Selected papers from the 15th and 16th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics

Funding Information: CIBB 2019 was held at the Department of Human and Social Sciences of the University of Bergamo, Italy, from the 4th to the 6th of September 2019 []. The organization of this edition of CIBB was supported by the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy, and by the Institute of Biomedical Technologies of the National Research Council, Italy. Besides the papers focused on computational intelligence methods applied to open problems of bioinformatics and biostatistics, the works submitted to CIBB 2019 dealt with algebraic and computational methods to study RNA behaviour, intelligence methods for molecular characterization and dynamics in translational medicine, modeling and simulation methods for computational biology and systems medicine, and machine learning in healthcare informatics and medical biology. A supplement published in BMC Medical Informatics and Decision Making journal [] collected three revised and extended papers focused on the latter topic.publishersversionpublishe

Neural networks for the Recognition of X-ray Images of Ailments for Covid-19

This investigation analyzes the current state of neural networks, considers the available types, optimizers used for training, describes their benefits and disadvantages. The task of computer vision is defined and the answer to the question why the use of neural networks is an important task today is given. The powerful neural network from Google was proposed as an example and its algorithm is described in detail. Studies have shown how to configure models to get high performance

Young Tableaux for Gene Expressions

Young tableaux are certain tabulararrangements of integers. Alfred Young introducedthem to describe irreducible representations of thesymmetric group at the end of the 19th century. We willuse combinatorial algorithms of permutations andYoung tableaux to describe a modification of theresearch method of Ahnert et al for identifyingsignificant genes in the biological processes studied inmicroarray experiments. In the last decade, DNAmicroarrays (DNA chips) have been used to study geneexpressions in many diseases such as cancer anddiabetes. To analyze data of microarray expressioncurves of genes, Ahnert et al associated permutations tothe data points of the microarray curves. Using Monte-Carlo simulation they established boundscorresponding to various maps of permutations for anymicroarray curve’s algorithmic compressibility whichmeasures its significance in the underlying biologicalprocess. Using the Robinson- Schensted-Knuthprocedure, we will associate Young tableaux topermutations corresponding to the data points ofmicroarray curves. We will calculate the bound ofAhnert et al corresponding to the map which gives thelength of the longest increasing or decreasingsubsequence of a permutation

Up-Down Sequences of Permutations for Gene Expressions

We will describe modifications of theresearch methods of Willbrand et al and Ahnert etal for identifying significant genes in the biologicalprocesses studied in microarray experiments.Willbrand et al introduced a new method ofidentifying significant genes by analyzingprobabilities of up-down signatures of microarrayexpression curves of genes. Ahnert et al generalizedthe method of Willbrand et al and establishedvarious bounds on any microarray curve’salgorithmic compressibility which measures itssignificance in underlying biological process. Wewill compute the probabilities of up-downsignatures of microarray curves defined byWillbrand et al by using Foulkes’ method forenumeration of permutations with prescribed updownsequences and the hook length formula ofFrame et al. Moreover, we will compute the boundof Ahnert et al corresponding to the map whichgives the number of permutations with the samepattern of rises and falls for any microarraycurve’s algorithmic compressibility. It isfascinating to see that how combinatorialalgorithms of permutations and Young tableauxare useful in analyzing data of gene expressionsand identifying significant genes in biologicalprocesses

09081 Abstracts Collection -- Similarity-based learning on structures

From 15.02. to 20.02.2009, the Dagstuhl Seminar 09081 ``Similarity-based learning on structures \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

HOME-BIO (sHOtgun MEtagenomic analysis of BIOlogical entities): a specific and comprehensive pipeline for metagenomic shotgun sequencing data analysis

Background: Next-Generation-Sequencing (NGS) enables detection of microorganisms present in biological and other matrices of various origin and nature, allowing not only the identification of known phyla and strains but also the discovery of novel ones. The large amount of metagenomic shotgun data produced by NGS require comprehensive and user-friendly pipelines for data analysis, that speed up the bioinformatics steps, relieving the users from the need to manually perform complex and time-consuming tasks. Results: We describe here HOME-BIO (sHOtgun MEtagenomic analysis of BIOlogical entities), an exhaustive pipeline for metagenomics data analysis, comprising three independent analytical modules designed for an inclusive analysis of large NGS datasets. Conclusions: HOME-BIO is a powerful and easy-to-use tool that can be run also by users with limited computational expertise. It allows in-depth analyses by removing low-complexity/ problematic reads, integrating the analytical steps that lead to a comprehensive taxonomy profile of each sample by querying different source databases, and it is customizable according to specific users’ needs

TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain

BackgroundGene prioritization (gene ranking) aims to obtain the centrality of genes, which is critical for cancer diagnosis and therapy since keys genes correspond to the biomarkers or targets of drugs. Great efforts have been devoted to the gene ranking problem by exploring the similarity between candidate and known disease-causing genes. However, when the number of disease-causing genes is limited, they are not applicable largely due to the low accuracy. Actually, the number of disease-causing genes for cancers, particularly for these rare cancers, are really limited. Therefore, there is a critical needed to design effective and efficient algorithms for gene ranking with limited prior disease-causing genes.ResultsIn this study, we propose a transfer learning based algorithm for gene prioritization (called TLGP) in the cancer (target domain) without disease-causing genes by transferring knowledge from other cancers (source domain). The underlying assumption is that knowledge shared by similar cancers improves the accuracy of gene prioritization. Specifically, TLGP first quantifies the similarity between the target and source domain by calculating the affinity matrix for genes. Then, TLGP automatically learns a fusion network for the target cancer by fusing affinity matrix, pathogenic genes and genomic data of source cancers. Finally, genes in the target cancer are prioritized. The experimental results indicate that the learnt fusion network is more reliable than gene co-expression network, implying that transferring knowledge from other cancers improves the accuracy of network construction. Moreover, TLGP outperforms state-of-the-art approaches in terms of accuracy, improving at least 5%.ConclusionThe proposed model and method provide an effective and efficient strategy for gene ranking by integrating genomic data from various cancers

Analysis of selection in protein-coding sequences accounting for common biases

The evolution of protein-coding genes is usually driven by selective processes, which favor some evolutionary trajectories over others, optimizing the subsequent protein stability and activity. The analysis of selection in this type of genetic data is broadly performed with the metric nonsynonymous/synonymous substitution rate ratio (dN/dS). However, most of the well-established methodologies to estimate this metric make crucial assumptions, such as lack of recombination or invariable codon frequencies along genes, which can bias the estimation. Here, we review the most relevant biases in the dN/dS estimation and provide a detailed guide to estimate this metric using state-of-the-art procedures that account for such biases, along with illustrative practical examples and recommendations. We also discuss the traditional interpretation of the estimated dN/dS emphasizing the importance of considering complementary biological information such as the role of the observed substitutions on the stability and function of proteins. This review is oriented to help evolutionary biologists that aim to accurately estimate selection in protein-coding sequences.Agencia Estatal de Investigación | Ref. RYC-2015-18241Agencia Estatal de Investigación | Ref. IJCI-2016-29550Xunta de Galicia | Ref. ED431F 2018/08Fundação para a Ciência e a Tecnologia | Ref. SFRH/BD/143607/201

A new Bayesian piecewise linear regression model for dynamic network reconstruction

Background: Linear regression models are important tools for learning regulatory networks from gene expression time series. A conventional assumption for non-homogeneous regulatory processes on a short time scale is that the network structure stays constant across time, while the network parameters are time-dependent. The objective is then to learn the network structure along with changepoints that divide the time series into time segments. An uncoupled model learns the parameters separately for each segment, while a coupled model enforces the parameters of any segment to stay similar to those of the previous segment. In this paper, we propose a new consensus model that infers for each individual time segment whether it is coupled to (or uncoupled from) the previous segment. Results: The results show that the new consensus model is superior to the uncoupled and the coupled model, as well as superior to a recently proposed generalized coupled model. Conclusions: The newly proposed model has the uncoupled and the coupled model as limiting cases, and it is able to infer the best trade-off between them from the data