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PubMed Journal Database | IEEE/ACM transactions on computational biology and bioinformatics RSS

01:09 EDT 20th June 2019 | BioPortfolio

The US National Library of Medicine and National Institutes of Health manage PubMed.gov which comprises of more than 29 million records, papers, reports for biomedical literature, including MEDLINE, life science and medical journals, articles, reviews, reports and  books.

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For example view all recent relevant publications on Epigenetics and associated publications and clincial trials.

Showing PubMed Articles 1–25 of 294 from IEEE/ACM transactions on computational biology and bioinformatics

Bayesian inference in Y-linked two-sex branching processes with mutations: ABC approach.

A Y-linked two-sex branching process with mutations and blind choice of males is a suitable model for analysing the evolution of the number of carriers of a Y-linked allele and its mutations. Such a model considers a two-sex monogamous population in which each female chooses her partner from among the male population without caring about his type (i.e., the allele he carries). In this work, we deal with the problem of estimating the main parameters of these models by developing Bayesian inference in a param...

Object weighting: a new clustering approach to deal with outliers and cluster overlap in computational biology.

Considerable efforts have been made over the last decades to improve the robustness of clustering algorithms against noise features and outliers, known to be important sources of error in clustering. Outliers dominate the sum-of-the-squares calculations and generate cluster overlap, thus leading to unreliable clustering results. They can be particularly detrimental in computational biology, e.g., when determining the number of clusters in gene expression data related to cancer or when inferring phylogenetic...

Protein Structure Prediction Using Population-based Algorithm Guided by Information Entropy.

Ab initio protein structure prediction is one of the most challenging problems in computational biology. Multistage algorithms are widely used in ab initio protein structure prediction. The different computational costs of a multistage algorithm for different proteins are important to be considered. In this study, a population-based algorithm guided by information entropy (PAIE), which includes exploration and exploitation stages, is proposed for protein structure prediction. In PAIE, an entropy-based stage...

MGRFE: multilayer recursive feature elimination based on an embedded genetic algorithm for cancer classification.

Microarray gene expression data have become a topic of great interest for cancer classification and for further research in the field of bioinformatics. Nonetheless, due to the "large p, small n" paradigm of limited biosamples and high-dimensional data, gene selection is becoming a demanding task, which is aimed at selecting a minimal number of discriminatory genes associated closely with a phenotype. Feature or gene selection is still a challenging problem owing to its nondeterministic polynomial time comp...

Predicting Carbon Spectrum in Heteronuclear Single Quantum Coherence Spectroscopy for Online Feedback During Surgery.

1H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC)-NMR is applied to distinguish metabolites in 2D spec...

An Improved Muti-task Learning Algorithm for Analyzing Cancer Survival Data.

Survival analysis is a popular branch of statistics. At present, many algorithms (like traditional multi-tasking learning model) cannot be applied well in practice because of censored data. Although using some model (like parametric regression model) can avoid it in somehow, they need strict assumptions. This undermines the very nature of things, badly detrimental to the study of practical problems. The method proposed in this paper can apply well to the censored data, but does not need to make any addition...

CNV_IFTV: an isolation forest and total variation-based detection of CNVs from short-read sequencing data.

Accurate detection of copy number variations (CNVs) from short-read sequencing data is challenging due to the uneven distribution of reads and the unbalanced amplitudes of gains and losses. The direct use of read depths to measure CNVs tends to limit performance. Thus, robust computational approaches equipped with appropriate statistics are required to detect CNV regions and boundaries. This study proposes a new method called CNV_IFTV to address this need. CNV_IFTV assigns an anomaly score to each genome bi...

Scalable De Novo Genome Assembly Using a Pregel-Like Graph-Parallel System.

De novo genome assembly is the process of stitching short DNA sequences to generate longer DNA sequences, without using any reference sequence for alignment. It enables high-throughput genome sequencing and thus accelerates the discovery of new genomes. In this paper, we present a toolkit, called PPA-assembler, for de novo genome assembly in a distributed setting. The operations in our toolkit provide strong performance guarantees, and can be assembled to implement various sequencing strategies. PPA-assembl...

Optimal Bayesian Transfer Learning for Count Data.

There is often limited amount of omics data to design predictive models in biomedicine. Knowing that these omics data come from underlying processes that may share common pathways and disease mechanisms, it may be beneficial for designing a more accurate and reliable predictor in a target domain of interest, where there is a lack of labeled data to leverage available data in relevant source domains. Here, we focus on developing Bayesian transfer learning methods for analyzing next-generation sequencing (NGS...

Structural Bistability Analysis of Flower-shaped and Chain-shaped Boolean Networks.

Bistability, i.e., the existence of just two stable equilibria, is known to play an important role in biological systems, e.g., cellular differentiation and apoptosis. In this paper, we consider the bistability but as a structural property of a class of network systems, that is, the bistability under the assumption that the information on the network structure is available but the information on the components is not available. First, we introduce Boolean networks as a model of biological network systems an...

Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks.

Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein su...

Two-stage Distance Feature-based Optimization Algorithm for De novo Protein Structure Prediction.

De novo protein structure prediction can be treated as a conformational space optimization problem under the guidance of an energy function. However, it is a challenge of how to design an accurate energy function which ensures low-energy conformations close to native structures. Fortunately, recent studies have shown that the accuracy of de novo protein structure prediction can be significantly improved by integrating the residue-residue distance information. In this paper, a two-stage distance feature-base...

BiClusO: A novel biclustering approach and its application to species-VOC relational data.

In this paper, we propose a novel biclustering approach called BiClusO. Biclustering can be applied to various types of bipartite data such as gene-condition or gene-disease relations. For example, we applied BiClusO to bipartite relations between species and volatile organic compounds (VOCs). VOCs, which are emitted by different species, have huge environmental and ecological impacts. The biosynthesis of VOCs depends on different metabolic pathways which can be used to categorize the species. A previous st...

An efficient approach towards the source-target control of Boolean networks.

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must e...

Predicting essential proteins by integrating network topology, subcellular localization information, gene expression profile and GO annotation data.

Essential proteins are indispensable for maintaining normal cellular functions. Identification of essential proteins from Protein-protein interaction (PPI) networks has become a hot topic in recent years. Traditionally biological experimental based approaches are time-consuming and expensive, although lots of computational based methods have been developed in the past years; however, the prediction accuracy is still unsatisfied. In this research, by introducing the protein sub-cellular localization informat...

LSTM-Based End-to-End Framework for Biomedical Event Extraction.

Biomedical event extraction plays an important role in the extraction of biological information from large-scale scientific publications. However, most state-of-the-art systems separate this task into several steps, which leads to cascading errors. In addition, it is complicated to generate features from syntactic and dependency analysis separately. Therefore, in this paper, we propose an end to-end model based on long short-term memory (LSTM) to optimize biomedical event extraction. Experimental results de...

End-to-end Security for Local and Remote Human Genetic Data Applications at the EGA.

Sensitive genomic data should remain secure - whether on disk for storage, or analysis, or in transport. However, secure storage, delivery, and usage of genomic data is complicated by the size of files and diversity of workflows. This paper presents solutions developed by GA4GH and EGA to use customized encryption, encrypted file formats, toolchain integration, and intelligent APIs to help solve this problem.

A Global and Local Enhanced Residual U-Net for Accurate Retinal Vessel Segmentation.

Retinal vessel segmentation is a critical procedure towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. Recent deep learning-based approaches have achieved impressive performance in retinal vessel segmentation. However, they usually apply global image pre-processing and take the whole retinal images as input during network training, which have two drawbacks for accurate retinal vessel segmentation. First, these methods lack the utilization of the local pat...

Prioritizing type 2 diabetes genes by weighted PageRank on bilayer heterogeneous networks.

The prevalence of diabetes mellitus has been increasing rapidly in recent years. The interacting mixed effects of genetics and environments build possible interpretable pathogenesis. Thus, finding causal diabetes genes is crucial in its clinical diagnosis and medical treatment. Currently, network-based method becomes a powerful tool of systematically analyzing complex diseases, such as the identification of candidate disease genes. In this paper, we propose a bioinformatics framework of prioritizing type 2 ...

Sorting Signed Permutations by Inverse Tandem Duplication Random Losses.

Gene order evolution of unichromosomal genomes, for example mitochondrial genomes, has been modelled mostly by four major types of genome rearrangements: inversions, transpositions, inverse transpositions, and tandem duplication random losses. Generalizing models that include all those rearrangements while admitting computational tractability are rare. In this paper we study such a rearrangement model, namely the inverse tandem duplication random loss (iTDRL) model, where an iTDRL duplicates and inverts a c...

Topological metrizations of trees, and new quartet methods of tree inference.

Topological phylogenetic trees can be assigned edge weights in several natural ways, highlighting different aspects of the tree. Here the rooted triple and quartet metrizations are introduced, and applied to formulate novel methods of inferring large trees from rooted triple and quartet data. These methods lead to new statistically consistent procedures for inference of a species tree from gene trees under the multispecies coalescent model.

Post-Structuring Radiology Reports of Breast Cancer Patients for Clinical Quality Assurance.

Hospitals often set protocols based on well defined standards to maintain the quality of patient reports. To ensure that the clinicians conform to the protocols, quality assurance of these reports is needed. Patient reports are currently written in free-text format, which complicates the task of quality assurance. In this paper, we present a machine learning based natural language processing system for automatic quality assurance of radiology reports on breast cancer. This is achieved in three steps: we i) ...

Approaching the One-sided Exemplar Adjacency Number Problem.

The one-sided Exemplar Adjacency Number (EAN) is a known problem for computing the exemplar similarity between a generic linear genome G with gene duplications and an exemplar genome H (over the same set of n gene families). In this problem, we need to compute an exemplar genome G, which is a permutation obtained from G, such that the number of common adjacencies between G and H is maximized. Unfortunately, the problem is not only NP-hard but also NP-hard to approximate. In this paper, we approach the probl...

Analysis of Subtelomeric REXTAL Assemblies Using QUAST.

Genomic regions of high segmental duplication content and/or structural variation have led to gaps and misassemblies in the human reference sequence, and are refractory to assembly from whole-genome short-read datasets. Human subtelomere regions are highly enriched in both segmental duplication content and structural variations, and as a consequence are both impossible to assemble accurately and highly variable from individual to individual. Recently, we developed a pipeline for improved region-specific ass...

On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics.

In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution audience not to take this assumption for granted, and present empirical evidence of its ineffectiveness from a machine learning perspective. Our examination is based on a setting where binding profiles are used as features to train predictive models; we show (1)...


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