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https://www.readbyqxmd.com/read/28723659/data-driven-analysis-of-immune-infiltrate-in-a-large-cohort-of-breast-cancer-and-its-association-with-disease-progression-er-activity-and-genomic-complexity
#1
Ruth Dannenfelser, Marianne Nome, Andliena Tahiri, Josie Ursini-Siegel, Hans Kristian Moen Vollan, Vilde D Haakensen, Åslaug Helland, Bjørn Naume, Carlos Caldas, Anne-Lise Børresen-Dale, Vessela N Kristensen, Olga G Troyanskaya
The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry...
July 7, 2017: Oncotarget
https://www.readbyqxmd.com/read/28722399/bias-free-chemically-diverse-test-sets-from-machine-learning
#2
Ellen Swann, Michael Fernandez, Michelle L Coote, Amanda S Barnard
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarise large sets of nanoparticles however molecules are more diverse and not as easily characterised by descriptors. In this work we compare three sets of descriptors based on the one-, two- and three-dimensional structure of a molecule...
July 19, 2017: ACS Combinatorial Science
https://www.readbyqxmd.com/read/28720874/knowledge-transfer-learning-for-prediction-of-matrix-metalloprotease-substrate-cleavage-sites
#3
Yanan Wang, Jiangning Song, Tatiana T Marquez-Lago, André Leier, Chen Li, Trevor Lithgow, Geoffrey I Webb, Hong-Bin Shen
Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies have identified type-specific target substrates; however, the complete repertoire of MMP substrates remains uncharacterized. Indeed, computational prediction of substrate-cleavage sites associated with MMPs is a challenging problem. This holds especially true when considering MMPs with few experimentally verified cleavage sites, such as for MMP-2, -3, -7, and -8...
July 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28720869/rapid-bayesian-optimisation-for-synthesis-of-short-polymer-fiber-materials
#4
Cheng Li, David Rubín de Celis Leal, Santu Rana, Sunil Gupta, Alessandra Sutti, Stewart Greenhill, Teo Slezak, Murray Height, Svetha Venkatesh
The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative method which uses machine learning to optimise process development, incorporating multiple qualitative and quantitative objectives. We demonstrate the method with a novel fluid processing platform for synthesis of short polymer fibers, and show how the synthesis process can be efficiently directed to achieve material and process objectives...
July 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28720796/eeg-machine-learning-for-accurate-detection-of-cholinergic-intervention-and-alzheimer-s-disease
#5
Sonja Simpraga, Ricardo Alvarez-Jimenez, Huibert D Mansvelder, Joop M A van Gerven, Geert Jan Groeneveld, Simon-Shlomo Poil, Klaus Linkenkaer-Hansen
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electroencephalography (EEG) recordings to capture the brain's multi-faceted signature of disease or pharmacological intervention and use machine learning to improve classification performance. Using data from healthy subjects receiving scopolamine we developed an index of the muscarinic acetylcholine receptor antagonist (mAChR) consisting of 14 EEG biomarkers...
July 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28720710/application-of-response-surface-methods-to-determine-conditions-for-optimal-genomic-prediction
#6
Réka Howard, Alicia L Carriquiry, William D Beavis
An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits composed of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability)...
July 18, 2017: G3: Genes—Genomes—Genetics
https://www.readbyqxmd.com/read/28719743/machine-learning-on-samdi-mass-spectrometry-signal-to-noise-ratio-improves-peptide-array-designs
#7
Albert Yan Xue, Lindsey C Szymczak, Milan Mrksich, Neda Bagheri
Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal to noise ratios (S/N). These complex effects can lead to potentially unrepresentative signal intensities and can bias subsequent analyses. Within mass spectrometry-based peptide technologies, the relation between a peptide's amino acid sequence and S/N remains largely non-quantitative. To address this challenge, we present a method to quantify and analyze mass spectrometry S/N of two peptide arrays, and we use this analysis to portray quality of data and to design future arrays for SAMDI mass spectrometry...
July 18, 2017: Analytical Chemistry
https://www.readbyqxmd.com/read/28719206/direct-quantum-dynamics-using-grid-based-wavefunction-propagation-and-machine-learned-potential-energy-surfaces
#8
Gareth W Richings, Scott Habershon
We describe a method for performing nuclear quantum dynamics calculations using standard, grid-based algorithms, including the multi configurational time-dependent Hartree (MCTDH) method, where the potential energy surface (PES) is calculated ``on-the-fly''. The method of Gaussian process regression (GPR) is used to construct a global representation of the PES using values of the energy at points distributed in molecular configuration space during the course of the wavepacket propagation. We demonstrate this direct dynamics approach for both an analytical PES function describing 3-dimensional proton transfer dynamics in malonaldehyde, and for 2- and 6-dimensional quantum dynamics simulations of proton transfer in salicylaldimine...
July 18, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28719054/evolutionary-cell-biology-of-proteins-from-protists-to-humans-and-plants
#9
REVIEW
Helmut Plattner
During evolution, the cell as a fine-tuned machine had to undergo permanent adjustments to match changes in its environment, while "closed for repair work" was not possible. Evolution from protists (protozoa and unicellular algae) to multicellular organisms may have occurred in basically two lineages, Unikonta and Bikonta, culminating in mammals and angiosperms (flowering plants), respectively. Unicellular models for unikont evolution are myxamoebae (Dictyostelium) and increasingly also choanoflagellates, whereas, for bikonts, ciliates are preferred models...
July 18, 2017: Journal of Eukaryotic Microbiology
https://www.readbyqxmd.com/read/28718848/label-free-high-throughput-holographic-screening-and-enumeration-of-tumor-cells-in-blood
#10
Dhananjay Kumar Singh, Caroline C Ahrens, Wei Li, Siva A Vanapalli
We introduce inline digital holographic microscopy (in-line DHM) as a label-free technique for detecting tumor cells in blood. The optimized DHM platform fingerprints every cell flowing through a microchannel at 10 000 cells per second, based on three features - size, maximum intensity and mean intensity. To identify tumor cells in a background of blood cells, we developed robust gating criteria using machine-learning approaches. We established classifiers from the features extracted from 100 000-cell training sets consisting of red blood cells, peripheral blood mononuclear cells and tumor cell lines...
July 18, 2017: Lab on a Chip
https://www.readbyqxmd.com/read/28718087/speaking-two-languages-in-america-a-semantic-space-analysis-of-how-presidential-candidates-and-their-supporters-represent-abstract-political-concepts-differently
#11
Ping Li, Benjamin Schloss, D Jake Follmer
In this article we report a computational semantic analysis of the presidential candidates' speeches in the two major political parties in the USA. In Study One, we modeled the political semantic spaces as a function of party, candidate, and time of election, and findings revealed patterns of differences in the semantic representation of key political concepts and the changing landscapes in which the presidential candidates align or misalign with their parties in terms of the representation and organization of politically central concepts...
July 17, 2017: Behavior Research Methods
https://www.readbyqxmd.com/read/28717600/implementation-of-the-xpert-mtb-rif-assay-for-tuberculosis-in-mongolia-a-qualitative-exploration-of-barriers-and-enablers
#12
Nicole L Rendell, Solongo Bekhbat, Gantungalag Ganbaatar, Munkhjargal Dorjravdan, Madhukar Pai, Claudia C Dobler
OBJECTIVE: The aim of our study was to identify barriers and enablers to implementation of the Xpert MTB/RIF test within Mongolia's National Tuberculosis Program. METHODS: Twenty-foursemi-structured interviews were conducted between June and September 2015 with laboratory staff and tuberculosis physicians in Mongolia's capital Ulaanbaatar and regional towns where Xpert MTB/RIF testing had been implemented. Interviews were recorded, transcribed, translated and analysed thematically using NVIVO qualitative analysis software...
2017: PeerJ
https://www.readbyqxmd.com/read/28717555/adaptive-nonparametric-kinematic-modeling-of-concentric-tube-robots
#13
Georgios Fagogenis, Christos Bergeles, Pierre E Dupont
Concentric tube robots comprise telescopic precurved elastic tubes. The robot's tip and shape are controlled via relative tube motions, i.e. tube rotations and translations. Non-linear interactions between the tubes, e.g. friction and torsion, as well as uncertainty in the physical properties of the tubes themselves, e.g. the Young's modulus, curvature, or stiffness, hinder accurate kinematic modelling. In this paper, we present a machine-learning-based methodology for kinematic modelling of concentric tube robots and in situ model adaptation...
October 2016: Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems
https://www.readbyqxmd.com/read/28716716/fiber-tractography-using-machine-learning
#14
Peter F Neher, Marc-Alexandre Côté, Jean-Christophe Houde, Maxime Descoteaux, Klaus H Maier-Hein
We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography...
July 14, 2017: NeuroImage
https://www.readbyqxmd.com/read/28716627/membrane-proteins-structures-a-review-on-computational-modeling-tools
#15
REVIEW
Jose G Almeida, Antonio J Preto, Panagiotis I Koukos, Alexandre M J J Bonvin, Irina S Moreira
BACKGROUND: Membrane proteins (MPs) play diverse and important functions in living organisms. They constitute 20% to 30% of the known bacterial, archaean and eukaryotic organisms' genomes. In humans, their importance is emphasized as they represent 50% of all known drug targets. Nevertheless, experimental determination of their three-dimensional (3D) structure has proven to be both time consuming and rather expensive, which has led to the development of computational algorithms to complement the available experimental methods and provide valuable insights...
July 14, 2017: Biochimica et Biophysica Acta
https://www.readbyqxmd.com/read/28716036/short-dna-sequence-patterns-accurately-identify-broadly-active-human-enhancers
#16
Laura L Colbran, Ling Chen, John A Capra
BACKGROUND: Enhancers are DNA regulatory elements that influence gene expression. There is substantial diversity in enhancers' activity patterns: some enhancers drive expression in a single cellular context, while others are active across many. Sequence characteristics, such as transcription factor (TF) binding motifs, influence the activity patterns of regulatory sequences; however, the regulatory logic through which specific sequences drive enhancer activity patterns is poorly understood...
July 17, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28716018/prediction-of-extubation-readiness-in-extremely-preterm-infants-by-the-automated-analysis-of-cardiorespiratory-behavior-study-protocol
#17
Wissam Shalish, Lara J Kanbar, Smita Rao, Carlos A Robles-Rubio, Lajos Kovacs, Sanjay Chawla, Martin Keszler, Doina Precup, Karen Brown, Robert E Kearney, Guilherme M Sant'Anna
BACKGROUND: Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with increased morbidities...
July 17, 2017: BMC Pediatrics
https://www.readbyqxmd.com/read/28715343/deep-belief-networks-for-electroencephalography-a-review-of-recent-contributions-and-future-outlooks
#18
Faezeh Movahedi, James L Coyle, Ervin Sejdic
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state of- the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28715336/combination-of-supervised-and-unsupervised-approaches-for-mirna-target-prediction
#19
Nafiseh Sedaghat, Mahmood Fathy, Mohammad Hossein Modarressi, Ali Shojaie
MicroRNAs (miRNAs) are short non-coding RNAs which target mRNAs by binding to them and regulating their expression. Involvement of miRNAs has been discovered in many diseases, so it is fruitful to investigate the miRNAs and their targets to develop new therapeutic ways by designing anti-miRNA oligonucleotides. There are various computational methods to predict the target genes, however, their precisions are not good enough. In this paper, we apply a two-step approach to refine the results of sequence-based prediction algorithms...
July 13, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28715325/inference-based-similarity-search-in-randomized-montgomery-domains-for-privacy-preserving-biometric-identification
#20
Yi Wang, Jianwu Wan, Jun Guo, Yiu-Ming Cheung, Pong C Yuen
Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability...
July 14, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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