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bioinformatics using machine learning

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https://www.readbyqxmd.com/read/28700537/qst-response-patterns-to-capsaicin-and-uv-b-induced-local-skin-hypersensitization-in-healthy-subjects-a-machine-learned-analysis
#1
Jörn Lötsch, Gerd Geisslinger, Sarah Heinemann, Florian Lerch, Bruno G Oertel, Alfred Ultsch
The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we assessed established experimental models of heat hyperalgesia of the skin, consisting of local ultraviolet-B (UV-B) irradiation or capsaicin application, in 82 healthy subjects using a variety of noxious stimuli. We extended the original heat simulation by applying cold and mechanical stimuli and assessing the hypersensitization effects with a clinically established quantitative sensory testing (QST) battery (German Research Network on Neuropathic Pain)...
July 8, 2017: Pain
https://www.readbyqxmd.com/read/28659577/elitist-binary-wolf-search-algorithm-for-heuristic-feature-selection-in-high-dimensional-bioinformatics-datasets
#2
Jinyan Li, Simon Fong, Raymond K Wong, Richard Millham, Kelvin K L Wong
Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, 'It is not the strongest of the species that survives, but the most adaptable'. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation...
June 28, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28643394/incorporating-deep-learning-with-convolutional-neural-networks-and-position-specific-scoring-matrices-for-identifying-electron-transport-proteins
#3
Nguyen-Quoc-Khanh Le, Quang-Thai Ho, Yu-Yen Ou
In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80...
June 22, 2017: Journal of Computational Chemistry
https://www.readbyqxmd.com/read/28590455/machine-learned-data-structures-of-lipid-marker-serum-concentrations-in-multiple-sclerosis-patients-differ-from-those-in-healthy-subjects
#4
Jörn Lötsch, Michael Thrun, Florian Lerch, Robert Brunkhorst, Susanne Schiffmann, Dominique Thomas, Irmgard Tegder, Gerd Geisslinger, Alfred Ultsch
Lipid metabolism has been suggested to be a major pathophysiological mechanism of multiple sclerosis (MS). With the increasing knowledge about lipid signaling, acquired data become increasingly complex making bioinformatics necessary in lipid research. We used unsupervised machine-learning to analyze lipid marker serum concentrations, pursuing the hypothesis that for the most relevant markers the emerging data structures will coincide with the diagnosis of MS. Machine learning was implemented as emergent self-organizing feature maps (ESOM) combined with the U*-matrix visualization technique...
June 7, 2017: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28575181/deepsite-protein-binding-site-predictor-using-3d-convolutional-neural-networks
#5
J Jiménez, S Doerr, G Martínez-Rosell, A S Rose, G De Fabritiis
Motivation: An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. Results: Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples...
May 31, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28575147/tissue-specific-network-based-genome-wide-study-of-amygdala-imaging-phenotypes-to-identify-functional-interaction-modules
#6
Xiaohui Yao, Jingwen Yan, Kefei Liu, Sungeun Kim, Kwangsik Nho, Shannon L Risacher, Casey S Greene, Jason H Moore, Andrew J Saykin, Li Shen
Motivation: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: 1) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; 2) detect densely connected modules based on interactions among top re-prioritized genes; and 3) identify phenotype-relevant modules enriched by top GWAS findings...
May 29, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28574156/prediction-of-lithium-response-in-first-episode-mania-using-the-lithium-intelligent-agent-lithia-pilot-data-and-proof-of-concept
#7
David E Fleck, Nicholas Ernest, Caleb M Adler, Kelly Cohen, James C Eliassen, Matthew Norris, Richard A Komoroski, Wen-Jang Chu, Jeffrey A Welge, Thomas J Blom, Melissa P DelBello, Stephen M Strakowski
OBJECTIVES: Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree (GFT) design called the LITHium Intelligent Agent (LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy ((1) H-MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania...
June 2017: Bipolar Disorders
https://www.readbyqxmd.com/read/28499419/across-proteome-modeling-of-dimer-structures-for-the-bottom-up-assembly-of-protein-protein-interaction-networks
#8
Surabhi Maheshwari, Michal Brylinski
BACKGROUND: Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved...
May 12, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28499008/rippminer-a-bioinformatics-resource-for-deciphering-chemical-structures-of-ripps-based-on-prediction-of-cleavage-and-cross-links
#9
Priyesh Agrawal, Shradha Khater, Money Gupta, Neetu Sain, Debasisa Mohanty
Ribosomally synthesized and post-translationally modified peptides (RiPPs) constitute a rapidly growing class of natural products with diverse structures and bioactivities. We have developed RiPPMiner, a novel bioinformatics resource for deciphering chemical structures of RiPPs by genome mining. RiPPMiner derives its predictive power from machine learning based classifiers, trained using a well curated database of more than 500 experimentally characterized RiPPs. RiPPMiner uses Support Vector Machine to distinguish RiPP precursors from other small proteins and classify the precursors into 12 sub-classes of RiPPs...
May 12, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28476106/geminivirus-data-warehouse-a-database-enriched-with-machine-learning-approaches
#10
Jose Cleydson F Silva, Thales F M Carvalho, Marcos F Basso, Michihito Deguchi, Welison A Pereira, Roberto R Sobrinho, Pedro M P Vidigal, Otávio J B Brustolini, Fabyano F Silva, Maximiller Dal-Bianco, Renildes L F Fontes, Anésia A Santos, Francisco Murilo Zerbini, Fabio R Cerqueira, Elizabeth P B Fontes
BACKGROUND: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases...
May 5, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28472232/privacy-preserving-evaporative-cooling-feature-selection-and-classification-with-relief-f-and-random-forests
#11
Trang T Le, W Kyle Simmons, Masaya Misaki, Jerzy Bodurka, Bill C White, Jonathan Savitz, Brett A McKinney
Motivation: Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed...
May 4, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28469415/current-developments-in-machine-learning-techniques-in-biological-data-mining
#12
EDITORIAL
Gerard G Dumancas, Indra Adrianto, Ghalib Bello, Mikhail Dozmorov
This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques...
2017: Bioinformatics and Biology Insights
https://www.readbyqxmd.com/read/28449114/neuro-symbolic-representation-learning-on-biological-knowledge-graphs
#13
Mona Alshahrani, Mohammed Asif Khan, Omar Maddouri, Akira R Kinjo, Núria Queralt-Rosinach, Robert Hoehndorf
Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs...
April 25, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28444127/hla-class-i-binding-prediction-via-convolutional-neural-networks
#14
Yeeleng S Vang, Xiaohui Xie
Motivation: Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and nonself cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases...
April 21, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28430949/capturing-non-local-interactions-by-long-short-term-memory-bidirectional-recurrent-neural-networks-for-improving-prediction-of-protein-secondary-structure-backbone-angles-contact-numbers-and-solvent-accessibility
#15
Rhys Heffernan, Yuedong Yang, Kuldip Paliwal, Yaoqi Zhou
Motivation: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some "short to intermediate" non-local interactions...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28398465/machine-learning-in-computational-biology-to-accelerate-high-throughput-protein-expression
#16
Anand Sastry, Jonathan Monk, Hanna Tegel, Mathias Uhlén, Bernhard O Palsson, Johan Rockberg, Elizabeth Brunk
Motivation: The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40,000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecularlevel properties influencing expression and solubility...
April 7, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28391206/multiple-swarm-ensembles-improving-the-predictive-power-and-robustness-of-predictive-models-and-its-use-in-computational-biology
#17
Pedro Alves, Shuang Liu, Daifeng Wang, Mark Gerstein
Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness...
April 5, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28361684/nearender-an-r-package-for-functional-interpretation-of-omics-data-via-network-enrichment-analysis
#18
Ashwini Jeggari, Andrey Alexeyenko
BACKGROUND: The statistical evaluation of pathway enrichment, i.e. of gene profiles' confluence to the pathway level, allows exploring molecular landscapes using functionally annotated gene sets. However, pathway scores can also be used as predictive features in machine learning. That requires, firstly, increasing statistical power and biological relevance via a network enrichment analysis (NEA) and, secondly, a fast and convenient procedure for rendering the original data into a space of pathway scores...
March 23, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28351701/extracting-features-from-protein-sequences-to-improve-deep-extreme-learning-machine-for-protein-fold-recognition
#19
Wisam Ibrahim, Mohammad Saniee Abadeh
Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences...
March 27, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28341746/leveraging-sequence-based-faecal-microbial-community-survey-data-to-identify-a-composite-biomarker-for-colorectal-cancer
#20
Manasi S Shah, Todd Z DeSantis, Thomas Weinmaier, Paul J McMurdie, Julia L Cope, Adam Altrichter, Jose-Miguel Yamal, Emily B Hollister
OBJECTIVE: Colorectal cancer (CRC) is the second leading cause of cancer-associated mortality in the USA. The faecal microbiome may provide non-invasive biomarkers of CRC and indicate transition in the adenoma-carcinoma sequence. Re-analysing raw sequence and metadata from several studies uniformly, we sought to identify a composite and generalisable microbial marker for CRC. DESIGN: Raw 16S rRNA gene sequence data sets from nine studies were processed with two pipelines, (1) QIIME closed reference (QIIME-CR) or (2) a strain-specific method herein termed SS-UP (Strain Select, UPARSE bioinformatics pipeline)...
March 24, 2017: Gut
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