Providing SSPCO Algorithm to Construct Static Protein-Protein Interaction (PPI) Networks

Protein-Protein Inter-action Networks are dynamic in reality; i.e. Inter-actions among different proteins may be ineffective in different circumstances and times. One of the most crucial parameters in the conversion of a static network into a temporal graph is the well-tuning of transformation threshold. In this part of the article, using additional data, like gene expression data in different times and circumstances and well-known protein complexes, it is tried to determine an appropriate threshold. To accomplish this task, we transform the problem into an optimization one and then we solve it using a meta-heuristic algorithm, named Particle Swarm Optimization (SSPCO). One of the most important parts in our work is the determination of interestingness function in the SSPCO. It is defined as a function of standard complexes and gene co-expression data. After producing a threshold per each gene, in the following section we will discuss how using these thresholds, active proteins are determined and then temporal graph is created. For final assessment of the produced graph quality, we use graph clustering algorithms and protein complexes determination algorithms. For accomplishing this task, we use MCL, Cluster One, MCODE algorithms. Due to high number of the obtained clusters, the obtained results, if they have some special conditions, will filter out or be merged with each other. Standard performance criteria like Recal, Precision, and F-measure are employed. There is a new proposed criterion named Smoothness. Our experimental results show that the graphs produced by the proposed method outperform the previous methods

Molecular Evolution of Hominoid Primates: Phylogeny and Regulation

The complete mtDNA of one eastern gorilla was sequenced to provide the most accurate date for the mitochondrial divergence of gorillas. The most recent common ancestor of eastern lowland and western lowland gorillas existed about 1.9 million years ago, slightly more recent than that of chimpanzee and bonobo. This study also depicts that the eastern and western gorillas show species level genetic divergence. Hominoid mating systems differ tremendously. The level of sperm competition varies according to the mating system, which presumably imposes unique selective pressures on the seminal proteins of each species. Cartilage acidic protein 1 (CRTAC1) was identified in our lab as the protein with the largest difference in abundance between human and chimpanzee, being found at 142-fold higher in chimpanzee. The coding region of CRTAC1 is extremely conserved with signature of strong purifying selection. Paradoxically, CRTAC1 `promoter\u27 from human drives transcription significantly greater than chimpanzee, with or without androgen stimulation. Analyzing H3K27Ac data, a ~2.2kb region was identified as a possible additional cis-regulatory element. The cis-regulatory region behaved like a silencer and aided in strong transcriptional repression in humans. Although its underlying basis remains elusive, it can be speculated that the differential expression of CRTAC1 between human and chimpanzee seminal plasma results from tissue specific over/under expression of this gene. The unique gains and losses of miRNAs within hominoids have remained understudied. The overall goal of this project was to identify the uniquely gained and lost miRNAs and their targets within hominoids. I found 14 miRNAs uniquely gained in humans. Maximum uniquely gained and lost miRNAs were found to be brain specific. The targets of uniquely gained miRNAs in human are also associated with brain-associated functions. Older miRNAs were found to be more conserved compared to the newer miRNAs gained \u3c15 Mya

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

The Evolution and Mechanics of Translational Control in Plants

The expression of numerous plant mRNAs is attenuated by RNA sequence elements located in the 5\u27 and 3\u27 untranslated regions (UTRs). For example, in plants and many higher eukaryotes, roughly 35% of genes encode mRNAs that contain one or more upstream open reading frames (uORFs) in the 5\u27 UTR. For this dissertation I have analyzed the pattern of conservation of such mRNA sequence elements. In the first set of studies, I have taken a comparative transcriptomics approach to address which RNA sequence elements are conserved between various families of angiosperm plants. Such conservation indicates an element\u27s fundamental importance to plant biology, points to pathways for which it is most vital, and suggests the mechanism by which it acts. Conserved motifs were detected in 3% of genes. These include di-purine repeat motifs, uORF-associated motifs, putative binding sites for PUMILIO-like RNA binding proteins, small RNA targets, and a wide range of other sequence motifs. Due to the scanning process that precedes translation initiation, uORFs are often translated, thereby repressing initiation at the an mRNA\u27s main ORF. As one might predict, I found a clear bias against the AUG start codon within the 5\u27 untranslated region (5\u27 UTR) among all plants examined. Further supporting this finding, comparative analysis indicates that, for ~42% of genes, AUGs and their resultant uORFs reduce carrier fitness. Interestingly, for at least 5% of genes, uORFs are not only tolerated, but enriched. The remaining uORFs appear to be neutral. Because of their tangible impact on plant biology, it is critical to differentiate how uORFs affect translation and how, in many cases, their inhibitory effects are neutralized. In pursuit of this aim, I developed a computational model of the initiation process that uses five parameters to account for uORF presence. In vivo translation efficiency data from uORF-containing reporter constructs were used to estimate the model\u27s parameters in wild type Arabidopsis. In addition, the model was applied to identify salient defects associated with a mutation in the subunit h of eukaryotic initiation factor 3 (eIF3h). The model indicates that eIF3h, by supporting re-initation during uORF elongation, facilitates uORF tolerance

Assessment of monthly rain fade in the equatorial region at C & KU-band using measat-3 satellite links

C & Ku-band satellite communication links are the most commonly used for equatorial satellite communication links. Severe rainfall rate in equatorial regions can cause a large rain attenuation in real compared to the prediction. ITU-R P. 618 standards are commonly used to predict satellite rain fade in designing satellite communication network. However, the prediction of ITU-R is still found to be inaccurate hence hinder a reliable operational satellite communication link in equatorial region. This paper aims to provide an accurate insight by assessment of the monthly C & Ku-band rain fade performance by collecting data from commercial earth stations using C band and Ku-band antenna with 11 m and 13 m diameter respectively. The antennas measure the C & Ku-band beacon signal from MEASAT-3 under equatorial rain conditions. The data is collected for one year in 2015. The monthly cumulative distribution function is developed based on the 1-year data. RMSE analysis is made by comparing the monthly measured data of C-band and Ku-band to the ITU-R predictions developed based on ITU-R’s P.618, P.837, P.838 and P.839 standards. The findings show that Ku-band produces an average of 25 RMSE value while the C-band rain attenuation produces an average of 2 RMSE value. Therefore, the ITU-R model still under predicts the rain attenuation in the equatorial region and this call for revisit of the fundamental quantity in determining the rain fade for rain attenuation to be re-evaluated