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BMC Bioinformatics

Xiong-Hui Zhou, Xin-Yi Chu, Gang Xue, Jiang-Hui Xiong, Hong-Yu Zhang
BACKGROUND: The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer. RESULTS: Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers...
February 18, 2019: BMC Bioinformatics
Hongli Fu, Yingxi Yang, Xiaobo Wang, Hui Wang, Yan Xu
BACKGROUND: Protein ubiquitination occurs when the ubiquitin protein binds to a target protein residue of lysine (K), and it is an important regulator of many cellular functions, such as signal transduction, cell division, and immune reactions, in eukaryotes. Experimental and clinical studies have shown that ubiquitination plays a key role in several human diseases, and recent advances in proteomic technology have spurred interest in identifying ubiquitination sites. However, most current computing tools for predicting target sites are based on small-scale data and shallow machine learning algorithms...
February 18, 2019: BMC Bioinformatics
Irina Kuznetsova, Artur Lugmayr, Stefan J Siira, Oliver Rackham, Aleksandra Filipovska
BACKGROUND: Prioritisation of gene ontology terms from differential gene expression analyses in a two-dimensional format remains a challenge with exponentially growing data volumes. Typically, gene ontology terms are represented as tree-maps that enclose all data into defined space. However, large datasets make this type of visualisation appear cluttered and busy, and often not informative as some labels are omitted due space limits, especially when published in two-dimensional (2D) figures...
February 18, 2019: BMC Bioinformatics
Arnaud Amzallag, Sridhar Ramaswamy, Cyril H Benes
BACKGROUND: Drug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools...
February 18, 2019: BMC Bioinformatics
Jake Alan Pitt, Julio R Banga
BACKGROUND: Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability...
February 15, 2019: BMC Bioinformatics
Joshua R Williams, Ruoting Yang, John L Clifford, Daniel Watson, Ross Campbell, Derese Getnet, Raina Kumar, Rasha Hammamieh, Marti Jett
BACKGROUND: Life science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questions: what collective patterns do most analytes follow, which analytes follow an identical pattern or synchronize across multiple cohorts, and how do biological functions evolve over time. Existing tools fall short of robustly answering and visualizing all three questions in a unified interface...
February 15, 2019: BMC Bioinformatics
Kaisa Liimatainen, Lauri Kananen, Leena Latonen, Pekka Ruusuvuori
BACKGROUND: Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be detected with high accuracy, but manually annotated training data is required. We propose a method for cell detection that requires annotated training data for one cell line only, and generalizes to other, unseen cell lines. RESULTS: Training a deep learning model with one cell line only can provide accurate detections for similar unseen cell lines (domains)...
February 15, 2019: BMC Bioinformatics
Jimmy F Zhang, Alex R Paciorkowski, Paul A Craig, Feng Cui
BACKGROUND: Functional characterization of single nucleotide variants (SNVs) involves two steps, the first step is to convert DNA to protein and the second step is to visualize protein sequences with their structures. As massively parallel sequencing has emerged as a leading technology in genomics, resulting in a significant increase in data volume, direct visualization of SNVs together with associated protein sequences/structures in a new user interface (UI) would be a more effective way to assess their potential effects on protein function...
February 15, 2019: BMC Bioinformatics
Giovanni Scala, Angela Serra, Veer Singh Marwah, Laura Aliisa Saarimäki, Dario Greco
BACKGROUND: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction...
February 15, 2019: BMC Bioinformatics
Hyrum D Carroll, John L Spouge, Mileidy Gonzalez
BACKGROUND: Genetic sequence database retrieval benchmarks play an essential role in evaluating the performance of sequence searching tools. To date, all phylogenetically diverse benchmarks known to the authors include only query sequences with single protein domains. Domains are the primary building blocks of protein structure and function. Independently, each domain can fulfill a single function, but most proteins (>80% in Metazoa) exist as multi-domain proteins. Multiple domain units combine in various arrangements or architectures to create different functions and are often under evolutionary pressures to yield new ones...
February 14, 2019: BMC Bioinformatics
Anghong Xiao, Zongze Wu, Shoubin Dong
BACKGROUND: The advance of next generation sequencing enables higher throughput with lower price, and as the basic of high-throughput sequencing data analysis, variant calling is widely used in disease research, clinical treatment and medicine research. However, current mainstream variant caller tools have a serious problem of computation bottlenecks, resulting in some long tail tasks when performing on large datasets. This prevents high scalability on clusters of multi-node and multi-core, and leads to long runtime and inefficient usage of computing resources...
February 14, 2019: BMC Bioinformatics
Yongzhi Yang, Ying Li, Qiao Chen, Yongshuai Sun, Zhiqiang Lu
BACKGROUND: With the availability of well-assembled genomes of a growing number of organisms, identifying the bioinformatic basis of whole genome duplication (WGD) is a growing field of genomics. The most extant software for detecting footprints of WGDs has been restricted to a well-assembled genome. However, the massive poor quality genomes and the more accessible transcriptomes have been largely ignored, and in theoretically they are also likely to contribute to detect WGD using dS based method...
February 13, 2019: BMC Bioinformatics
Sunjoo Bang, Sangjoon Son, Sooyoung Kim, Hyunjung Shin
BACKGROUND: Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression. METHODS: In this article, we propose a network based computational analysis for target gene identification for multi-target drugs. The min-cut algorithm is employed to cut all the paths from onset genes to apoptotic genes on a disease pathway...
February 13, 2019: BMC Bioinformatics
Dat Quoc Nguyen, Karin Verspoor
BACKGROUND: Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance. RESULTS: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT...
February 12, 2019: BMC Bioinformatics
Minoo Ashtiani, Payman Nickchi, Soheil Jahangiri-Tazehkand, Abdollah Safari, Mehdi Mirzaie, Mohieddin Jafari
BACKGROUND: Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark. RESULTS: Here, we provide an R package, IMMAN, as a tool for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPINs)...
February 12, 2019: BMC Bioinformatics
Amira Al-Aamri, Kamal Taha, Yousof Al-Hammadi, Maher Maalouf, Dirar Homouz
BACKGROUND: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models...
February 8, 2019: BMC Bioinformatics
Tra-My Ngo, Yik-Ying Teo
BACKGROUND: It is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. This advancement has led to the collation of TB databases such as PATRIC and ReSeqTB that possess both whole genome sequences and drug resistance phenotypes of infecting Mtb isolates. Bioinformatics tools have also been developed to predict drug resistance from whole genome sequencing (WGS) data...
February 8, 2019: BMC Bioinformatics
Jin Yao, Mark R Hurle, Matthew R Nelson, Pankaj Agarwal
BACKGROUND: Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications...
February 8, 2019: BMC Bioinformatics
Kamal Taha, Youssef Iraqi, Amira Al Aamri
BACKGROUND: A large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein p from already annotated proteins that have similar characteristics as p. Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. They extract biological molecule terms that directly describe protein functions from biomedical texts...
February 8, 2019: BMC Bioinformatics
Qiu Xiao, Jiawei Luo, Cheng Liang, Jie Cai, Guanghui Li, Buwen Cao
BACKGROUND: Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities...
February 7, 2019: BMC Bioinformatics
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