Distributed Artificial Intelligence Models for Knowledge Discovery in Bioinformatics

The increased volume of existing information on biological processes and the use of large databases have significantly increased the accessibility of datasets to the scientific community. This has enabled performing an analysis to facilitate the extraction of relevant information or modeling and optimizing tasks in different processes. Parallel to the increasing volumes of information is the emergence of new or adapted distributed computing models such as grid computing and cloud computing. These management systems along with new techniques of artificial intelligence, or more specifically knowledge discovery, are making it possible to perform an analysis of the information in a more efficient manner and are enabling the creation of adaptive systems with learning ability

Bioinformatics and Medicine in the Era of Deep Learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

Bioinformatics and Medicine in the Era of Deep Learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

Effect of Non-Coding RNA on Post-Transcriptional Gene Silencing of Alzheimer Disease

A large amount of hidden biological information is contained in the human genome, which is not expressed or revealed in the form of proteins; the usual end product form of gene expression. Instead, most of such information is in the form of non-coding RNAs (ncRNAs). ncRNAs correspond to genes that are transcribed, but do not get translated into proteins. This part of the genome was, till recently, considered as ‘junk’. The term ‘junk’ implied lack of any discernible function of these RNA. More than 98% of the human genomic size encompasses these non-coding RNAs. But, recent research has evidently brought out the indispensible contribution of non-coding RNA in controlling and regulating gene expression. ncRNA such as siRNAs and microRNAs have been reported to greatly help in causing post-transcriptional gene silencing (PTGS) in cells through RNA interference (RNAi) pathway. In this work, we have investigated the possibility of using siRNAs and microRNAs to aid in gene silencing of early onset Alzheimer’s disease genes. 
Alzheimer’s disease specific mutations and their corresponding positions in mRNA have been identified for six genes; Presenilin-1, Presenilin-2, APP (amyloid beta precursor protein), APBB3, BACE-1 and PSENEN. 

Small interfering RNAs (siRNAs) that can cause PTGS through RNA interference pathway have been designed. RNA analysis has been done to verify complementarity of antisense siRNA sequence with target mRNA sequence. Interaction studies have been done computationally between these antisense siRNA strands and seven Argonaute proteins. From the interaction studies, only one of the seven Argonaute proteins; 1Q8K, was found to have interaction with the siRNAs indicating the importance and uniqueness of this particular protein in RISC (RNA induced silencing complex). 

The interaction studies have been carried out for the microRNAs also. Out of the 700 mature human microRNAs collected, 394 microRNAs have been identified to show partial complementarity with their target sequence on PSEN-1 mRNA. Of these 394, five microRNAs have shown partial complementarity to early onset Alzheimer’s disease specific mutations in PSEN-1 mRNA. Interaction studies have been done between these microRNAs and Argonaute proteins. Thus, design, characterization and analysis of ncRNAs that contribute to post transcriptional gene silencing of Alzheimer’s disease have been achieved.
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Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

Analysis of the impact degree distribution in metabolic networks using branching process approximation

Theoretical frameworks to estimate the tolerance of metabolic networks to various failures are important to evaluate the robustness of biological complex systems in systems biology. In this paper, we focus on a measure for robustness in metabolic networks, namely, the impact degree, and propose an approximation method to predict the probability distribution of impact degrees from metabolic network structures using the theory of branching process. We demonstrate the relevance of this method by testing it on real-world metabolic networks. Although the approximation method possesses a few limitations, it may be a powerful tool for evaluating metabolic robustness.Comment: 17 pages, 4 figures, 4 table