Taysir Hassan A. Soliman

Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction

BackgroundThe gut microbiota plays an important role in human health and disease by acting as a metabolic organ. Metagenomic sequencing has shown how dysbiosis in the gut microbiota is associated with human metabolic diseases such as obesity and diabetes. Modeling may assist to gain insight into the metabolic implication of an altered microbiota. Fast and accurate reconstruction of metabolic models for members of the gut microbiota, as well as methods to simulate a community of microorganisms, are therefore needed. The Integrated Microbial Genomes (IMG) database contains functional annotation for nearly 4,650 bacterial genomes. This tremendous new genomic information adds new opportunities for systems biology to reconstruct accurate genome scale metabolic models (GEMs).ResultsHere we assembled a reaction data set containing 2,340 reactions obtained from existing genome-scale metabolic models, where each reaction is assigned with KEGG Orthology. The reaction data set was then used to reconstruct two genome scale metabolic models for gut microorganisms available in the IMG database Bifidobacterium adolescentis L2-32, which produces acetate during fermentation, and Faecalibacterium prausnitzii A2-165, which consumes acetate and produces butyrate. F. prausnitzii is less abundant in patients with Crohn’s disease and has been suggested to play an anti-inflammatory role in the gut ecosystem. The B. adolescentis model, iBif452, comprises 699 reactions and 611 unique metabolites. The F. prausnitzii model, iFap484, comprises 713 reactions and 621 unique metabolites. Each model was validated with in vivo data. We used OptCom and Flux Balance Analysis to simulate how both organisms interact.ConclusionsThe consortium of iBif452 and iFap484 was applied to predict F. prausnitzii’s demand for acetate and production of butyrate which plays an essential role in colonic homeostasis and cancer prevention. The assembled reaction set is a useful tool to generate bacterial draft models from KEGG Orthology.

A Lossless Compression Algorithm for DNA sequences

The increase of the amount of DNA sequences requires efficient computational algorithms for performing sequence comparison and analysis. Standard compression algorithms are not able to compress DNA sequences because they do not consider special characteristics of DNA sequences (i.e., DNA sequences contain several approximate repeats and complimentary palindromes). Recently, new algorithms have been proposed to compress DNA sequences, often using detection of long approximate repeats. The current work proposes a Lossless Compression Algorithm (LCA), providing a new encoding method. LCA achieves a better compression ratio than that of existing DNA-oriented compression algorithms, when compared to GenCompress, DNACompress, and DNAPack.

Systems biology analysis of hepatitis C virus infection reveals the role of copy number increases in regions of chromosome 1q in hepatocellular carcinoma metabolism

Hepatitis C virus (HCV) infection is a worldwide healthcare problem; however, traditional treatment methods have failed to cure all patients, and HCV has developed resistance to new drugs. Systems biology-based analyses could play an important role in the holistic analysis of the impact of HCV on hepatocellular metabolism. Here, we integrated HCV assembly reactions with a genome-scale hepatocyte metabolic model to identify metabolic targets for HCV assembly and metabolic alterations that occur between different HCV progression states (cirrhosis, dysplastic nodule, and early and advanced hepatocellular carcinoma (HCC)) and healthy liver tissue. We found that diacylglycerolipids were essential for HCV assembly. In addition, the metabolism of keratan sulfate and chondroitin sulfate was significantly changed in the cirrhosis stage, whereas the metabolism of acyl-carnitine was significantly changed in the dysplastic nodule and early HCC stages. Our results explained the role of the upregulated expression of BCAT1, PLOD3 and six other methyltransferase genes involved in carnitine biosynthesis and S-adenosylmethionine metabolism in the early and advanced HCC stages. Moreover, GNPAT and BCAP31 expression was upregulated in the early and advanced HCC stages and could lead to increased acyl-CoA consumption. By integrating our results with copy number variation analyses, we observed that GNPAT, PPOX and five of the methyltransferase genes (ASH1L, METTL13, SMYD2, TARBP1 and SMYD3), which are all located on chromosome 1q, had increased copy numbers in the cancer samples relative to the normal samples. Finally, we confirmed our predictions with the results of metabolomics studies and proposed that inhibiting the identified targets has the potential to provide an effective treatment strategy for HCV-associated liver disorders.

A gene selection approach for classifying diseases based on microarray datasets

Gene Selection is very important problem in the classification of serious diseases in clinical information systems. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analysis. In the current work, a hybrid approach is presented in order to classify diseases, such as colon cancer, leukemia, and liver cancer, based on informative genes. This hybrid approach uses clustering (K-means) with statistical analysis (ANOVA) as a preprocessing step for gene selection and Support Vector Machines (SVM) to classify diseases related to microarray experiments. To compare the performance of the proposed methodology, two kinds of comparisons were achieved: 1) applying statistical analysis combined with clustering algorithm (K-means) as a preprocessing step and 2) comparing different classification algorithms: decision tree (ID3), naïve bayes, adaptive naïve bayes, and support vector machines. In case of combining clustering with statistical analysis, much better classification accuracy is given of 97% rather than without applying clustering in the preprocessing phase. In addition, SVM had proven better accuracy than decision trees, Naïve Bayes, and Adaptive Naïve Bayes classification.

A hybrid Analytical Hierarchical Process and Deep Neural Networks approach for classifying breast cancer

Data mining techniques and multi criteria decision making techniques have been used widely in many areas, such as customer relationship management, medicine, engineering, education, geographic information systems, and recommendation systems. The present study aims to design a hybrid approach based on Deep Neural Networks (DNNs) and multi criteria decision making. DNNs and multi criteria decision making techniques are integrated with Analytical Hierarchy Process (AHP) to improve classification accuracy and deal with large datasets. Three different breast cancer datasets are used for evaluating the performance of the proposed hybrid approach. In most cases, the hybrid approach of applying DNN Backpropagation with three hidden layers and AHP gives better accuracy rate 84.33%, precision 95.9%, recall 86.6%, and F-measure of 90.2% than using one hidden layer or applying DNN Backpropagation with three hidden layers without AHP. In addition, classical classification algorithm are also compared: J48, naïve bayes, and random tree, where they give less results than proposed approach.

A Context-Aware Recommender System for Personalized Places in Mobile Applications

Selecting the most appropriate places under differ-ent context is important contribution nowadays for people who visit places for the first time. The aim of the work in this paper is to make a context-aware recommender system, which recom-mends places to users based on the current weather, the time of the day, and the user’s mood. This Context-aware Recommender System will determine the current weather and time of the day in a user’s location. Then, it gets places that are appropriate to context state in the user’s location. Also, Recommender system takes the current user’s mood and then selects the location that the user should go. Places are recommended based on what other users have visited in the similar context conditions. Recommender system puts rates for each place in each context for each user. The place’s rates are calculated by The Genetic algorithm, based on Gamma function. Finally,mobile application was implemented in the context-aware recommender system.

Mining disease integrated ontology

Ontology has become a very vital issue to solve important issues regarding human diseases through data integration of chemical and biological data. Mining such data discovers highly important knowledge about diseases can give an important insight to arrive to new drug targets and assist in personalized medicine. In the current paper, a mining technique for diseases is developed based on integrated ontology and association rule mining algorithm. To perform mining, the semantic web, as a knowledge representation methodology is used to integrate data. In addition, an Ontology Association Rule Mining algorithm (OARM) is developed since existing algorithms cannot be applied because of the ontology nature of data containing several types of relations. To test our performance, prostate cancer data is obtained from NCI, which is related to 279 genes and 89 genes (from prostate cancer pathway).

Mining drug targets based on microarray experiments

Life science industry has been flourished with the explosion of online Bioinformatics databases and tools, which can affect the drug discovery process and development. The integration of unstructured data, available in biomedical literature (i.e. PubMED), and structured data, existing in biological databases, by applying both text and data mining techniques is a very critical issue in order to identify selected desirable genes or drug targets. The current work describes a system, drug target integrative miner (DruTiMine), which aims at providing a service to enhance and shorten the process of drug target discovery for pharmaceutical companies and research labs concerned with drug discovery. The system applies both the capabilities of text mining and data mining techniques for drug target discovery through integration of multiple biomedical resources, related to a specific disease, and mining the association among them. Furthermore, an association rule mining algorithm, multi-basket miner, has been developed in order to discover associations of categories having multiple values.

A spatiotemporal algebra in Hadoop for moving objects

Abstract Spatiotemporal data represent the real-world objects that move in geographic space over time. The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data. This leads to the need for scalable spatiotemporal data management systems. Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory. They shall also provide a range of query processing operators that may scale out in a cloud setting. Currently, very few researches have been conducted to meet this requirement. This paper proposes a Hadoop extension with a spatiotemporal algebra. The algebra consists of moving object types added as Hadoop native types, and operators on top of them. The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data, and for operators that can be unary or binary. Both the types and operators are accessible for the MapReduce jobs. Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis. Certain queries may call more than one operator for different jobs and keep these operators running in parallel. This paper describes the design and implementation of this algebra, and evaluates it using a benchmark that is specific to moving object databases.

Detecting Twitter Users’ Opinions of Arabic Comments During Various Time Episodes via Deep Neural Network

Due to the revolution of web 2.0, the amount of opinionated data has been extremely increased, produced by online users through sharing comments, videos, pictures, reviews, news and opinions. Although Twitter is one of the most prevalent social networking, the gathered data from Twitter is highly disorganized. However, extracting useful information from tweets is considered a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. There has been a lot of work on sentiment analysis in English texts. However, the datasets and the publications of Arabic tweets analysis are still somewhat limited. In addition, one of the main important issues is that users can change their opinions on different subjects over time. In this work, two main points are discussed. First, a deep neural network (DNN) approach (back propagation algorithm) is applied to Arabic tweets to two different domains: Egyptian stock exchange and sports’ tweets. Second, DNN is implemented to detect users’ attitude in a time period of two years for each dataset (2014 and 2015) and (2012 and 2013). The datasets are manually annotated via constructing a lexicon from the two already existing ones. When DNN performance is evaluated an average value of accuracy 90.22%, precision 90.56%, recall 90.90%, and F-measure of 90.68%, when compared to other three machine learning algorithms Naive Bayes (NB), Decision Tree, and K-Nearest.