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bioinformatics using machine learning

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https://www.readbyqxmd.com/read/28315224/an-overview-of-bioinformatics-tools-and-resources-in-allergy
#1
Zhiyan Fu, Jing Lin
The rapidly increasing number of characterized allergens has created huge demands for advanced information storage, retrieval, and analysis. Bioinformatics and machine learning approaches provide useful tools for the study of allergens and epitopes prediction, which greatly complement traditional laboratory techniques. The specific applications mainly include identification of B- and T-cell epitopes, and assessment of allergenicity and cross-reactivity. In order to facilitate the work of clinical and basic researchers who are not familiar with bioinformatics, we review in this chapter the most important databases, bioinformatic tools, and methods with relevance to the study of allergens...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28296577/genome-wide-identification-and-characterization-of-small-rnas-in-rhodobacter-capsulatus-and-identification-of-small-rnas-affected-by-loss-of-the-response-regulator-ctra
#2
Marc P Grüll, Lourdes Peña-Castillo, Martin E Mulligan, Andrew S Lang
Small non-coding RNAs (sRNAs) are involved in the control of numerous cellular processes through various regulatory mechanisms, and in the past decade many studies have identified sRNAs in a multitude of bacterial species using RNA sequencing (RNA-seq). Here, we present the first genome-wide analysis of sRNA sequencing data in Rhodobacter capsulatus, a purple nonsulfur photosynthetic alphaproteobacterium. Using a recently developed bioinformatics approach, sRNA-Detect, we detected 422 putative sRNAs from R...
March 15, 2017: RNA Biology
https://www.readbyqxmd.com/read/28198674/sequence-specific-bias-correction-for-rna-seq-data-using-recurrent-neural-networks
#3
Yao-Zhong Zhang, Rui Yamaguchi, Seiya Imoto, Satoru Miyano
BACKGROUND: The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures...
January 25, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28179914/cell-cycle-and-cell-size-dependent-gene-expression-reveals-distinct-subpopulations-at-single-cell-level
#4
Soheila Dolatabadi, Julián Candia, Nina Akrap, Christoffer Vannas, Tajana Tesan Tomic, Wolfgang Losert, Göran Landberg, Pierre Åman, Anders Ståhlberg
Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 - S - G2/M) with variable cell sizes...
2017: Frontiers in Genetics
https://www.readbyqxmd.com/read/28157153/enhancing-the-biological-relevance-of-machine-learning-classifiers-for-reverse-vaccinology
#5
Ashley I Heinson, Yawwani Gunawardana, Bastiaan Moesker, Carmen C Denman Hume, Elena Vataga, Yper Hall, Elena Stylianou, Helen McShane, Ann Williams, Mahesan Niranjan, Christopher H Woelk
Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data...
February 1, 2017: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28137713/hipred-an-integrative-approach-to-predicting-haploinsufficient-genes
#6
Hashem A Shihab, Mark F Rogers, Colin Campbell, Tom R Gaunt
MOTIVATION: A major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency incorporate biological networks, e.g. protein-protein interaction networks, that have recently been shown to introduce study bias. As a result, these methods tend to perform best on well studied genes, but underperform on less studied genes. The advent of large genome sequencing consortia, such as the 1,000 genomes project, NHLBI Exome Sequencing Project (ESP) and the Exome Aggregation Consortium (ExAC) creates an urgent need for unbiased haploinsufficiency prediction methods...
January 30, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28073761/seeing-the-trees-through-the-forest-sequence-based-homo-and-heteromeric-protein-protein-interaction-sites-prediction-using-random-forest
#7
Qingzhen Hou, Paul De Geest, Wim F Vranken, Jaap Heringa, K Anton Feenstra
MOTIVATION: Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in proteinprotein interactions. Such predictions are typically based on machine learning methods that take advantage of the properties and sequence positions of amino acids that are known to be involved in interaction...
January 10, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28064045/a-l1-regularized-feature-selection-method-for-local-dimension-reduction-on-microarray-data
#8
Shun Guo, Donghui Guo, Lifei Chen, Qingshan Jiang
Dimension reduction is a crucial technique in machine learning and data mining, which is widely used in areas of medicine, bioinformatics and genetics. In this paper, we propose a two-stage local dimension reduction approach for classification on microarray data. In first stage, a new L1-regularized feature selection method is defined to remove irrelevant and redundant features and to select the important features (biomarkers). In the next stage, PLS-based feature extraction is implemented on the selected features to extract synthesis features that best reflect discriminating characteristics for classification...
December 31, 2016: Computational Biology and Chemistry
https://www.readbyqxmd.com/read/28052925/proq3d-improved-model-quality-assessments-using-deep-learning
#9
Karolis Uziela, David Menéndez Hurtado, Nanjiang Shu, Björn Wallner, Arne Elofsson
Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network...
January 3, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28046236/su-d-204-06-integration-of-machine-learning-and-bioinformatics-methods-to-analyze-genome-wide-association-study-data-for-rectal-bleeding-and-erectile-dysfunction-following-radiotherapy-in-prostate-cancer
#10
J Oh, S Kerns, H Ostrer, B Rosenstein, J Deasy
PURPOSE: We investigated whether integration of machine learning and bioinformatics techniques on genome-wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation-induced late rectal bleeding and erectile dysfunction in prostate cancer patients. METHODS: We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning-based predictive model of late radiation-induced toxicities: rectal bleeding and erectile dysfunction...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28040499/machine-learned-cluster-identification-in-high-dimensional-data
#11
Alfred Ultsch, Jörn Lötsch
BACKGROUND: High-dimensional biomedical data are frequently clustered to identify subgroup structures pointing at distinct disease subtypes. It is crucial that the used cluster algorithm works correctly. However, by imposing a predefined shape on the clusters, classical algorithms occasionally suggest a cluster structure in homogenously distributed data or assign data points to incorrect clusters. We analyzed whether this can be avoided by using emergent self-organizing feature maps (ESOM)...
February 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28039166/an-ensemble-approach-to-protein-fold-classification-by-integration-of-template-based-assignment-and-support-vector-machine-classifier
#12
Jiaqi Xia, Zhenling Peng, Dawei Qi, Hongbo Mu, Jianyi Yang
MOTIVATION: Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learning algorithms. Combination of both solutions to improve the prediction accuracy was never explored before. RESULTS: We developed two algorithms, HH-fold and SVM-fold for protein fold classification. HH-fold is a template-based fold assignment algorithm using the HHsearch program...
December 30, 2016: Bioinformatics
https://www.readbyqxmd.com/read/28035027/qacon-single-model-quality-assessment-using-protein-structural-and-contact-information-with-machine-learning-techniques
#13
Renzhi Cao, Badri Adhikari, Debswapna Bhattacharya, Miao Sun, Jie Hou, Jianlin Cheng
MOTIVATION: Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large portion of low quality models in the model pool. RESULTS: In this paper, we develop a novel single-model quality assessment method QAcon utilizing structural features, physicochemical properties, and residue contact predictions...
December 28, 2016: Bioinformatics
https://www.readbyqxmd.com/read/27999618/complex-systems-analysis-of-bladder-cancer-susceptibility-reveals-a-role-for-decarboxylase-activity-in-two-genome-wide-association-studies
#14
Samantha Cheng, Angeline S Andrew, Peter C Andrews, Jason H Moore
BACKGROUND: Bladder cancer is common disease with a complex etiology that is likely due to many different genetic and environmental factors. The goal of this study was to embrace this complexity using a bioinformatics analysis pipeline designed to use machine learning to measure synergistic interactions between single nucleotide polymorphisms (SNPs) in two genome-wide association studies (GWAS) and then to assess their enrichment within functional groups defined by Gene Ontology. The significance of the results was evaluated using permutation testing and those results that replicated between the two GWAS data sets were reported...
2016: BioData Mining
https://www.readbyqxmd.com/read/27999256/recent-progress-in-machine-learning-based-methods-for-protein-fold-recognition
#15
REVIEW
Leyi Wei, Quan Zou
Knowledge on protein folding has a profound impact on understanding the heterogeneity and molecular function of proteins, further facilitating drug design. Predicting the 3D structure (fold) of a protein is a key problem in molecular biology. Determination of the fold of a protein mainly relies on molecular experimental methods. With the development of next-generation sequencing techniques, the discovery of new protein sequences has been rapidly increasing. With such a great number of proteins, the use of experimental techniques to determine protein folding is extremely difficult because these techniques are time consuming and expensive...
December 16, 2016: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/27998936/cellsort-a-support-vector-machine-tool-for-optimizing-fluorescence-activated-cell-sorting-and-reducing-experimental-effort
#16
Jessica S Yu, Dante A Pertusi, Adebola V Adeniran, Keith E J Tyo
MOTIVATION: High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and are practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required...
December 20, 2016: Bioinformatics
https://www.readbyqxmd.com/read/27993784/hum-mploc-3-0-prediction-enhancement-of-human-protein-subcellular-localization-through-modeling-the-hidden-correlations-of-gene-ontology-and-functional-domain-features
#17
Hang Zhou, Yang Yang, Hong-Bin Shen
MOTIVATION: Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy...
December 19, 2016: Bioinformatics
https://www.readbyqxmd.com/read/27993775/genius-web-server-to-predict-local-gene-networks-and-key-genes-for-biological-functions
#18
Tomas Puelma, Viviana Araus, Javier Canales, Elena A Vidal, Juan M Cabello, Alvaro Soto, Rodrigo A Gutiérrez
GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods...
December 19, 2016: Bioinformatics
https://www.readbyqxmd.com/read/27922074/resistance-gene-identification-from-larimichthys-crocea-with-machine-learning-techniques
#19
Yinyin Cai, Zhijun Liao, Ying Ju, Juan Liu, Yong Mao, Xiangrong Liu
The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. It is meaningful to identify and predict R-gene of Larimichthys crocea (L.Crocea). It is friendly for breeding and the marine environment as well. Large amounts of L.Crocea's immune mechanisms have been explored by biological methods. However, much about them is still unclear...
December 6, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27920952/a-methodology-for-the-design-of-experiments-in-computational-intelligence-with-multiple-regression-models
#20
Carlos Fernandez-Lozano, Marcos Gestal, Cristian R Munteanu, Julian Dorado, Alejandro Pazos
The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs...
2016: PeerJ
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