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https://www.readbyqxmd.com/read/27906159/persistent-microbiome-alterations-modulate-the-rate-of-post-dieting-weight-regain
#1
Christoph A Thaiss, Shlomik Itav, Daphna Rothschild, Mariska Meijer, Maayan Levy, Claudia Moresi, Lenka Dohnalová, Sofia Braverman, Shachar Rozin, Sergey Malitsky, Mally Dori-Bachash, Yael Kuperman, Inbal Biton, Arieh Gertler, Alon Harmelin, Hagit Shapiro, Zamir Halpern, Asaph Aharoni, Eran Segal, Eran Elinav
In tackling the obesity pandemic, significant efforts are devoted to the development of effective weight reduction strategies, yet many dieting individuals fail to maintain a long-term weight reduction, and instead undergo excessive weight regain cycles. The mechanisms driving recurrent post-dieting obesity remain largely elusive. Here, we identify an intestinal microbiome signature that persists after successful dieting of obese mice, which contributes to faster weight regain and metabolic aberrations upon re-exposure to obesity-promoting conditions and transmits the accelerated weight regain phenotype upon inter-animal transfer...
November 24, 2016: Nature
https://www.readbyqxmd.com/read/27906033/the-role-of-the-backbone-torsion-in-protein-folding
#2
EDITORIAL
Irina Sorokina, Arcady Mushegian
BACKGROUND: The set of forces and sequence of events that govern the transition from an unfolded polypeptide chain to a functional protein with correct spatial structure remain incompletely known, despite the importance of the problem and decades of theory development, computer simulations, and laboratory experiments. Information about the correctly folded state of most proteins is likely to be present in their sequences, and yet many proteins fail to attain native structure after overexpression in a non-native environment or upon experimental denaturation and refolding...
December 1, 2016: Biology Direct
https://www.readbyqxmd.com/read/27905893/computational-prediction-of-multidisciplinary-team-decision-making-for-adjuvant-breast-cancer-drug-therapies-a-machine-learning-approach
#3
Frank P Y Lin, Adrian Pokorny, Christina Teng, Rachel Dear, Richard J Epstein
BACKGROUND: Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. METHODS: We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations...
December 1, 2016: BMC Cancer
https://www.readbyqxmd.com/read/27903825/synthetic-biology-routes-to-bio-artificial-intelligence
#4
REVIEW
Darren N Nesbeth, Alexey Zaikin, Yasushi Saka, M Carmen Romano, Claudiu V Giuraniuc, Oleg Kanakov, Tetyana Laptyeva
The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular 'teachers' and 'students' is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance...
November 30, 2016: Essays in Biochemistry
https://www.readbyqxmd.com/read/27903639/automated-classification-of-pain-perception-using-high-density-electroencephalography-data
#5
Gaurav Misra, Wei-En Wang, Derek B Archer, Arnab Roy, Stephen A Coombes
Translating brief millisecond-long pain-eliciting stimuli to the subjective perception of pain is associated with changes in theta, alpha, beta, and gamma oscillations over sensorimotor cortex. However, when a pain-eliciting stimulus continues for minutes, regions beyond the sensorimotor cortex such as the prefrontal cortex are also engaged. Abnormalities in prefrontal cortex have been associated with chronic pain states, but conventional millisecond-long EEG paradigms do not engage prefrontal regions. In the current study we collected high-density EEG data during an experimental paradigm in which subjects experienced a 4 second low or high intensity pain-eliciting stimulus...
November 30, 2016: Journal of Neurophysiology
https://www.readbyqxmd.com/read/27903489/finding-important-terms-for-patients-in-their-electronic-health-records-a-learning-to-rank-approach-using-expert-annotations
#6
Jinying Chen, Jiaping Zheng, Hong Yu
BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients' notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them...
November 30, 2016: JMIR Medical Informatics
https://www.readbyqxmd.com/read/27902695/text-mining-genotype-phenotype-relationships-from-biomedical-literature-for-database-curation-and-precision-medicine
#7
Ayush Singhal, Michael Simmons, Zhiyong Lu
The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature...
November 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/27901055/mediboost-a-patient-stratification-tool-for-interpretable-decision-making-in-the-era-of-precision-medicine
#8
Gilmer Valdes, José Marcio Luna, Eric Eaton, Charles B Simone, Lyle H Ungar, Timothy D Solberg
Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current decision tree algorithms, however, are consistently outperformed in accuracy by other, less-interpretable machine learning models, such as ensemble methods...
November 30, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27900948/prediction-of-antiepileptic-drug-treatment-outcomes-using-machine-learning
#9
Sinisa Colic, Robert G Wither, Min Lang, Liang Zhang, James H Eubanks, Berj L Bardakjian
OBJECTIVE: Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. APPROACH: Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome...
November 30, 2016: Journal of Neural Engineering
https://www.readbyqxmd.com/read/27899623/combining-transcription-factor-binding-affinities-with-open-chromatin-data-for-accurate-gene-expression-prediction
#10
Florian Schmidt, Nina Gasparoni, Gilles Gasparoni, Kathrin Gianmoena, Cristina Cadenas, Julia K Polansky, Peter Ebert, Karl Nordström, Matthias Barann, Anupam Sinha, Sebastian Fröhler, Jieyi Xiong, Azim Dehghani Amirabad, Fatemeh Behjati Ardakani, Barbara Hutter, Gideon Zipprich, Bärbel Felder, Jürgen Eils, Benedikt Brors, Wei Chen, Jan G Hengstler, Alf Hamann, Thomas Lengauer, Philip Rosenstiel, Jörn Walter, Marcel H Schulz
The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq...
November 29, 2016: Nucleic Acids Research
https://www.readbyqxmd.com/read/27899510/identification-and-in-silico-modeling-of-enhancers-reveals-new-features-of-the-cardiac-differentiation-network
#11
Denis Seyres, Yad Ghavi-Helm, Guillaume Junion, Ouarda Taghli-Lamallem, Céline Guichard, Laurence Röder, Charles Girardot, Eileen E M Furlong, Laurent Perrin
Developmental patterning and tissue formation are regulated through complex gene regulatory networks (GRNs) driven through the action of transcription factors (TFs) converging on enhancer elements. Here, as a point of entry to dissect the poorly defined GRN underlying cardiomyocyte differentiation, we apply an integrated approach to identify active enhancers and TFs involved in Drosophila heart development. The Drosophila heart consists of 104 cardiomyocytes, representing less than 0.5% of all cells in the embryo...
December 1, 2016: Development
https://www.readbyqxmd.com/read/27898976/development-and-validation-of-a-deep-learning-algorithm-for-detection-of-diabetic-retinopathy-in-retinal-fundus-photographs
#12
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C Nelson, Jessica L Mega, Dale R Webster
Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs...
November 29, 2016: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/27897236/recognition-of-mould-colony-on-unhulled-paddy-based-on-computer-vision-using-conventional-machine-learning-and-deep-learning-techniques
#13
Ke Sun, Zhengjie Wang, Kang Tu, Shaojin Wang, Leiqing Pan
To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models...
November 29, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27897007/production-of-a-preliminary-quality-control-pipeline-for-single-nuclei-rna-seq-and-its-application-in-the-analysis-of-cell-type-diversity-of-post-mortem-human-brain-neocortex
#14
Brian Aevermann, Jamison McCorrison, Pratap Venepally, Rebecca Hodge, Trygve Bakken, Jeremy Miller, Mark Novotny, Danny N Tran, Francisco Diezfuertes, Lena Christiansen, Fan Zhang, Frank Steemers, Roger S Lasken, E D Lein, Nicholas Schork, Richard H Scheuermann
Next generation sequencing of the RNA content of single cells or single nuclei (sc/nRNA-seq) has become a powerful approach to understand the cellular complexity and diversity of multicellular organisms and environmental ecosystems. However, the fact that the procedure begins with a relatively small amount of starting material, thereby pushing the limits of the laboratory procedures required, dictates that careful approaches for sample quality control (QC) are essential to reduce the impact of technical noise and sample bias in downstream analysis applications...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896982/predictive-modeling-of-hospital-readmission-rates-using-electronic-medical-record-wide-machine-learning-a-case-study-using-mount-sinai-heart-failure-cohort
#15
Khader Shameer, Kipp W Johnson, Alexandre Yahi, Riccardo Miotto, L I Li, Doran Ricks, Jebakumar Jebakaran, Patricia Kovatch, Partho P Sengupta, Sengupta Gelijns, Alan Moskovitz, Bruce Darrow, David L David, Andrew Kasarskis, Nicholas P Tatonetti, Sean Pinney, Joel T Dudley
Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896973/patterns-in-biomedical-data-how-do-we-find-them
#16
Anna O Basile, Anurag Verma, Marta Byrska-Bishop, Sarah A Pendergrass, Christian Darabos, H Lester Kirchner
Given the exponential growth of biomedical data, researchers are faced with numerous challenges in extracting and interpreting information from these large, high-dimensional, incomplete, and often noisy data. To facilitate addressing this growing concern, the "Patterns in Biomedical Data-How do we find them?" session of the 2017 Pacific Symposium on Biocomputing (PSB) is devoted to exploring pattern recognition using data-driven approaches for biomedical and precision medicine applications. The papers selected for this session focus on novel machine learning techniques as well as applications of established methods to heterogeneous data...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896969/reproducible-drug-repurposing-when-similarity-does-not-suffice
#17
Emre Guney
Repurposing existing drugs for new uses has attracted considerable attention over the past years. To identify potential candidates that could be repositioned for a new indication, many studies make use of chemical, target, and side effect similarity between drugs to train classifiers. Despite promising prediction accuracies of these supervised computational models, their use in practice, such as for rare diseases, is hindered by the assumption that there are already known and similar drugs for a given condition of interest...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896959/prosnet-integrating-homology-with-molecular-networks-for-protein-function-prediction
#18
Sheng Wang, Meng Qu, Jian Peng
Automated annotation of protein function has become a critical task in the post-genomic era. Network-based approaches and homology-based approaches have been widely used and recently tested in large-scale community-wide assessment experiments. It is natural to integrate network data with homology information to further improve the predictive performance. However, integrating these two heterogeneous, high-dimensional and noisy datasets is non-trivial. In this work, we introduce a novel protein function prediction algorithm ProSNet...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27896760/opportunities-and-challenges-of-multiplex-assays-a-machine-learning-perspective
#19
Junfang Chen, Emanuel Schwarz
Multiplex assays that allow the simultaneous measurement of multiple analytes in small sample quantities have developed into a widely used technology. Their implementation spans across multiple assay systems and can provide readouts of similar quality as the respective single-plex measures, albeit at far higher throughput. Multiplex assay systems are therefore an important element for biomarker discovery and development strategies but analysis of the derived data can face substantial challenges that may limit the possibility of identifying meaningful biological markers...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/27896759/identification-and-clinical-translation-of-biomarker-signatures-statistical-considerations
#20
Emanuel Schwarz
Powerful machine learning tools exist to extract biological patterns for diagnosis or prediction from high-dimensional datasets. Simultaneous advances in high-throughput profiling technologies have led to a rapid acceleration of biomarker discovery investigations across all areas of medicine. However, the translation of biomarker signatures into clinically useful tools has thus far been difficult. In this chapter, several important considerations are discussed that influence such translation in the context of classifier design...
2017: Methods in Molecular Biology
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