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Biological machines

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https://www.readbyqxmd.com/read/28453671/wright-fisher-exact-solver-wfes-scalable-analysis-of-population-genetic-models-without-simulation-or-diffusion-theory
#1
Ivan Krukov, Bianca de Sanctis, A P Jason de Koning
Motivation: The simplifying assumptions that are used widely in theoretical population genetics may not always be appropriate for empirical population genetics. General computational approaches that do not require the assumptions of classical theory are therefore quite desirable. One such general approach is provided by the theory of absorbing Markov chains, which can be used to obtain exact results by directly analyzing population genetic Markov models, such as the classic bi-allelic Wright-Fisher model...
May 1, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28449114/neuro-symbolic-representation-learning-on-biological-knowledge-graphs
#2
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
#3
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/28440291/identifying-n-6-methyladenosine-sites-using-multi-interval-nucleotide-pair-position-specificity-and-support-vector-machine
#4
Pengwei Xing, Ran Su, Fei Guo, Leyi Wei
N6-methyladenosine (m(6)A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m(6)A sites within sequences since high-resolution mapping of m(6)A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m(6)A sites are time-consuming and expensive...
April 25, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28440283/quantitative-diagnosis-of-breast-tumors-by-morphometric-classification-of-microenvironmental-myoepithelial-cells-using-a-machine-learning-approach
#5
Yoichiro Yamamoto, Akira Saito, Ayako Tateishi, Hisashi Shimojo, Hiroyuki Kanno, Shinichi Tsuchiya, Ken-Ichi Ito, Eric Cosatto, Hans Peter Graf, Rodrigo R Moraleda, Roland Eils, Niels Grabe
Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS)...
April 25, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28439103/brain-age-predicts-mortality
#6
J H Cole, S J Ritchie, M E Bastin, M C Valdés Hernández, S Muñoz Maniega, N Royle, J Corley, A Pattie, S E Harris, Q Zhang, N R Wray, P Redmond, R E Marioni, J M Starr, S R Cox, J M Wardlaw, D J Sharp, I J Deary
Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, 'brain-predicted age', derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality...
April 25, 2017: Molecular Psychiatry
https://www.readbyqxmd.com/read/28437616/neural-network-and-nearest-neighbour-algorithms-for-enhancing-sampling-of-molecular-dynamics
#7
Raimondas Galvelis, Yuji Sugita
The free energy calculations of complex chemical and biological systems with molecular dynamics (MD) are inefficient due to multiple local minima separated by high-energy barriers. The minima can be escaped using an enhanced sampling method such as metadynamics, which apply bias (i.e. importance sampling) along a set of collective variables (CV), but the maximum number of CVs (or dimensions) is severely limited. We propose a high-dimensional bias potential method (NN2B) based on two machine learning algorithms: the nearest neighbour density estimator (NNDE) and the artificial neural network (ANN) for the bias potential approximation...
April 24, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28437133/the-impact-of-the-centrifuge-characteristics-and-centrifugation-protocols-on-the-cells-growth-factors-and-fibrin-architecture-of-a-leukocyte-and-platelet-rich-fibrin-l-prf-clot-and-membrane
#8
David M Dohan Ehrenfest, Nelson R Pinto, Andrea Pereda, Paula Jiménez, Marco Del Corso, Byung-Soo Kang, Mauricio Nally, Nicole Lanata, Hom-Lay Wang, Marc Quirynen
L-PRF (leukocyte- and platelet-rich fibrin) is one of the four families of platelet concentrates for surgical use and is widely used in oral and maxillofacial regenerative therapies. The first objective of this article was to evaluate the mechanical vibrations appearing during centrifugation in four models of commercially available table-top centrifuges used to produce L-PRF and the impact of the centrifuge characteristics on the cell and fibrin architecture of a L-PRF clot and membrane. The second objective of this article was to evaluate how changing some parameters of the L-PRF protocol may influence its biological signature, independently from the characteristics of the centrifuge...
April 24, 2017: Platelets
https://www.readbyqxmd.com/read/28436884/novelty-indicator-for-enhanced-prioritization-of-predicted-gene-ontology-annotations
#9
Davide Chicco, Fernando Palluzzi, Marco Masseroli
Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new annotations are both effective and efficient, especially if compared with time-consuming trials of biological validation...
April 18, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28435872/unraveling-self-assembly-pathways-of-the-468-kda-proteolytic-machine-tet2
#10
Pavel Macek, Rime Kerfah, Elisabetta Boeri Erba, Elodie Crublet, Christine Moriscot, Guy Schoehn, Carlos Amero, Jerome Boisbouvier
The spontaneous formation of biological higher-order structures from smaller building blocks, called self-assembly, is a fundamental attribute of life. Although the protein self-assembly is a time-dependent process that occurs at the molecular level, its current understanding originates either from static structures of trapped intermediates or from modeling. Nuclear magnetic resonance (NMR) spectroscopy has the unique ability to monitor structural changes in real time; however, its size limitation and time-resolution constraints remain a challenge when studying the self-assembly of large biological particles...
April 2017: Science Advances
https://www.readbyqxmd.com/read/28426242/cellular-electron-cryotomography-toward-structural-biology-in-situ
#11
Catherine M Oikonomou, Grant J Jensen
Electron cryotomography (ECT) provides three-dimensional views of macromolecular complexes inside cells in a native frozen-hydrated state. Over the last two decades, ECT has revealed the ultrastructure of cells in unprecedented detail. It has also allowed us to visualize the structures of macromolecular machines in their native context inside intact cells. In many cases, such machines cannot be purified intact for in vitro study. In other cases, the function of a structure is lost outside the cell, so that the mechanism can be understood only by observation in situ...
April 19, 2017: Annual Review of Biochemistry
https://www.readbyqxmd.com/read/28423569/accurate-prediction-of-protein-protein-interactions-by-integrating-potential-evolutionary-information-embedded-in-pssm-profile-and-discriminative-vector-machine-classifier
#12
Zheng-Wei Li, Zhu-Hong You, Xing Chen, Li-Ping Li, De-Shuang Huang, Gui-Ying Yan, Ru Nie, Yu-An Huang
Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era...
April 4, 2017: Oncotarget
https://www.readbyqxmd.com/read/28420678/automated-analysis-of-high-content-microscopy-data-with-deep-learning
#13
Oren Z Kraus, Ben T Grys, Jimmy Ba, Yolanda Chong, Brendan J Frey, Charles Boone, Brenda J Andrews
Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization...
April 18, 2017: Molecular Systems Biology
https://www.readbyqxmd.com/read/28418043/predicting-protein-protein-association-rates-using-coarse-grained-simulation-and-machine-learning
#14
Zhong-Ru Xie, Jiawen Chen, Yinghao Wu
Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins...
April 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28416677/structural-toggle-in-the-rnaseh-domain-of-prp8-helps-balance-splicing-fidelity-and-catalytic-efficiency
#15
Megan Mayerle, Madhura Raghavan, Sarah Ledoux, Argenta Price, Nicholas Stepankiw, Haralambos Hadjivassiliou, Erica A Moehle, Senén D Mendoza, Jeffrey A Pleiss, Christine Guthrie, John Abelson
Pre-mRNA splicing is an essential step of eukaryotic gene expression that requires both high efficiency and high fidelity. Prp8 has long been considered the "master regulator" of the spliceosome, the molecular machine that executes pre-mRNA splicing. Cross-linking and structural studies place the RNaseH domain (RH) of Prp8 near the spliceosome's catalytic core and demonstrate that prp8 alleles that map to a 17-aa extension in RH stabilize it in one of two mutually exclusive structures, the biological relevance of which are unknown...
April 17, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28414216/unraveling-the-thousand-word-picture-an-introduction-to-super-resolution-data-analysis
#16
Antony Lee, Konstantinos Tsekouras, Christopher Calderon, Carlos Bustamante, Steve Pressé
Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light's diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we've termed the interpretation problem...
April 17, 2017: Chemical Reviews
https://www.readbyqxmd.com/read/28413974/identification-of-cell-cycle-regulated-genes-by-convolutional-neural-network
#17
Chenglin Liu, Peng Cui, Tao Huang
BACKGROUND: The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. METHODS: Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution...
April 17, 2017: Combinatorial Chemistry & High Throughput Screening
https://www.readbyqxmd.com/read/28411111/s-sulfpred-a-sensitive-predictor-to-capture-s-sulfenylation-sites-based-on-a-resampling-one-sided-selection-undersampling-synthetic-minority-oversampling-technique
#18
Cangzhi Jia, Yun Zuo
Protein S-sulfenylation is a reversible post-translational modification involving covalent attachment of hydroxide to the thiol group of cysteine residues, which is involved in various biological processes including cell signaling, response to stress and protein functions. Herein we present S-SulfPred, a support vector machine based model to capture potential S-sulfenylation sites and improve the efficiency and relevance of experimental identification of protein S-sulfenylation sites. One-sided selection (OSS) undersampling and synthetic minority oversampling technique (SMOTE) oversampling were combined to establish balanced training datasets...
April 12, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28408735/predicting-neurological-adverse-drug-reactions-based-on-biological-chemical-and-phenotypic-properties-of-drugs-using-machine-learning-models
#19
Salma Jamal, Sukriti Goyal, Asheesh Shanker, Abhinav Grover
Adverse drug reactions (ADRs) have become one of the primary reasons for the failure of drugs and a leading cause of deaths. Owing to the severe effects of ADRs, there is an urgent need for the generation of effective models which can accurately predict ADRs during early stages of drug development based on integration of various features of drugs. In the current study, we have focused on neurological ADRs and have used various properties of drugs that include biological properties (targets, transporters and enzymes), chemical properties (substructure fingerprints), phenotypic properties (side effects (SE) and therapeutic indications) and a combinations of the two and three levels of features...
April 13, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28406174/complete-fold-annotation-of-the-human-proteome-using-a-novel-structural-feature-space
#20
Sarah A Middleton, Joseph Illuminati, Junhyong Kim
Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example...
April 13, 2017: Scientific Reports
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