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https://www.readbyqxmd.com/read/29667823/luciferase-advisor-high-accuracy-model-to-flag-false-positive-hits-in-luciferase-hts-assays
#1
Dipan Ghosh, Uwe Koch, Kamyar Hadian, Michael Sattler, Igor V Tetko
Firefly luciferase is an enzyme that has found ubiquitous use in biological assays in high-throughput screening (HTS) campaigns. The inhibition of luciferase in such assays could lead to a false positive result. This issue has been known for a long time, and there have been significant efforts to identify luciferase inhibitors in order to enhance recognition of false positives in screening assays. However, although a large amount of publicly accessible luciferase counterscreen data is available, to date little effort has been devoted to building a chemoinformatic model that can identify such molecules in a given data set...
April 18, 2018: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/29667359/predicting-responses-to-mechanical-ventilation-for-preterm-infants-with-acute-respiratory-illness-using-artificial-neural-networks
#2
Katharine Brigham, Samir Gupta, John C Brigham
Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real-time, based on ventilation monitoring up to the point of analysis...
April 17, 2018: International Journal for Numerical Methods in Biomedical Engineering
https://www.readbyqxmd.com/read/29667323/clades-a-classification-based-machine-learning-method-for-species-delimitation-from-population-genetic-data
#3
Jingwen Pei, Chong Chu, Xin Li, Bin Lu, Yufeng Wu
Species are considered to be the basic unit of ecological and evolutionary studies. Since multi-locus genomic data are increasingly available there has been considerable interests in the use of DNA sequence data to delimit species. In this paper, we show that machine learning can be used for species delimitation. Our method treats the species delimitation problem as a classification problem for identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purposes...
April 18, 2018: Molecular Ecology Resources
https://www.readbyqxmd.com/read/29667255/nonvolatile-memory-materials-for-neuromorphic-intelligent-machines
#4
REVIEW
Doo Seok Jeong, Cheol Seong Hwang
Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner...
April 18, 2018: Advanced Materials
https://www.readbyqxmd.com/read/29666662/prediction-of-gpcr-ligand-binding-using-machine-learning-algorithms
#5
Sangmin Seo, Jonghwan Choi, Soon Kil Ahn, Kil Won Kim, Jaekwang Kim, Jaehyuck Choi, Jinho Kim, Jaegyoon Ahn
We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good ligand-receptor binding...
2018: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/29666265/an-explainable-deep-machine-vision-framework-for-plant-stress-phenotyping
#6
Sambuddha Ghosal, David Blystone, Asheesh K Singh, Baskar Ganapathysubramanian, Arti Singh, Soumik Sarkar
Current approaches for accurate identification, classification, and quantification of biotic and abiotic stresses in crop research and production are predominantly visual and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intrarater cognitive variability. This translates to erroneous decisions and a significant waste of resources. Here, we demonstrate a machine learning framework's ability to identify and classify a diverse set of foliar stresses in soybean [ Glycine max (L...
April 16, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29666041/secure-logistic-regression-based-on-homomorphic-encryption-design-and-evaluation
#7
Miran Kim, Yongsoo Song, Shuang Wang, Yuhou Xia, Xiaoqian Jiang
BACKGROUND: Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis...
April 17, 2018: JMIR Medical Informatics
https://www.readbyqxmd.com/read/29665781/machine-learning-methods-reveal-the-temporal-pattern-of-dengue-incidence-using-meteorological-factors-in-metropolitan-manila-philippines
#8
Thaddeus M Carvajal, Katherine M Viacrusis, Lara Fides T Hernandez, Howell T Ho, Divina M Amalin, Kozo Watanabe
BACKGROUND: Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting...
April 17, 2018: BMC Infectious Diseases
https://www.readbyqxmd.com/read/29665779/a-machine-learning-model-to-determine-the-accuracy-of-variant-calls-in-capture-based-next-generation-sequencing
#9
Jeroen van den Akker, Gilad Mishne, Anjali D Zimmer, Alicia Y Zhou
BACKGROUND: Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist...
April 17, 2018: BMC Genomics
https://www.readbyqxmd.com/read/29665537/mass-detection-in-digital-breast-tomosynthesis-data-using-convolutional-neural-networks-and-multiple-instance-learning
#10
Mina Yousefi, Adam Krzyżak, Ching Y Suen
Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN)...
April 12, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29664902/a-general-prediction-model-for-the-detection-of-adhd-and-autism-using-structural-and-functional-mri
#11
Bhaskar Sen, Neil C Borle, Russell Greiner, Matthew R G Brown
This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder...
2018: PloS One
https://www.readbyqxmd.com/read/29664708/adaptive-and-resilient-soft-tensegrity-robots
#12
John Rieffel, Jean-Baptiste Mouret
Living organisms intertwine soft (e.g., muscle) and hard (e.g., bones) materials, giving them an intrinsic flexibility and resiliency often lacking in conventional rigid robots. The emerging field of soft robotics seeks to harness these same properties to create resilient machines. The nature of soft materials, however, presents considerable challenges to aspects of design, construction, and control-and up until now, the vast majority of gaits for soft robots have been hand-designed through empirical trial-and-error...
April 17, 2018: Soft Robotics
https://www.readbyqxmd.com/read/29664470/curated-compendium-of-human-transcriptional-biomarker-data
#13
Nathan P Golightly, Avery Bell, Anna I Bischoff, Parker D Hollingsworth, Stephen R Piccolo
One important use of genome-wide transcriptional profiles is to identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for clinical application. Data from thousands of translational-biomarker studies have been deposited in public repositories, enabling reuse. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways...
April 17, 2018: Scientific Data
https://www.readbyqxmd.com/read/29664466/points-of-significance-machine-learning-a-primer
#14
Danilo Bzdok, Martin Krzywinski, Naomi Altman
No abstract text is available yet for this article.
November 30, 2017: Nature Methods
https://www.readbyqxmd.com/read/29664143/pathway-aggregation-for-survival-prediction-via-multiple-kernel-learning
#15
Jennifer A Sinnott, Tianxi Cai
Attempts to predict prognosis in cancer patients using high-dimensional genomic data such as gene expression in tumor tissue can be made difficult by the large number of features and the potential complexity of the relationship between features and the outcome. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways and networks reduces the dimensionality of the problem and could improve prediction accuracy. Additionally, such knowledge-based models may be more biologically grounded and interpretable...
April 17, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/29663099/feasibility-study-of-stain-free-classification-of-cell-apoptosis-based-on-diffraction-imaging-flow-cytometry-and-supervised-machine-learning-techniques
#16
Jingwen Feng, Tong Feng, Chengwen Yang, Wei Wang, Yu Sa, Yuanming Feng
This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining...
April 16, 2018: Apoptosis: An International Journal on Programmed Cell Death
https://www.readbyqxmd.com/read/29662954/crystal-nucleation-in-metallic-alloys-using-x-ray-radiography-and-machine-learning
#17
Enzo Liotti, Carlos Arteta, Andrew Zisserman, Andrew Lui, Victor Lempitsky, Patrick S Grant
The crystallization of solidifying Al-Cu alloys over a wide range of conditions was studied in situ by synchrotron x-ray radiography, and the data were analyzed using a computer vision algorithm trained using machine learning. The effect of cooling rate and solute concentration on nucleation undercooling, crystal formation rate, and crystal growth rate was measured automatically for thousands of separate crystals, which was impossible to achieve manually. Nucleation undercooling distributions confirmed the efficiency of extrinsic grain refiners and gave support to the widely assumed free growth model of heterogeneous nucleation...
April 2018: Science Advances
https://www.readbyqxmd.com/read/29662953/accelerated-discovery-of-metallic-glasses-through-iteration-of-machine-learning-and-high-throughput-experiments
#18
Fang Ren, Logan Ward, Travis Williams, Kevin J Laws, Christopher Wolverton, Jason Hattrick-Simpers, Apurva Mehta
With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method-dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary...
April 2018: Science Advances
https://www.readbyqxmd.com/read/29662628/a-gene-expression-signature-predicts-recurrence-free-survival-in-meningioma
#19
Adriana Olar, Lindsey D Goodman, Khalida M Wani, Nicholas S Boehling, Devi S Sharma, Reema R Mody, Joy Gumin, Elizabeth B Claus, Frederick F Lang, Timothy F Cloughesy, Albert Lai, Kenneth D Aldape, Franco DeMonte, Erik P Sulman
BACKGROUND: Meningioma is the most common primary brain tumor and has a variable risk of local recurrence. While World Health Organization (WHO) grade generally correlates with recurrence, there is substantial within-grade variation of recurrence risk. Current risk stratification does not accurately predict which patients are likely to benefit from adjuvant radiation therapy (RT). We hypothesized that tumors at risk for recurrence have unique gene expression profiles (GEP) that could better select patients for adjuvant RT...
March 23, 2018: Oncotarget
https://www.readbyqxmd.com/read/29662210/genome-wide-mutant-profiling-predicts-the-mechanism-of-a-lipid-ii-binding-antibiotic
#20
Marina Santiago, Wonsik Lee, Antoine Abou Fayad, Kathryn A Coe, Mithila Rajagopal, Truc Do, Fabienne Hennessen, Veerasak Srisuknimit, Rolf Müller, Timothy C Meredith, Suzanne Walker
Identifying targets of antibacterial compounds remains a challenging step in the development of antibiotics. We have developed a two-pronged functional genomics approach to predict mechanism of action that uses mutant fitness data from antibiotic-treated transposon libraries containing both upregulation and inactivation mutants. We treated a Staphylococcus aureus transposon library containing 690,000 unique insertions with 32 antibiotics. Upregulation signatures identified from directional biases in insertions revealed known molecular targets and resistance mechanisms for the majority of these...
April 16, 2018: Nature Chemical Biology
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