturally, the energy section is one area where artificial intelligence and machine learning can be very beneficial. This paper uses the VOSviewer software to investigate and review the usage of artificial intelligence and machine learning in the energy field and proposes promising yet neglected or unexplored areas in which these concepts can be used. To achieve this, the 2000 most recent papers in addition to the 2000 most cited ones in different energy-related keywords were studied and their relationship to AI- and ML-related keywords was visualized. The results revealed different research trends in recent years from the basic to more cutting-edge topics and revealed many promising areas that are yet to be explored. Results also showed that from the commercial aspect, patents submitted for artificial intelligence and machine learning in energy-related areas had a sharp increase