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Journal of Bioinformatics and Computational Biology

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https://read.qxmd.com/read/30616476/a-max-margin-training-of-rna-secondary-structure-prediction-integrated-with-the-thermodynamic-model
#1
Manato Akiyama, Kengo Sato, Yasubumi Sakakibara
A popular approach for predicting RNA secondary structure is the thermodynamic nearest-neighbor model that finds a thermodynamically most stable secondary structure with minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning-based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a machine learning-based fine-grained model achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such a model has been reported...
December 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30616475/introduction-to-selected-papers-from-giw2018
#2
Jinyan Li, Kenta Nakai, Yun Zheng, Kengo Sato, Limsoon Wong
No abstract text is available yet for this article.
December 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30616474/author-index-volume-16-2018
#3
(no author information available yet)
No abstract text is available yet for this article.
December 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30616473/a-2-approximation-algorithm-for-the-contig-based-genomic-scaffold-filling-problem
#4
Haitao Jiang, Letu Qingge, Daming Zhu, Binhai Zhu
The genomic scaffold filling problem has attracted a lot of attention recently. The problem is on filling an incomplete sequence (scaffold) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>I</mml:mi> </mml:math> into <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>I</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>'</mml:mi> </mml:mrow> </mml:msup> </mml:math> , with respect to a complete reference genome <mml:math xmlns:mml="http://www...
December 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30567479/computational-identification-of-physicochemical-signatures-for-host-tropism-of-influenza-a-virus
#5
Rui Yin, Xinrui Zhou, Jie Zheng, Chee Keong Kwoh
Avian influenza viruses from migratory birds have managed to cross host species barriers and infected various hosts like human and swine. Epidemics and pandemics might occur when influenza viruses are adapted to humans, causing deaths and enormous economic loss. Receptor-binding specificity of the virus is one of the key factors for the transmission of influenza viruses across species. The determination of host tropism and understanding of molecular properties would help identify the mechanism why zoonotic influenza viruses can cross species barrier and infect humans...
October 30, 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30567478/constrained-maximum-entropy-models-to-select-genotype-interactions-associated-with-censored-failure-times
#6
Aotian Yang, David Miller, Qing Pan
We propose a novel screening method targeting genotype interactions associated with disease risks. The proposed method extends the maximum entropy conditional probability model to address disease occurrences over time. Continuous occurrence times are grouped into intervals. The model estimates the conditional distribution over the disease occurrence intervals given individual genotypes by maximizing the corresponding entropy subject to constraints linking genotype interactions to time intervals. The EM algorithm is employed to handle observations with uncertainty, for which the disease occurrence is censored...
October 30, 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30567477/extraction-of-drug-drug-interaction-using-neural-embedding
#7
Wen Juan Hou, Bamfa Ceesay
Information on changes in a drug's effect when taken in combination with a second drug, known as drug-drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system...
October 30, 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30567476/hiscom-ggi-hierarchical-structural-component-analysis-of-gene-gene-interactions
#8
Sungkyoung Choi, Sungyoung Lee, Yongkang Kim, Heungsun Hwang, Taesung Park
Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene-gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available...
October 30, 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30567475/systematic-analysis-and-integrative-discovery-of-active-site-subpocket-specific-dehydroquinate-synthase-inhibitors-combating-antibiotic-resistant-staphylococcus-aureus-infection
#9
Quanfeng Liu, Liping Li, Fei Xu
Shikimate pathway plays an essential role in the biosynthesis of aromatic amino acids in various plants and bacteria, which consists of seven key enzymes and they are all attractive targets for antibacterial agent development due to their absence in humans. The Staphylococcus aureus dehydroquinate synthase (SaDHQS) is involved in the second step of shikimate pathway, which catalyzes the NAD <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo>+</mml:mo> </mml:mrow> </mml:msup> </mml:math> -dependent conversion of 3-deoxy-D-arabino-heptulosonate-7-phosphate to dehydroquinate via multiple steps...
October 30, 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30567474/a-novel-graph-kernel-on-chemical-compound-classification
#10
Qiangrong Jiang, Jiajia Ma
Considering the classification of compounds as a nonlinear problem, the use of kernel methods is a good choice. Graph kernels provide a nice framework combining machine learning methods with graph theory, whereas the essence of graph kernels is to compare the substructures of two graphs, how to extract the substructures is a question. In this paper, we propose a novel graph kernel based on matrix named the local block kernel, which can compare the similarity of partial substructures that contain any number of vertexes...
October 30, 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30567473/cloud-bs-a-mapreduce-based-bisulfite-sequencing-aligner-on-cloud
#11
Joungmin Choi, Yoonjae Park, Sun Kim, Heejoon Chae
In recent years, there have been many studies utilizing DNA methylome data to answer fundamental biological questions. Bisulfite sequencing (BS-seq) has enabled measurement of a genome-wide absolute level of DNA methylation at single-nucleotide resolution. However, due to the ambiguity introduced by bisulfite-treatment, the aligning process especially in large-scale epigenetic research is still considered a huge burden. We present Cloud-BS, an efficient BS-seq aligner designed for parallel execution on a distributed environment...
October 30, 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30419787/identification-of-jointly-correlated-gene-sets
#12
Yuanfang Ren, Ahmet Ay, Travis A Gerke, Tamer Kahveci
Associations between expressions of genes play a key role in deciphering their functions. Correlation score between pairs of genes is often utilized to associate two genes. However, the relationship between genes is often more complex; multiple genes might collaborate to control the transcription of a gene. In this paper, we introduce the problem of searching pairs of genes, which collectively correlate with another gene. This problem is computationally much harder than the classical problem of identifying pairwise gene associations...
October 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30419786/gene-multifunctionality-scoring-using-gene-ontology
#13
Hisham Al-Mubaid
Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene multifunctionality based on functional annotations of the target gene from the Gene Ontology. The method is based on identifying pairs of GO annotations that represent semantically different biological functions and any gene annotated with two annotations from one pair is considered multifunctional...
October 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30419785/protein-secondary-structure-prediction-improved-by-recurrent-neural-networks-integrated-with-two-dimensional-convolutional-neural-networks
#14
Yanbu Guo, Bingyi Wang, Weihua Li, Bei Yang
Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature vector dimension. Common approaches to predict secondary structures only focus on the amino-acid residue dimension. However, the feature vector dimension may also contain useful information for PSSP. To integrate the information on both dimensions of the matrix, we propose a hybrid deep learning framework, two-dimensional convolutional bidirectional recurrent neural network (2C-BRNN), for improving the accuracy of 8-class secondary structure prediction...
October 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30419784/a-systematic-exploration-of-formula-see-text-cutoff-ranges-in-machine-learning-models-for-protein-mutation-stability-prediction
#15
Richard Olney, Aaron Tuor, Filip Jagodzinski, Brian Hutchinson
Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Mutagenesis experiments on physical proteins provide precise insights about the effects of amino acid substitutions, but such studies are time and cost prohibitive. Computational approaches for informing experimentalists where to allocate wet-lab resources are available, including a variety of machine learning models. Assessing the accuracy of machine learning models for predicting the effects of mutations is dependent on experiments for amino acid substitutions performed in vitro...
October 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30419783/adjusted-likelihood-ratio-test-for-variants-with-unknown-genotypes
#16
Ronald J Nowling, Scott J Emrich
Association tests performed with the Likelihood-Ratio Test (LR Test) can be an alternative to [Formula: see text], which is often used in population genetics to find variants of interest. Because the LR Test has several properties that could make it preferable to [Formula: see text], we propose a novel approach for modeling unknown genotypes in highly-similar species. To show the effectiveness of this LR Test approach, we apply it to single-nucleotide polymorphisms (SNPs) associated with the recent speciation of the malaria vectors Anopheles gambiae and Anopheles coluzzii and compare to [Formula: see text]...
October 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30419782/minimizing-the-deep-coalescence-cost
#17
Dawid Dąbkowski, Paweł Tabaszewski, Paweł Górecki
Metagenomic studies identify the species present in an environmental sample usually by using procedures that match molecular sequences, e.g. genes, with the species taxonomy. Here, we first formulate the problem of gene-species matching in the parsimony framework using binary phylogenetic gene and species trees under the deep coalescence cost and the assumption that each gene is paired uniquely with one species. In particular, we solve the problem in the cases when one of the trees is a caterpillar. Next, we propose a dynamic programming algorithm, which solves the problem exactly, however, its time and space complexity is exponential...
October 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30419781/introduction-to-jbcb-special-issue-on-bicob-2018
#18
Hisham Al-Mubaid, Qin Ding, Oliver Eulenstein
No abstract text is available yet for this article.
October 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30419780/transcriptional-processes-models-and-inference
#19
Keerthi S Shetty, Annappa B
Many biochemical events involve multistep reactions. One of the most important biological processes that involve multistep reaction is the transcriptional process. Models for multistep reaction necessarily need multiple states and it is a challenge to compute model parameters that best agree with experimental data. Therefore, the aim of this work is to design a multistep promoter model which accurately characterizes transcriptional bursting and is consistent with observed data. To address this issue, we develop a model for promoters with several OFF states and a single ON state using Erlang distribution...
October 2018: Journal of Bioinformatics and Computational Biology
https://read.qxmd.com/read/30415600/motif-discovery-in-biological-network-using-expansion-tree
#20
Sabyasachi Patra, Anjali Mohapatra
Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc...
September 19, 2018: Journal of Bioinformatics and Computational Biology
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