1,372 results match your criteria Briefings in bioinformatics[Journal]


100 Years of evolving gene-disease complexities and scientific debutants.

Brief Bioinform 2019 Apr 11. Epub 2019 Apr 11.

Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, Farmington Ave, Farmington, CT, USA.

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. Read More

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http://dx.doi.org/10.1093/bib/bbz038DOI Listing
April 2019
2 Reads

A feature-based approach to predict hot spots in protein-DNA binding interfaces.

Brief Bioinform 2019 Apr 8. Epub 2019 Apr 8.

Institutes of Physical Science and Information Technology, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China.

DNA-binding hot spot residues of proteins are dominant and fundamental interface residues that contribute most of the binding free energy of protein-DNA interfaces. As experimental methods for identifying hot spots are expensive and time consuming, computational approaches are urgently required in predicting hot spots on a large scale. In this work, we systematically assessed a wide variety of 114 features from a combination of the protein sequence, structure, network and solvent accessible information and their combinations along with various feature selection strategies for hot spot prediction. Read More

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http://dx.doi.org/10.1093/bib/bbz037DOI Listing

MicroRNAs and nervous system diseases: network insights and computational challenges.

Brief Bioinform 2019 Apr 5. Epub 2019 Apr 5.

Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

The nervous system is one of the most complex biological systems, and nervous system disease (NSD) is a major cause of disability and mortality. Extensive evidence indicates that numerous dysregulated microRNAs (miRNAs) are involved in a broad spectrum of NSDs. A comprehensive review of miRNA-mediated regulatory will facilitate our understanding of miRNA dysregulation mechanisms in NSDs. Read More

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http://dx.doi.org/10.1093/bib/bbz032DOI Listing
April 2019
1 Read
9.617 Impact Factor

Identifying psychiatric disorder-associated gut microbiota using microbiota-related gene set enrichment analysis.

Brief Bioinform 2019 Apr 5. Epub 2019 Apr 5.

School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.

Psychiatric disorders are a group of complex psychological syndromes with high prevalence. It has been reported that gut microbiota has a dominant influence on the risks of psychiatric disorders through gut microbiota-brain axis. We extended the classic gene set enrichment analysis (GSEA) approach to detect the association between gut microbiota and complex diseases using published genome-wide association study (GWAS) and GWAS of gut microbiota summary data. Read More

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http://dx.doi.org/10.1093/bib/bbz034DOI Listing
April 2019
2 Reads
9.617 Impact Factor

Molecular Biology Information Service: an innovative medical library-based bioinformatics support service for biomedical researchers.

Brief Bioinform 2019 Apr 5. Epub 2019 Apr 5.

University of Pittsburgh, Health Sciences Library System.

Biomedical researchers are increasingly reliant on obtaining bioinformatics training in order to conduct their research. Here we present a model that academic institutions may follow to provide such training for their researchers, based on the Molecular Biology Information Service (MBIS) of the Health Sciences Library System, University of Pittsburgh (Pitt). The MBIS runs a four-facet service with the following goals: (1) identify, procure and implement commercially licensed bioinformatics software, (2) teach hands-on workshops using bioinformatics tools to solve research questions, (3) provide in-person and email consultations on software/databases and (4) maintain a web portal providing overall guidance on the access and use of bioinformatics resources and MBIS-created webtools. Read More

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http://dx.doi.org/10.1093/bib/bbz035DOI Listing
April 2019
1 Read

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications.

Brief Bioinform 2019 Mar 21. Epub 2019 Mar 21.

School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. Read More

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http://dx.doi.org/10.1093/bib/bbz029DOI Listing

Degrees of freedom analysis in educational research and decision-making: leveraging qualitative data to promote excellence in bioinformatics training and education.

Brief Bioinform 2019 03;20(2):416-425

Georgetown University Medical Center, Building D, Suite, Reservoir Rd. NW, Washington, DC, USA.

Qualitative data are commonly collected in higher, graduate and postgraduate education; however, perhaps especially in the quantitative sciences, utilization of these qualitative data for decision-making can be challenging. A method for the analysis of qualitative data is the degrees of freedom analysis (DoFA), published in 1975. Given its origins in political science and its application in mainly business contexts, the DoFA method is unlikely to be discoverable or used to understand survey or other educational data obtained from teaching, training or evaluation. Read More

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http://dx.doi.org/10.1093/bib/bbx106DOI Listing
March 2019
2 Reads

Sensitivity and specificity of information criteria.

Brief Bioinform 2019 Mar 20. Epub 2019 Mar 20.

Research School of Biology at the Australian National University and a visiting researcher at the Earth Institute and School of Biology and Environmental Science, University College Dublin.

Information criteria (ICs) based on penalized likelihood, such as Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. Read More

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http://dx.doi.org/10.1093/bib/bbz016DOI Listing

Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells.

Brief Bioinform 2019 Mar 20. Epub 2019 Mar 20.

CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.

Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning-based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. Read More

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http://dx.doi.org/10.1093/bib/bbz027DOI Listing
March 2019
5 Reads

Comparing enrichment analysis and machine learning for identifying gene properties that discriminate between gene classes.

Brief Bioinform 2019 Mar 20. Epub 2019 Mar 20.

School of Computing, University of Kent, Kent, CT2 7NF, UK.

Biologists very often use enrichment methods based on statistical hypothesis tests to identify gene properties that are significantly over-represented in a given set of genes of interest, by comparison with a 'background' set of genes. These enrichment methods, although based on rigorous statistical foundations, are not always the best single option to identify patterns in biological data. In many cases, one can also use classification algorithms from the machine-learning field. Read More

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http://dx.doi.org/10.1093/bib/bbz028DOI Listing

Shaping the nebulous enhancer in the era of high-throughput assays and genome editing.

Brief Bioinform 2019 Mar 20. Epub 2019 Mar 20.

Department of Biomedical Engineering.

Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. Read More

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http://dx.doi.org/10.1093/bib/bbz030DOI Listing
March 2019
7 Reads
9.617 Impact Factor

Evaluation of drug efficacy based on the spatial position comparison of drug-target interaction centers.

Brief Bioinform 2019 Mar 13. Epub 2019 Mar 13.

School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou.

The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drug's pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs' efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug-target pairs. Read More

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http://dx.doi.org/10.1093/bib/bbz024DOI Listing
March 2019
3 Reads

Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction.

Brief Bioinform 2019 Mar 13. Epub 2019 Mar 13.

School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, China.

Anticancer drug response prediction plays an important role in personalized medicine. In particular, precisely predicting drug response in specific cancer types and patients is still a challenge problem. Here we propose Meta-GDBP, a novel anticancer drug-response model, which involves two levels. Read More

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http://dx.doi.org/10.1093/bib/bbz022DOI Listing

Optimal selection of genetic variants for adjustment of population stratification in European association studies.

Brief Bioinform 2019 Mar 13. Epub 2019 Mar 13.

Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, Germany.

Population stratification is usually corrected relying on principal component analysis (PCA) of genome-wide genotype data, even in populations considered genetically homogeneous, such as Europeans. The need to genotype only a small number of genetic variants that show large differences in allele frequency among subpopulations-so-called ancestry-informative markers (AIMs)-instead of the whole genome for stratification adjustment could represent an advantage for replication studies and candidate gene/pathway studies. Here we compare the correction performance of classical and robust principal components (PCs) with the use of AIMs selected according to four different methods: the informativeness for assignment measure ($IN$-AIMs), the combination of PCA and F-statistics, PCA-correlated measurement and the PCA weighted loadings for each genetic variant. Read More

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http://dx.doi.org/10.1093/bib/bbz023DOI Listing
March 2019
1 Read

Assessment of metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences.

Brief Bioinform 2019 Mar 11. Epub 2019 Mar 11.

Shanghai Key Lab of Intelligent Information Processing, the School of Computer Science and the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.

In metagenomic studies of microbial communities, the short reads come from mixtures of genomes. Read assembly is usually an essential first step for the follow-up studies in metagenomic research. Understanding the power and limitations of various read assembly programs in practice is important for researchers to choose which programs to use in their investigations. Read More

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http://dx.doi.org/10.1093/bib/bbz025DOI Listing
March 2019
9 Reads

MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy-defined energy.

Brief Bioinform 2019 Mar 12. Epub 2019 Mar 12.

School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.

Recursive feature elimination (RFE), as one of the most popular feature selection algorithms, has been extensively applied to bioinformatics. During the training, a group of candidate subsets are generated by iteratively eliminating the least important features from the original features. However, how to determine the optimal subset from them still remains ambiguous. Read More

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http://dx.doi.org/10.1093/bib/bbz021DOI Listing

MCAM abnormal expression and clinical outcome associations are highly cancer dependent as revealed through pan-cancer analysis.

Brief Bioinform 2019 Feb 28. Epub 2019 Feb 28.

Department of Respiratory Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China.

MCAM (CD146) is a cell surface adhesion molecule that has been reported to promote cancer development, progression and metastasis and is considered as a potential tumor biomarker and therapeutic target. However, inconsistent reports exist, and its clinical value is yet to be confirmed. Here we took advantage of several large genomic data collections (Genotype-Tissue Expression, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia) and comprehensively analyzed MCAM expression in thousands of normal and cancer samples and cell lines along with their clinical phenotypes and drug response information. Read More

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http://dx.doi.org/10.1093/bib/bbz019DOI Listing
February 2019
3 Reads
9.617 Impact Factor

New approaches for metagenome assembly with short reads.

Brief Bioinform 2019 Feb 28. Epub 2019 Feb 28.

Earlham Institute, Norwich Research Park, Norwich, UK.

In recent years, the use of longer range read data combined with advances in assembly algorithms has stimulated big improvements in the contiguity and quality of genome assemblies. However, these advances have not directly transferred to metagenomic data sets, as assumptions made by the single genome assembly algorithms do not apply when assembling multiple genomes at varying levels of abundance. The development of dedicated assemblers for metagenomic data was a relatively late innovation and for many years, researchers had to make do using tools designed for single genomes. Read More

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http://dx.doi.org/10.1093/bib/bbz020DOI Listing
February 2019
4 Reads

CAFU: a Galaxy framework for exploring unmapped RNA-Seq data.

Brief Bioinform 2019 Feb 28. Epub 2019 Feb 28.

State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.

A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. Read More

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http://dx.doi.org/10.1093/bib/bbz018DOI Listing
February 2019

Network-based methods for predicting essential genes or proteins: a survey.

Brief Bioinform 2019 Feb 18. Epub 2019 Feb 18.

School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.

Genes that are thought to be critical for the survival of organisms or cells are called essential genes. The prediction of essential genes and their products (essential proteins) is of great value in exploring the mechanism of complex diseases, the study of the minimal required genome for living cells and the development of new drug targets. As laboratory methods are often complicated, costly and time-consuming, a great many of computational methods have been proposed to identify essential genes/proteins from the perspective of the network level with the in-depth understanding of network biology and the rapid development of biotechnologies. Read More

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http://dx.doi.org/10.1093/bib/bbz017DOI Listing
February 2019
9 Reads

Delivering blended bioinformatics training in resource-limited settings: a case study on the University of Khartoum H3ABioNet node.

Brief Bioinform 2019 Feb 15. Epub 2019 Feb 15.

Center for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.

Motivation: Delivering high-quality distance-based courses in resource-limited settings is a challenging task. Besides the needed infrastructure and expertise, effective delivery of a bioinformatics course could benefit from hands-on sessions, interactivity and problem-based learning approaches.

Results: In this article, we discuss the challenges and best practices in delivering bioinformatics training in resource-limited settings taking the example of hosting and running a multiple-delivery online course, Introduction to Bioinformatics, that was developed by the H3ABioNet Education and Training working group and delivered in 27 remote classrooms across Africa in 2017. Read More

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http://dx.doi.org/10.1093/bib/bbz004DOI Listing
February 2019
2 Reads

Ensuring privacy and security of genomic data and functionalities.

Brief Bioinform 2019 Feb 12. Epub 2019 Feb 12.

Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia.

In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. Read More

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http://dx.doi.org/10.1093/bib/bbz013DOI Listing
February 2019
9.617 Impact Factor

In silico drug repositioning based on drug-miRNA associations.

Brief Bioinform 2019 Feb 11. Epub 2019 Feb 11.

Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.

Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Read More

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http://dx.doi.org/10.1093/bib/bbz012DOI Listing
February 2019
2 Reads

Computational methods for identifying the critical nodes in biological networks.

Brief Bioinform 2019 Feb 12. Epub 2019 Feb 12.

Department of Computer Science, Xiamen University, China.

A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Read More

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http://dx.doi.org/10.1093/bib/bbz011DOI Listing
February 2019
14 Reads

A comparison of deterministic and stochastic approaches for sensitivity analysis in computational systems biology.

Brief Bioinform 2019 Feb 7. Epub 2019 Feb 7.

The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy.

With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). Read More

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http://dx.doi.org/10.1093/bib/bbz014DOI Listing
February 2019
3 Reads

Performance of gene expression-based single sample predictors for assessment of clinicopathological subgroups and molecular subtypes in cancers: a case comparison study in non-small cell lung cancer.

Brief Bioinform 2019 Feb 4. Epub 2019 Feb 4.

Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Medicon Village, Lund, Sweden.

The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. Read More

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http://dx.doi.org/10.1093/bib/bbz008DOI Listing
February 2019
2 Reads

The global dissemination of bacterial infections necessitates the study of reverse genomic epidemiology.

Brief Bioinform 2019 Feb 4. Epub 2019 Feb 4.

Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Whole genome sequencing (WGS) has revolutionized the genotyping of bacterial pathogens and is expected to become the new gold standard for tracing the transmissions of bacterial infectious diseases for public health purposes. Traditional genomic epidemiology often uses WGS as a verification tool, namely, when a common source or epidemiological link is suspected, the collected isolates are sequenced for the determination of clonal relationships. However, increasingly frequent international travel and food transportation, and the associated potential for the cross-border transmission of bacterial pathogens, often lead to an absence of information on bacterial transmission routes. Read More

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http://dx.doi.org/10.1093/bib/bbz010DOI Listing
February 2019
2 Reads

Decoding competing endogenous RNA networks for cancer biomarker discovery.

Brief Bioinform 2019 Jan 30. Epub 2019 Jan 30.

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.

Crosstalk between competing endogenous RNAs (ceRNAs) is mediated by shared microRNAs (miRNAs) and plays important roles both in normal physiology and tumorigenesis; thus, it is attractive for systems-level decoding of gene regulation. As ceRNA networks link the function of miRNAs with that of transcripts sharing the same miRNA response elements (MREs), e.g. Read More

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http://dx.doi.org/10.1093/bib/bbz006DOI Listing
January 2019
2 Reads

Evaluation of ontology structural metrics based on public repository data.

Brief Bioinform 2019 Feb 4. Epub 2019 Feb 4.

Departamento de Informática y Sistemas, Universidad de Murcia, IMIB-Arrixaca, Murcia, Spain.

The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. Read More

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http://dx.doi.org/10.1093/bib/bbz009DOI Listing
February 2019
1 Read

Computational medicine: quantitative modeling of complex diseases.

Authors:
Basant K Tiwary

Brief Bioinform 2019 Jan 30. Epub 2019 Jan 30.

Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India.

Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. Read More

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http://dx.doi.org/10.1093/bib/bbz005DOI Listing
January 2019

Disentangling the complexity of low complexity proteins.

Brief Bioinform 2019 Jan 30. Epub 2019 Jan 30.

Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, Mainz, Germany.

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. Read More

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http://dx.doi.org/10.1093/bib/bbz007DOI Listing
January 2019
12 Reads
9.617 Impact Factor

Cancer Target Gene Screening: a web application for breast cancer target gene screening using multi-omics data analysis.

Brief Bioinform 2019 Jan 29. Epub 2019 Jan 29.

Department of Pathology, College of Medicine, Hanyang University, Seoul, Republic of Korea.

Breast cancer comprises several molecular subtypes with distinct clinical features and treatment responses, and a substantial portion of each subtype remains incurable. A comprehensive analysis of multi-omics data and clinical profiles is required in order to better understand the biological complexity of this cancer type and to identify new prognostic and therapeutic markers. Thus, there arises a need for useful analytical tools to assist in the investigation and clinical management of the disease. Read More

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http://dx.doi.org/10.1093/bib/bbz003DOI Listing
January 2019
6 Reads

Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs.

Brief Bioinform 2019 Jan 23. Epub 2019 Jan 23.

Lab of Innovative Drug Research and Bioinformatics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.

Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004-17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Read More

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http://dx.doi.org/10.1093/bib/bby130DOI Listing
January 2019
7 Reads
9.617 Impact Factor

Advanced bioinformatics methods for practical applications in proteomics.

Brief Bioinform 2019 01;20(1):347-355

National University of Singapore.

Mass spectrometry (MS)-based proteomics has undergone rapid advancements in recent years, creating challenging problems for bioinformatics. We focus on four aspects where bioinformatics plays a crucial role (and proteomics is needed for clinical application): peptide-spectra matching (PSM) based on the new data-independent acquisition (DIA) paradigm, resolving missing proteins (MPs), dealing with biological and technical heterogeneity in data and statistical feature selection (SFS). DIA is a brute-force strategy that provides greater width and depth but, because it indiscriminately captures spectra such that signal from multiple peptides is mixed, getting good PSMs is difficult. Read More

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http://dx.doi.org/10.1093/bib/bbx128DOI Listing
January 2019
5 Reads

A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction.

Brief Bioinform 2019 01;20(1):330-346

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China.

Intrinsically disordered proteins and regions are widely distributed in proteins, which are associated with many biological processes and diseases. Accurate prediction of intrinsically disordered proteins and regions is critical for both basic research (such as protein structure and function prediction) and practical applications (such as drug development). During the past decades, many computational approaches have been proposed, which have greatly facilitated the development of this important field. Read More

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http://dx.doi.org/10.1093/bib/bbx126DOI Listing
January 2019
25 Reads

Principal component analysis of binary genomics data.

Brief Bioinform 2019 01;20(1):317-329

Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.

Motivation: Genome-wide measurements of genetic and epigenetic alterations are generating more and more high-dimensional binary data. The special mathematical characteristics of binary data make the direct use of the classical principal component analysis (PCA) model to explore low-dimensional structures less obvious. Although there are several PCA alternatives for binary data in the psychometric, data analysis and machine learning literature, they are not well known to the bioinformatics community. Read More

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http://dx.doi.org/10.1093/bib/bbx119DOI Listing
January 2019
11 Reads

Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes.

Brief Bioinform 2019 Jan 17. Epub 2019 Jan 17.

Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA.

Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. Read More

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http://dx.doi.org/10.1093/bib/bby129DOI Listing
January 2019
1 Read

miR+Pathway: the integration and visualization of miRNA and KEGG pathways.

Brief Bioinform 2019 Jan 16. Epub 2019 Jan 16.

Ministry of Agriculture Key Lab of Agricultural Entomology, Institute of Insect Sciences, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.

miRNAs represent a type of noncoding small molecule RNA. Many studies have shown that miRNAs are widely involved in the regulation of various pathways. The key to fully understanding the regulatory function of miRNAs is the determination of the pathways in which the miRNAs participate. Read More

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http://dx.doi.org/10.1093/bib/bby128DOI Listing
January 2019
25 Reads

Corrigendum to: Dating admixture events is unsolved problem in multi-way admixed populations.

Authors:

Brief Bioinform 2019 Jan 14. Epub 2019 Jan 14.

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http://dx.doi.org/10.1093/bib/bbz001DOI Listing
January 2019
1 Read

Structure-based prediction of post-translational modification cross-talk within proteins using complementary residue- and residue pair-based features.

Brief Bioinform 2019 Jan 11. Epub 2019 Jan 11.

Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.

Post-translational modification (PTM)-based regulation can be mediated not only by the modification of a single residue but also by the interplay of different modifications. Accurate prediction of PTM cross-talk is a highly challenging issue and is in its infant stage. Especially, less attention has been paid to the structural preferences (except intrinsic disorder and spatial proximity) of cross-talk pairs and the characteristics of individual residues involved in cross-talk, which may restrict the improvement of the prediction accuracy. Read More

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http://dx.doi.org/10.1093/bib/bby123DOI Listing
January 2019
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ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.

Brief Bioinform 2019 Jan 15. Epub 2019 Jan 15.

College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. Read More

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https://academic.oup.com/bib/advance-article/doi/10.1093/bib
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http://dx.doi.org/10.1093/bib/bby127DOI Listing
January 2019
8 Reads

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools.

Brief Bioinform 2019 Jan 10. Epub 2019 Jan 10.

College of Intelligence and Computing, Tianjin University, Tianjin, China.

Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. Read More

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http://dx.doi.org/10.1093/bib/bby124DOI Listing
January 2019
2 Reads

Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.

Brief Bioinform 2019 Jan 11. Epub 2019 Jan 11.

Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston.

Cancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied extensively for cancer classification as well as prognosis using large genomic and other data over the past decade. This article provides a comprehensive review of various graph- and rule-based machine learning algorithms that have been applied to numerous genomics data to determine the cancer-specific gene modules, identify gene signature-based classifiers and carry out other related objectives of potential therapeutic value. Read More

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http://dx.doi.org/10.1093/bib/bby120DOI Listing
January 2019
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Investigating the role of Simpson's paradox in the analysis of top-ranked features in high-dimensional bioinformatics datasets.

Authors:
Alex A Freitas

Brief Bioinform 2019 Jan 9. Epub 2019 Jan 9.

University of Kent, Kent, UK.

An important problem in bioinformatics consists of identifying the most important features (or predictors), among a large number of features in a given classification dataset. This problem is often addressed by using a machine learning-based feature ranking method to identify a small set of top-ranked predictors (i.e. Read More

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http://dx.doi.org/10.1093/bib/bby126DOI Listing
January 2019
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Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy.

Brief Bioinform 2018 Dec 27. Epub 2018 Dec 27.

Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.

Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Read More

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http://dx.doi.org/10.1093/bib/bby122DOI Listing
December 2018
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Homeolog expression quantification methods for allopolyploids.

Brief Bioinform 2018 12 27. Epub 2018 Dec 27.

Artificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan.

Genome duplication with hybridization, or allopolyploidization, occurs in animals, fungi and plants, and is especially common in crop plants. There is an increasing interest in the study of allopolyploids because of advances in polyploid genome assembly; however, the high level of sequence similarity in duplicated gene copies (homeologs) poses many challenges. Here we compared standard RNA-seq expression quantification approaches used currently for diploid species against subgenome-classification approaches which maps reads to each subgenome separately. Read More

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http://dx.doi.org/10.1093/bib/bby121DOI Listing
December 2018
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Fuzzy Petri nets for modelling of uncertain biological systems.

Brief Bioinform 2018 Dec 27. Epub 2018 Dec 27.

Department of Computer Science, Brunel University London, Middlesex, UK.

The modelling of biological systems is accompanied with epistemic uncertainties that range from structural uncertainty to parametric uncertainty due to such limitations as insufficient understanding of the underlying mechanism and incomplete measurement data of a system. Fuzzy logic approaches such as fuzzy Petri nets (FPNs) are effective in addressing these issues. In this paper, we review FPNs that have been used for modelling uncertain biological systems, which we classify in three categories: basic fuzzy Petri nets, fuzzy quantitative Petri nets and Petri nets with fuzzy kinetic parameters. Read More

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http://dx.doi.org/10.1093/bib/bby118DOI Listing
December 2018
2 Reads

Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing.

Brief Bioinform 2018 Dec 18. Epub 2018 Dec 18.

Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.

Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications. Read More

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http://dx.doi.org/10.1093/bib/bby119DOI Listing
December 2018
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Network embedding in biomedical data science.

Brief Bioinform 2018 Dec 10. Epub 2018 Dec 10.

Department of Healthcare Policy and Research, Weill Cornell Medicine at Cornell University, New York, NY, USA.

Owning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. Read More

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http://dx.doi.org/10.1093/bib/bby117DOI Listing
December 2018
4 Reads