Structural Influence of gene networks on their inference: Analysis of C3NET

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Background The availability of large-scale high-throughput data possesses considerable challenges toward their functional analysis. For this reason gene network inference methods gained considerable interest. However, our current knowledge, especially about the influence of the structure of a gene network on its inference, is limited. Results In this paper we present a comprehensive investigation of the structural influence of gene networks on the inferential characteristics of C3NET - a recently introduced gene network inference algorithm. We employ local as well as global performance metrics in combination with an ensemble approach. The results from our numerical study for various biological and synthetic network structures and simulation conditions, also comparing C3NET with other inference algorithms, lead a multitude of theoretical and practical insights into the working behavior of C3NET. In addition, in order to facilitate the practical usage of C3NET we provide an user-friendly R package, called c3net, and describe its functionality. It is available from https://r-forge.r-project.org/projects/c3net and from the CRAN package repository. Conclusions The availability of gene network inference algorithms with known inferential properties opens a new era of large-scale screening experiments that could be equally beneficial for basic biological and biomedical research with auspicious prospects. The availability of our easy to use software package c3net may contribute to the popularization of such methods. Reviewers This article was reviewed by Lev Klebanov, Joel Bader and Yuriy Gusev.Peer Reviewe

Inferring Genome-Wide Interaction Networks

WOS: 000415155100007PubMed ID: 27896738The inference of gene regulatory networks is an important process that contributes to a better understanding of biological and biomedical problems. These networks aim to capture the causal molecular interactions of biological processes and provide valuable information about normal cell physiology. In this book chapter, we introduce GNI methods, namely C3NET, RN, ARACNE, CLR, and MRNET and describe their components and working mechanisms. We present a comparison of the performance of these algorithms using the results of our previously published studies. According to the study results, which were obtained from simulated as well as expression data sets, the inference algorithm C3NET provides consistently better results than the other widely used methods

Heuristic construction of high-rate linear block codes

We propose a new heuristic construction technique that generates all the even codes of length greater than 8 with full rank property for Hamming distance-4. The codes generated by the proposed method include the extended Hamming codes and the Reed Muller codes of distance-4. The way of obtaining the codes with higher minimum distance using the proposed method is also described. (c) 2005 Elsevier GmbH. All rights reserved

Cooperative communications with multilevel/AES-SD4-CPFSK in wireless sensor networks

In this paper, a new joint multilevel data encryption and channel coding mechanism is proposed, which is called "multilevel/advanced encryption standard-systematic distance 4-continuous phase frequency shift keying" (ML/AES-SD4-CPFSK). In the proposed scheme, we have not only taken advantage of spatial diversity gains but also optimally allocated energy and bandwidth resources among sensor nodes as well as providing high level of security and error protection for cooperative communications in wireless sensor networks. Relay protocols of cooperative communications, such as amplify-and-forward and decode-and-forward with/without adversary nodes, have been studied for 4CPFSK, 8CPFSK, and 16CPFSK of ML/AES-SD4-CPFSK. We have evaluated the error performances of multilevel AES for data encryption, multilevel SD-4 for channel coding, and various CPFSK types for modulation utilizing cooperative communications in wireless sensor networks. According to computer simulation results, significant diversity gain and coding gain have been achieved. As an example, bit error rate (BER) performance of 10(-5) value has been obtained at a signal-to-noise ratio (SNR) of -6 dB for SD-4-CPFSK scheme in a compared related journal paper, whereas in our proposed system, we have reached the same BER value at a SNR of -23 dB with amplify-and-forward with direct path signal protocol in 16-level AES, two-level SD-4 coded 16CPFSK, and at the same time, we have reached the same BER value at a SNR of -22 dB with amplify-and-forward without direct path signal protocol in 16-level AES, two-level SD-4 coded 16CPFSK

Prediction and evaluation of tropospheric ozone concentration in Istanbul using artificial neural network modeling according to time parameter

674-679In this paper, lower tropospheric ozone concentration was modeled using artificial neural networks (ANNs) according to 1 day, 3 days and 7 days time periods to determine best prediction period. In model formation, data that was taken from ozone measuring stations and Government Meteorology Works Office was daily averages of last 6 months of 2003 and first 6 months of 2004. Air pollutant parameters (6) and meteorological parameters (8) were used in ANN architecture for Anatolian and European sides of Istanbul separately. Correlation factor was determined to examine model effectiveness for each time period. Weekly average prediction model has been observed with highest correlation factor and three day’s correlation factor was higher than daily’s

FEATURE SELECTION FOR THE PREDICTION OF TROPOSPHERIC OZONE CONCENTRATION USING A WRAPPER METHOD

High concentrations of ozone (O-3) in the lower troposphere increase global warming, and thus affect climatic conditions and human health. Especially in metropolitan cities like Istanbul, ozone level approximates to security levels that may threaten human health. Therefore, there are many research efforts on building accurate ozone prediction models to develop public warning strategies. The goal of this study is to construct a tropospheric (ground) ozone prediction model and analyze the effectiveness of air pollutant and meteorological variables in ozone prediction using artificial neural networks (ANNs). The air pollutant and meteorological variables used in ANN modeling are taken from monitoring stations located in Istanbul. The effectiveness of each input feature is determined by using backward elimination method which utilizes the constructed ANN model as an evaluation function. The obtained results point out that outdoor temperature (OT) and solar irradiation (Si) are the most important input features of meteorological variables, and total hydrocarbons (THC), nitrogen dioxide (NO2) and nitric oxide (NO) are those of air pollutant variables. The subset of parameters found by backward elimination feature selection method that provides the maximum prediction accuracy is obtained with six input features which are OT, SI, NO2, THC, NO, and sulfur dioxide (SO2) for both validation and test sets

A New and Fast Approach for Antimicrobial Resistance Detection: Combination of Artificial Intelligence and Surface-Enhanced Raman Spectra

Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples, which can be used for rapid detection and identification of bacterial strains. However, distinguishing the antibiotic-resistant and susceptible bacteria by SERS spectra is challenging due to the high molecular similarity of the bacterial strains. To overcome this challenge, we proposed to use artificial intelligence (AI) methods to assist SERS-based diagnostics of AMR bacteria. We used machine learning to optimize the sampling of SERS substrates, improving the data collection efficiency and reliability. We also used deep learning to analyze the SERS spectra of bacteria. Our AI-assisted SERS strategy enables label-free spectroscopic profiling of AMR bacteria in complex clinical settings, offering a promising solution for combating the AMR threat

Impact of genetic polymorphisms on human immune cell gene expression

While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (database of immune cell expression, expression quantitative trait loci [eQTLs], and epigenomics) project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell-specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis (https://dice-database.org).</p