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Biological machines

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https://www.readbyqxmd.com/read/29346327/enhanced-isotopic-ratio-outlier-analysis-iroa-peak-detection-and-identification-with-ultra-high-resolution-gc-orbitrap-ms-potential-application-for-investigation-of-model-organism-metabolomes
#1
Yunping Qiu, Robyn D Moir, Ian M Willis, Suresh Seethapathy, Robert C Biniakewitz, Irwin J Kurland
Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG)...
January 18, 2018: Metabolites
https://www.readbyqxmd.com/read/29346099/cell-membrane-tracking-in-living-brain-tissue-using-differential-interference-contrast-microscopy
#2
John Lee, Ilya Kolb, Craig R Forest, Christopher J Rozell
Differential interference contrast (DIC) microscopy is widely used for observing unstained biological samples that are otherwise optically transparent. Combining this optical technique with machine vision could enable the automation of many life science experiments; however, identifying relevant features under DIC is challenging. In particular, precise tracking of cell boundaries in a thick ( ) slice of tissue has not previously been accomplished. We present a novel deconvolution algorithm that achieves the state-of-the-art performance at identifying and tracking these membrane locations...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29344895/bioinformatics-approaches-to-predict-drug-responses-from-genomic-sequencing
#3
Neel S Madhukar, Olivier Elemento
Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers...
2018: Methods in Molecular Biology
https://www.readbyqxmd.com/read/29340790/an-automated-framework-for-qsar-model-building
#4
Samina Kausar, Andre O Falcao
BACKGROUND: In-silico quantitative structure-activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling...
January 16, 2018: Journal of Cheminformatics
https://www.readbyqxmd.com/read/29338709/wormachine-machine-learning-based-phenotypic-analysis-tool-for-worms
#5
Adam Hakim, Yael Mor, Itai Antoine Toker, Amir Levine, Moran Neuhof, Yishai Markovitz, Oded Rechavi
BACKGROUND: Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis. RESULTS: We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation...
January 16, 2018: BMC Biology
https://www.readbyqxmd.com/read/29336552/engineering-the-surface-of-therapeutic-living-cells
#6
Jooyeon Park, Brenda Andrade, Yongbeom Seo, Myung-Joo Kim, Steven C Zimmerman, Hyunjoon Kong
Biological cells are complex living machines that have garnered significant attention for their potential to serve as a new generation of therapeutic and delivery agents. Because of their secretion, differentiation, and homing activities, therapeutic cells have tremendous potential to treat or even cure various diseases and injuries that have defied conventional therapeutic strategies. Therapeutic cells can be systemically or locally transplanted. In addition, with their ability to express receptors that bind specific tissue markers, cells are being studied as nano- or microsized drug carriers capable of targeted transport...
January 16, 2018: Chemical Reviews
https://www.readbyqxmd.com/read/29336514/machine-learned-analysis-of-quantitative-sensory-testing-responses-to-noxious-cold-stimulation-in-healthy-subjects
#7
I Weyer-Menkhoff, M C Thrun, J Lötsch
BACKGROUND: Pain in response to noxious cold has a complex molecular background probably involving several types of sensors. A recent observation has been the multimodal distribution of human cold pain thresholds. This study aimed at analysing reproducibility and stability of this observation and further exploration of data patterns supporting a complex background. METHOD: Pain thresholds to noxious cold stimuli (range 32-0 °C, tonic: temperature decrease -1 °C/s, phasic: temperature decrease -8 °C/s) were acquired in 148 healthy volunteers...
January 16, 2018: European Journal of Pain: EJP
https://www.readbyqxmd.com/read/29336158/chromosome-gene-orientations-inversion-networks-goins-of-plasmodium-proteome
#8
Viviana F Quevedo-Tumailli, Bernabe Ortega-Tenezaca, Humbert Gonzalez-Diaz
The spatial distribution of genes in chromosomes seems not to be random. For instance, only 10% of genes are transcribed from bidirectional promoters in humans and many more are organised in larger clusters. This raises intriguing questions asked by different authors before. We would like to add a few more questions in this context, related to gene orientation inversions. Does gene orientation (inversion) follow a random pattern? Is it relevant to biological activity somehow? In this paper, we define a new kind of network coined as the Gene Orientation Inversion Network (GOIN)...
January 16, 2018: Journal of Proteome Research
https://www.readbyqxmd.com/read/29331743/preliminary-investigation-of-human-exhaled-breath-for-tuberculosis-diagnosis-by-multidimensional-gas-chromatography-time-of-flight-mass-spectrometry-and-machine-learning
#9
Marco Beccaria, Theodore R Mellors, Jacky S Petion, Christiaan A Rees, Mavra Nasir, Hannah K Systrom, Jean W Sairistil, Marc-Antoine Jean-Juste, Vanessa Rivera, Kerline Lavoile, Patrice Severe, Jean W Pape, Peter F Wright, Jane E Hill
Tuberculosis (TB) remains a global public health malady that claims almost 1.8 million lives annually. Diagnosis of TB represents perhaps one of the most challenging aspects of tuberculosis control. Gold standards for diagnosis of active TB (culture and nucleic acid amplification) are sputum-dependent, however, in up to a third of TB cases, an adequate biological sputum sample is not readily available. The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, and non-invasive, and ready-available diagnostic service that could positively change TB detection...
January 4, 2018: Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences
https://www.readbyqxmd.com/read/29329334/ensemble-learning-method-for-the-prediction-of-new-bioactive-molecules
#10
Lateefat Temitope Afolabi, Faisal Saeed, Haslinda Hashim, Olutomilayo Olayemi Petinrin
Pharmacologically active molecules can provide remedies for a range of different illnesses and infections. Therefore, the search for such bioactive molecules has been an enduring mission. As such, there is a need to employ a more suitable, reliable, and robust classification method for enhancing the prediction of the existence of new bioactive molecules. In this paper, we adopt a recently developed combination of different boosting methods (Adaboost) for the prediction of new bioactive molecules. We conducted the research experiments utilizing the widely used MDL Drug Data Report (MDDR) database...
2018: PloS One
https://www.readbyqxmd.com/read/29329077/nano-hydroxyapatite-blasted-titanium-surface-creates-a-biointerface-able-to-govern-src-dependent-osteoblast-metabolism-as-prerequisite-to-ecm-remodeling
#11
Célio J C Fernandes, Fábio Bezerra, Marcel R Ferreira, Amanda F C Andrade, Thais Silva Pinto, Willian F Zambuzzi
Over the last several years, we have focused on the importance of intracellular signaling pathways in dynamically governing the biointerface between pre-osteoblast and surface of biomaterial. Thus, this study investigates the molecular hallmarks involved in the pre-osteoblast relationship with different topography considering Machined (Mc), Dual Acid-Etching (DAE), and nano hydroxyapatite-blasted (nHA) groups. There was substantial differences in topography of titanium surface, considering Atomic Force Microscopy and water contact angle (Mc = 81...
December 28, 2017: Colloids and Surfaces. B, Biointerfaces
https://www.readbyqxmd.com/read/29328377/feature-genes-in-metastatic-breast-cancer-identified-by-metade-and-svm-classifier-methods
#12
Youlin Tuo, Ning An, Ming Zhang
The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of P<0.05. Based on the protein‑protein interactions (PPIs) in the Biological General Repository for Interaction Datasets, Human Protein Reference Database and Biomolecular Interaction Network Database, the PPI network of the feature genes was constructed...
January 9, 2018: Molecular Medicine Reports
https://www.readbyqxmd.com/read/29323205/unsupervised-learning-and-pattern-recognition-of-biological-data-structures-with-density-functional-theory-and-machine-learning
#13
Chien-Chang Chen, Hung-Hui Juan, Meng-Yuan Tsai, Henry Horng-Shing Lu
By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems...
January 11, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29322919/hadamard-kernel-svm-with-applications-for-breast-cancer-outcome-predictions
#14
Hao Jiang, Wai-Ki Ching, Wai-Shun Cheung, Wenpin Hou, Hong Yin
BACKGROUND: Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation...
December 21, 2017: BMC Systems Biology
https://www.readbyqxmd.com/read/29316706/assessing-the-performances-of-protein-function-prediction-algorithms-from-the-perspectives-of-identification-accuracy-and-false-discovery-rate
#15
Chun Yan Yu, Xiao Xu Li, Hong Yang, Ying Hong Li, Wei Wei Xue, Yu Zong Chen, Lin Tao, Feng Zhu
The function of a protein is of great interest in the cutting-edge research of biological mechanisms, disease development and drug/target discovery. Besides experimental explorations, a variety of computational methods have been designed to predict protein function. Among these in silico methods, the prediction of BLAST is based on protein sequence similarity, while that of machine learning is also based on the sequence, but without the consideration of their similarity. This unique characteristic of machine learning makes it a good complement to BLAST and many other approaches in predicting the function of remotely relevant proteins and the homologous proteins of distinct function...
January 8, 2018: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/29312619/systematic-assessment-of-cervical-cancer-initiation-and-progression-uncovers-genetic-panels-for-deep-learning-based-early-diagnosis-and-proposes-novel-diagnostic-and-prognostic-biomarkers
#16
Nguyen Phuoc Long, Kyung Hee Jung, Sang Jun Yoon, Nguyen Hoang Anh, Tran Diem Nghi, Yun Pyo Kang, Hong Hua Yan, Jung Eun Min, Soon-Sun Hong, Sung Won Kwon
Although many outstanding achievements in the management of cervical cancer (CxCa) have obtained, it still imposes a major burden which has prompted scientists to discover and validate new CxCa biomarkers to improve the diagnostic and prognostic assessment of CxCa. In this study, eight different gene expression data sets containing 202 cancer, 115 cervical intraepithelial neoplasia (CIN), and 105 normal samples were utilized for an integrative systems biology assessment in a multi-stage carcinogenesis manner...
December 12, 2017: Oncotarget
https://www.readbyqxmd.com/read/29305594/egbmmda-extreme-gradient-boosting-machine-for-mirna-disease-association-prediction
#17
Xing Chen, Li Huang, Di Xie, Qi Zhao
Associations between microRNAs (miRNAs) and human diseases have been identified by increasing studies and discovering new ones is an ongoing process in medical laboratories. To improve experiment productivity, researchers computationally infer potential associations from biological data, selecting the most promising candidates for experimental verification. Predicting potential miRNA-disease association has become a research area of growing importance. This paper presents a model of Extreme Gradient Boosting Machine for MiRNA-Disease Association (EGBMMDA) prediction by integrating the miRNA functional similarity, the disease semantic similarity, and known miRNA-disease associations...
January 5, 2018: Cell Death & Disease
https://www.readbyqxmd.com/read/29305182/rbsurfpred-modeling-protein-accessible-surface-area-in-real-and-binary-space-using-regularized-and-optimized-regression
#18
Sumit Tarafder, Md Toukir Ahmed, Sumaiya Iqbal, Md Tamjidul Hoque, M Sohel Rahman
Accessible Surface Area (ASA) of a protein residue is an effective feature for protein structure prediction, binding region identification, fold recognition problems etc. Improving the prediction of ASA by the application of effective feature variables is a challenging but explorable task to consider, specially in the field of machine learning. Among the existing predictors of ASA, REGAd3p is a highly accurate ASA predictor which is based on regularized exact regression with polynomial kernel of degree 3. In this work, we present a new predictor RBSURFpred, which extends REGAd3p on several dimensions by incorporating 58 physicochemical, evolutionary and structural properties into 9-tuple peptides via Chou's general PseAAC, which allowed us to obtain higher accuracies in predicting both real-valued and binary ASA...
January 2, 2018: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/29305179/irspot-adpm-identify-recombination-spots-by-incorporating-the-associated-dinucleotide-product-model-into-chou-s-pseudo-components
#19
Lichao Zhang, Liang Kong
Gene recombination is a key process to produce hereditary differences. Recombination spot identification plays an important role in revealing genome evolution and promoting DNA function study. However, traditional experiments are not good at identifying recombination spot with huge amounts of DNA sequences springed up by sequencing. At present, some machine learning methods have been proposed to speed up this identification process. However, the correlations between nucleotides pairs at different positions along DNA sequence is often ignored, which reflects the important sequence order information...
January 2, 2018: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/29304774/assessment-of-the-association-between-increasing-membrane-pore-size-and-endotoxin-permeability-using-a-novel-experimental-dialysis-simulation-set-up
#20
Eva Schepers, Griet Glorieux, Sunny Eloot, Michael Hulko, Adriana Boschetti-de-Fierro, Werner Beck, Bernd Krause, Wim Van Biesen
BACKGROUND: Membranes with increasing pore size are introduced to enhance removal of large uremic toxins with regular hemodialysis. These membranes might theoretically have higher permeability for bacterial degradation products. In this paper, permeability for bacterial degradation products of membranes of comparable composition with different pore size was investigated with a new in vitro set-up that represents clinical flow and pressure conditions. METHODS: Dialysis was simulated with an AK200 machine using a low-flux, high-flux, medium cut-off (MCO) or high cut-off (HCO) device (n = 6/type)...
January 5, 2018: BMC Nephrology
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