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Biological machines

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https://www.readbyqxmd.com/read/28635678/evaluation-of-classifier-performance-for-multiclass-phenotype-discrimination-in-untargeted-metabolomics
#1
Patrick J Trainor, Andrew P DeFilippis, Shesh N Rai
Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination...
June 21, 2017: Metabolites
https://www.readbyqxmd.com/read/28635541/association-between-abnormal-brain-functional-connectivity-in-children-and-psychopathology-a-study-based-on-graph-theory-and-machine-learning
#2
João Ricardo Sato, Claudinei Eduardo Biazoli, Giovanni Abrahão Salum, Ary Gadelha, Nicolas Crossley, Gilson Vieira, André Zugman, Felipe Almeida Picon, Pedro Mario Pan, Marcelo Queiroz Hoexter, Edson Amaro, Mauricio Anés, Luciana Monteiro Moura, Marco Antonio Gomes Del'Aquilla, Philip Mcguire, Luis Augusto Rohde, Euripedes Constantino Miguel, Andrea Parolin Jackowski, Rodrigo Affonseca Bressan
OBJECTIVES: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM). METHODS: We applied this approach to resting-state fMRI data from 622 children and adolescents...
February 8, 2017: World Journal of Biological Psychiatry
https://www.readbyqxmd.com/read/28634715/regulatory-element-based-prediction-identifies-new-susceptibility-regulatory-variants-for-osteoporosis
#3
Shi Yao, Yan Guo, Shan-Shan Dong, Ruo-Han Hao, Xiao-Feng Chen, Yi-Xiao Chen, Jia-Bin Chen, Qing Tian, Hong-Wen Deng, Tie-Lin Yang
Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants...
June 20, 2017: Human Genetics
https://www.readbyqxmd.com/read/28630441/overcoming-the-biological-aging-of-titanium-using-a-wet-storage-method-after-ultraviolet-treatment
#4
Sung-Hwan Choi, Won-Seok Jeong, Jung-Yul Cha, Jae-Hoon Lee, Kee-Joon Lee, Hyung-Seog Yu, Eun-Ha Choi, Kwang-Mahn Kim, Chung-Ju Hwang
We evaluated whether the biological activity of the surface of titanium, when stored in an aqueous solution after ultraviolet (UV) treatment, is comparable to that of the surface immediately after UV treatment. We subjected Grade IV titanium discs with machined surfaces to UV radiation for 15 min and then tested them immediately and after storage for 28 days, with and without distilled H2O (dH2O). We evaluated the surface characteristics using surface profiling, contact angle analysis, X-ray photoelectron spectroscopy, and in terms of the surface zeta-potential...
June 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28630410/investigating-the-life-expectancy-and-proteolytic-degradation-of-engineered-skeletal-muscle-biological-machines
#5
Caroline Cvetkovic, Meghan C Ferrall-Fairbanks, Eunkyung Ko, Lauren Grant, Hyunjoon Kong, Manu O Platt, Rashid Bashir
A combination of techniques from 3D printing, tissue engineering and biomaterials has yielded a new class of engineered biological robots that could be reliably controlled via applied signals. These machines are powered by a muscle strip composed of differentiated skeletal myofibers in a matrix of natural proteins, including fibrin, that provide physical support and cues to the cells as an engineered basement membrane. However, maintaining consistent results becomes challenging when sustaining a living system in vitro...
June 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28628894/small-molecule-probes-for-cellular-death-machines
#6
REVIEW
Ying Li, Lihui Qian, Junying Yuan
The past decade has witnessed a significant expansion of our understanding about the regulated cell death mechanisms beyond apoptosis. The application of chemical biological approaches had played a major role in driving these exciting discoveries. The discovery and use of small molecule probes in cell death research has not only revealed significant insights into the regulatory mechanism of cell death but also provided new drug targets and lead drug candidates for developing therapeutics of human diseases with huge unmet need...
June 16, 2017: Current Opinion in Chemical Biology
https://www.readbyqxmd.com/read/28628861/enhancement-in-recovery-of-drugs-with-high-protein-binding-efficiency-from-human-plasma-using-magnetic-nanoparticles
#7
Aniruddha Bhati, Rucha P Desai, C N Ramchand
In this paper, we propose an alternate method for bioanalytical extraction of drugs from human plasma samples using bare magnetic nanoparticles. The magnetic nanoparticles (MNPs) were used for deproteination of biological samples that further assist in extraction of plasma bound drugs for bioanalytical studies. The method uses basic solvents (ethanol, methanol, etc.) rather than the expensive and toxic solvents. The MNPs provide several advantages like avoiding the use of centrifuge machine, and making extraction time effective...
June 13, 2017: Journal of Pharmaceutical and Biomedical Analysis
https://www.readbyqxmd.com/read/28628855/progress-in-programming-spatiotemporal-patterns-and-machine-assembly-in-cell-free-protein-expression-systems
#8
REVIEW
Alexandra M Tayar, Shirley S Daube, Roy H Bar-Ziv
Building biological systems outside the cell is an emerging interdisciplinary research field aimed to study design principles, and to emulate biological functions for technology. Reconstructing programmable cellular functions, from assembly of protein/nucleic-acid machines to spatially distributed systems, requires implementing minimal systems of molecular interactions encoded in genes, source-sink protein expression dynamics, and materials platforms for reaction-diffusion scenarios. Here, we first review how molecular turnover mechanisms, combined with nonlinear interactions and feedback in cell-free gene networks enable programmable dynamic expression patterns in various compartments...
June 16, 2017: Current Opinion in Chemical Biology
https://www.readbyqxmd.com/read/28628219/application-of-swath-proteomics-to-mouse-biology
#9
Yibo Wu, Evan G Williams, Ruedi Aebersold
The quantitative measurement of the proteome has been shown to yield new insights into physiology and cell biology that cannot be determined from the genome and transcriptome because the quantitative relationship between transcriptome and proteome is complex. MS-based proteomics techniques, such as SWATH-MS, have recently advanced to the point at which they may be reliably applied by biologists who are not specialists in mass spectrometry. Here we provide standard protocols for preparation of tissue samples for input into the SWATH-MS analytical pipeline...
June 19, 2017: Current Protocols in Mouse Biology
https://www.readbyqxmd.com/read/28627775/ensemble-architecture-for-prediction-of-enzyme-ligand-binding-residues-using-evolutionary-information
#10
Priyadarshini P Pai, Rohit Kadam Dattatreya, Sukanta Mondal
Enzyme interactions with ligands are crucial for various biochemical reactions governing life. Over many years attempts to identify these residues for biotechnological manipulations have been made using experimental and computational techniques. The computational approaches have gathered impetus with the accruing availability of sequence and structure information, broadly classified into template-based and de novo methods. One of the predominant de novo methods using sequence information involves application of biological properties for supervised machine learning...
June 19, 2017: Molecular Informatics
https://www.readbyqxmd.com/read/28624633/from-flamingo-dance-to-desirable-drug-discovery-a-nature-inspired-approach
#11
REVIEW
Aminael Sánchez-Rodríguez, Yunierkis Pérez-Castillo, Stephan C Schürer, Orazio Nicolotti, Giuseppe Felice Mangiatordi, Fernanda Borges, M Natalia D S Cordeiro, Eduardo Tejera, José L Medina-Franco, Maykel Cruz-Monteagudo
The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a 'one-target fixation' to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies...
June 14, 2017: Drug Discovery Today
https://www.readbyqxmd.com/read/28623263/probing-spermiogenesis-a-digital-strategy-for-mouse-acrosome-classification
#12
Alessandro Taloni, Francesc Font-Clos, Luca Guidetti, Simone Milan, Miriam Ascagni, Chiara Vasco, Maria Enrica Pasini, Maria Rosa Gioria, Emilio Ciusani, Stefano Zapperi, Caterina A M La Porta
Classification of morphological features in biological samples is usually performed by a trained eye but the increasing amount of available digital images calls for semi-automatic classification techniques. Here we explore this possibility in the context of acrosome morphological analysis during spermiogenesis. Our method combines feature extraction from three dimensional reconstruction of confocal images with principal component analysis and machine learning. The method could be particularly useful in cases where the amount of data does not allow for a direct inspection by trained eye...
June 16, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28617230/predicting-anatomic-therapeutic-chemical-classification-codes-using-tiered-learning
#13
Thomas Olson, Rahul Singh
BACKGROUND: The low success rate and high cost of drug discovery requires the development of new paradigms to identify molecules of therapeutic value. The Anatomical Therapeutic Chemical (ATC) Code System is a World Health Organization (WHO) proposed classification that assigns multi-level codes to compounds based on their therapeutic, pharmacological and chemical characteristics as well as the in-vivo sites(s) of activity. The ability to predict ATC codes of compounds can assist in creation of high-quality chemical libraries for drug screening and in applications such as drug repositioning...
June 7, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28613237/major-depression-detection-from-eeg-signals-using-kernel-eigen-filter-bank-common-spatial-patterns
#14
Shih-Cheng Liao, Chien-Te Wu, Hao-Chuan Huang, Wei-Teng Cheng, Yi-Hung Liu
Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i...
June 14, 2017: Sensors
https://www.readbyqxmd.com/read/28608145/laundry-in-a-washing-machine-as-a-mediator-of-secondary-and-tertiary-dna-transfer
#15
Lev Voskoboinik, Merav Amiel, Ayeleth Reshef, Ron Gafny, Mark Barash
The aim of this work was to investigate the possibility of secondary and tertiary DNA transfer during laundry. The modes of transfer tested were mixed and separate laundry of worn and unworn garments in household and public washing machines. In addition, the possibility of a background DNA carry-over from a washing machine's drum was investigated. In the mixed (worn and unworn garments washed together) laundry experiment, 22% of samples from new unworn socks with no traceable DNA prior to experiment produced DNA profiles post-laundry...
June 12, 2017: International Journal of Legal Medicine
https://www.readbyqxmd.com/read/28606610/data-management-and-data-enrichment-for-systems-biology-projects
#16
Ulrike Wittig, Maja Rey, Andreas Weidemann, Wolfgang Müller
Collecting, curating, interlinking, and sharing high quality data are central to de.NBI-SysBio, the systems biology data management service center within the de.NBI network (German Network for Bioinformatics Infrastructure). The work of the center is guided by the FAIR principles for scientific data management and stewardship. FAIR stands for the four foundational principles Findability, Accessibility, Interoperability, and Reusability which were established to enhance the ability of machines to automatically find, access, exchange and use data...
June 9, 2017: Journal of Biotechnology
https://www.readbyqxmd.com/read/28606086/analysis-and-prediction-of-single-stranded-and-double-stranded-dna-binding-proteins-based-on-protein-sequences
#17
Wei Wang, Lin Sun, Shiguang Zhang, Hongjun Zhang, Jinling Shi, Tianhe Xu, Keliang Li
BACKGROUND: DNA-binding proteins perform important functions in a great number of biological activities. DNA-binding proteins can interact with ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA), and DNA-binding proteins can be categorized as single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). The identification of DNA-binding proteins from amino acid sequences can help to annotate protein functions and understand the binding specificity...
June 12, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28605369/real-time-tracking-of-dna-fragment-separation-by-smartphone
#18
Chunxian Tao, Bo Yang, Zhenqing Li, Dawei Zhang, Yoshinori Yamaguchi
Slab gel electrophoresis (SGE) is the most common method for the separation of DNA fragments; thus, it is broadly applied to the field of biology and others. However, the traditional SGE protocol is quite tedious, and the experiment takes a long time. Moreover, the chemical consumption in SGE experiments is very high. This work proposes a simple method for the separation of DNA fragments based on an SGE chip. The chip is made by an engraving machine. Two plastic sheets are used for the excitation and emission wavelengths of the optical signal...
June 1, 2017: Journal of Visualized Experiments: JoVE
https://www.readbyqxmd.com/read/28604872/predicting-protein-protein-interactions-from-protein-sequences-by-a-stacked-sparse-autoencoder-deep-neural-network
#19
Yan-Bin Wang, Zhu-Hong You, Xiao Li, Tong-Hai Jiang, Xing Chen, Xi Zhou, Lei Wang
Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibility to detect large-scale PPIs, these cannot be used to detect whole PPIs, and unreliable data may be generated. To solve this problem, in this study, a novel computational method was proposed to effectively predict the PPIs using the information of a protein sequence...
June 12, 2017: Molecular BioSystems
https://www.readbyqxmd.com/read/28601761/improving-virtual-screening-predictive-accuracy-of-human-kallikrein-5-inhibitors-using-machine-learning-models
#20
Xingang Fang, Sikha Bagui, Subhash Bagui
The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule...
May 29, 2017: Computational Biology and Chemistry
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