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G Damian Brusko, John Paul G Kolcun, Michael Y Wang
No abstract text is available yet for this article.
July 1, 2018: Neurosurgery
Rawan S Olayan, Haitham Ashoor, Vladimir B Bajic
No abstract text is available yet for this article.
June 15, 2018: Bioinformatics
Callum J Court, Jacqueline M Cole
Large auto-generated databases of magnetic materials properties have the potential for great utility in materials science research. This article presents an auto-generated database of 39,822 records containing chemical compounds and their associated Curie and Néel magnetic phase transition temperatures. The database was produced using natural language processing and semi-supervised quaternary relationship extraction, applied to a corpus of 68,078 chemistry and physics articles. Evaluation of the database shows an estimated overall precision of 73%...
June 19, 2018: Scientific Data
A Meyer-Lindenberg
Artificial intelligence and the underlying methods of machine learning and neuronal networks (NN) have made dramatic progress in recent years and have allowed computers to reach superhuman performance in domains that used to be thought of as uniquely human. In this overview, the underlying methodological developments that made this possible are briefly delineated and then the applications to psychiatry in three domains are discussed: precision medicine and biomarkers, natural language processing and artificial intelligence-based psychotherapeutic interventions...
June 18, 2018: Der Nervenarzt
Fan Feng, Luhua Lai, Jianfeng Pei
With the idea of retrosynthetic analysis, which was raised in the 1960s, chemical synthesis analysis and pathway design have been transformed from a complex problem to a regular process of structural simplification. This review aims to summarize the developments of computer-assisted synthetic analysis and design in recent years, and how machine-learning algorithms contributed to them. LHASA system started the pioneering work of designing semi-empirical reaction modes in computers, with its following rule-based and network-searching work not only expanding the databases, but also building new approaches to indicating reaction rules...
2018: Frontiers in Chemistry
Dalong Sun, Jing Chen, Longzi Liu, Guangxi Zhao, Pingping Dong, Bingrui Wu, Jun Wang, Ling Dong
A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM) stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD) prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA). The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets: GSE39582 includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics; GSE17538 is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients...
2018: PeerJ
Ling Hao, Jingxin Wang, David Page, Sanjay Asthana, Henrik Zetterberg, Cynthia Carlsson, Ozioma C Okonkwo, Lingjun Li
Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction...
June 18, 2018: Scientific Reports
Julie-Myrtille Bourgognon, Jereme G Spiers, Hannah Scheiblich, Alexey Antonov, Sophie J Bradley, Andrew B Tobin, Joern R Steinert
Neurodegenerative conditions are characterised by a progressive loss of neurons, which is believed to be initiated by misfolded protein aggregations. During this time period, many physiological and metabolomic alterations and changes in gene expression contribute to the decline in neuronal function. However, these pathological effects have not been fully characterised. In this study, we utilised a metabolomic approach to investigate the metabolic changes occurring in the hippocampus and cortex of mice infected with misfolded prion protein...
June 18, 2018: Cell Death and Differentiation
Michael G Walsh, Allard W de Smalen, Siobhan M Mor
Climate change is impacting ecosystem structure and function, with potentially drastic downstream effects on human and animal health. Emerging zoonotic diseases are expected to be particularly vulnerable to climate and biodiversity disturbance. Anthrax is an archetypal zoonosis that manifests its most significant burden on vulnerable pastoralist communities. The current study sought to investigate the influence of temperature increases on geographic anthrax suitability in the temperate, boreal, and arctic North, where observed climate impact has been rapid...
June 18, 2018: Scientific Reports
Taibo Li, April Kim, Joseph Rosenbluh, Heiko Horn, Liraz Greenfeld, David An, Andrew Zimmer, Arthur Liberzon, Jon Bistline, Ted Natoli, Yang Li, Aviad Tsherniak, Rajiv Narayan, Aravind Subramanian, Ted Liefeld, Bang Wong, Dawn Thompson, Sarah Calvo, Steve Carr, Jesse Boehm, Jake Jaffe, Jill Mesirov, Nir Hacohen, Aviv Regev, Kasper Lage
Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.
June 18, 2018: Nature Methods
Marta Ghio, Karolin Haegert, Matilde M Vaghi, Marco Tettamanti
We rarely use abstract and concrete concepts in isolation but rather embedded within a linguistic context. To examine the modulatory impact of the linguistic context on conceptual processing, we isolated the case of sentential negation polarity, in which an interaction occurs between the syntactic operator not and conceptual information in the negation's scope. Previous studies suggested that sentential negation of concrete action-related concepts modulates activation in the fronto-parieto-temporal action representation network...
August 5, 2018: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
Ashish Yeri, Ravi V Shah
No abstract text is available yet for this article.
June 2018: Circulation. Cardiovascular Imaging
James K Min
No abstract text is available yet for this article.
June 2018: Circulation. Cardiovascular Imaging
Adriaan Coenen, Young-Hak Kim, Mariusz Kruk, Christian Tesche, Jakob De Geer, Akira Kurata, Marisa L Lubbers, Joost Daemen, Lucian Itu, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Anders Persson, U Joseph Schoepf, Cezary Kepka, Dong Hyun Yang, Koen Nieman
BACKGROUND: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding...
June 2018: Circulation. Cardiovascular Imaging
Lei Feng, Susu Zhu, Fucheng Lin, Zhenzhu Su, Kangpei Yuan, Yiying Zhao, Yong He, Chu Zhang
Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths...
June 15, 2018: Sensors
Shenglong Yu, Hong Zhao
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection...
2018: PloS One
Kévin Vervier, Jacob J Michaelson
Motivation: Model-based estimates of general deleteriousness, like CADD, DANN or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these approaches say little about the tissues in which the effects of deleterious variants will be most meaningful. Tissue-specific annotations have been recently inferred for dozens of tissues/cell types from large collections of cross-tissue epigenomic data, and have demonstrated sensitivity in predicting affected tissues in complex traits...
April 18, 2018: Bioinformatics
Gaojing Wang, Qingquan Li, Lei Wang, Wei Wang, Mengqi Wu, Tao Liu
Human activity recognition (HAR) is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition...
June 18, 2018: Sensors
Vincent X Liu
No abstract text is available yet for this article.
July 2018: Critical Care Medicine
Pornchai Phukpattaranont, Sirinee Thongpanja, Khairul Anam, Adel Al-Jumaily, Chusak Limsakul
Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated...
June 18, 2018: Medical & Biological Engineering & Computing
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