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Deep learning

An Tang, Roger Tam, Alexandre Cadrin-Chênevert, Will Guest, Jaron Chong, Joseph Barfett, Leonid Chepelev, Robyn Cairns, J Ross Mitchell, Mark D Cicero, Manuel Gaudreau Poudrette, Jacob L Jaremko, Caroline Reinhold, Benoit Gallix, Bruce Gray, Raym Geis
Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition...
April 11, 2018: Canadian Association of Radiologists Journal, Journal L'Association Canadienne des Radiologistes
Francesco Bagattini, Fabio Schoen, Luca Tigli
In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space...
April 14, 2018: Journal of Chemical Physics
Aoran Xiao, Zhongyuan Wang, Lei Wang, Yexian Ren
Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image...
April 13, 2018: Sensors
Chien-Chih Wang, Kent Loong Tan, Chun-Ting Chen, Yu-Hsiang Lin, S Sathiya Keerthi, Dhruv Mahajan, S Sundararajan, Chih-Jen Lin
Deep learning involves a difficult nonconvex optimization problem with a large number of weights between any two adjacent layers of a deep structure. To handle large data sets or complicated networks, distributed training is needed, but the calculation of function, gradient, and Hessian is expensive. In particular, the communication and the synchronization cost may become a bottleneck. In this letter, we focus on situations where the model is distributedly stored and propose a novel distributed Newton method for training deep neural networks...
April 13, 2018: Neural Computation
Brian S Freeman, Graham Taylor, Bahram Gharabaghi, Jesse Thé
Implications Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction...
April 13, 2018: Journal of the Air & Waste Management Association
Sayantan G, Kien P T, Kadambari K V
A new semi-supervised approach based on deep learning and active learning for classification of electrocardiogram signals (ECG) is proposed. The objective of the proposed work is to model a scientific method for classification of cardiac irregularities using electrocardiogram beats. The model follows the Association for the Advancement of medical instrumentation (AAMI) standards and consists of three phases. In phase I, feature representation of ECG is learnt using Gaussian-Bernoulli deep belief network followed by a linear support vector machine (SVM) training in the consecutive phase...
April 12, 2018: Medical & Biological Engineering & Computing
Suqing Zheng, Mengying Jiang, Chengwei Zhao, Rui Zhu, Zhicheng Hu, Yong Xu, Fu Lin
In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis...
2018: Frontiers in Chemistry
Katsumi Hagita, Takeshi Higuchi, Hiroshi Jinnai
Scanning electron microscopy equipped with a focused ion beam (FIB-SEM) is a promising three-dimensional (3D) imaging technique for nano- and meso-scale morphologies. In FIB-SEM, the specimen surface is stripped by an ion beam and imaged by an SEM installed orthogonally to the FIB. The lateral resolution is governed by the SEM, while the depth resolution, i.e., the FIB milling direction, is determined by the thickness of the stripped thin layer. In most cases, the lateral resolution is superior to the depth resolution; hence, asymmetric resolution is generated in the 3D image...
April 12, 2018: Scientific Reports
Takeshi Fujiwara, Mayumi Kamada, Yasushi Okuno
According to the increase of data generated from analytical instruments, application of artificial intelligence(AI)technology in medical field is indispensable. In particular, practical application of AI technology is strongly required in "genomic medicine" and "genomic drug discovery" that conduct medical practice and novel drug development based on individual genomic information. In our laboratory, we have been developing a database to integrate genome data and clinical information obtained by clinical genome analysis and a computational support system for clinical interpretation of variants using AI...
April 2018: Gan to Kagaku Ryoho. Cancer & Chemotherapy
Anabik Pal, Utpal Garain, Aditi Chandra, Raghunath Chatterjee, Swapan Senapati
BACKGROUND AND OBJECTIVE: Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images...
June 2018: Computer Methods and Programs in Biomedicine
Mercedes Arguello Casteleiro, George Demetriou, Warren Read, Maria Jesus Fernandez Prieto, Nava Maroto, Diego Maseda Fernandez, Goran Nenadic, Julie Klein, John Keane, Robert Stevens
BACKGROUND: Automatic identification of term variants or acceptable alternative free-text terms for gene and protein names from the millions of biomedical publications is a challenging task. Ontologies, such as the Cardiovascular Disease Ontology (CVDO), capture domain knowledge in a computational form and can provide context for gene/protein names as written in the literature. This study investigates: 1) if word embeddings from Deep Learning algorithms can provide a list of term variants for a given gene/protein of interest; and 2) if biological knowledge from the CVDO can improve such a list without modifying the word embeddings created...
April 12, 2018: Journal of Biomedical Semantics
Muhammad Khalid Khan Niazi, Thomas Erol Tavolara, Vidya Arole, Douglas J Hartman, Liron Pantanowitz, Metin N Gurcan
The World Health Organization (WHO) has clear guidelines regarding the use of Ki67 index in defining the proliferative rate and assigning grade for pancreatic neuroendocrine tumor (NET). WHO mandates the quantification of Ki67 index by counting at least 500 positive tumor cells in a hotspot. Unfortunately, Ki67 antibody may stain both tumor and non-tumor cells as positive depending on the phase of the cell cycle. Likewise, the counter stain labels both tumor and non-tumor as negative. This non-specific nature of Ki67 stain and counter stain therefore hinders the exact quantification of Ki67 index...
2018: PloS One
ZeYu Wang, YanXia Wu, ShuHui Bu, PengCheng Han, GuoYin Zhang
Explicit structural inference is one key point to improve the accuracy of scene parsing. Meanwhile, adversarial training method is able to reinforce spatial contiguity in output segmentations. To take both advantages of the structural learning and adversarial training simultaneously, we propose a novel deep learning network architecture called Structural Inference Embedded Adversarial Networks (SIEANs) for pixel-wise scene labeling. The generator of our SIEANs, a novel designed scene parsing network, makes full use of convolutional neural networks and long short-term memory networks to learn the global contextual information of objects in four different directions from RGB-(D) images, which is able to describe the (three-dimensional) spatial distributions of objects in a more comprehensive and accurate way...
2018: PloS One
Amalio Telenti, Christoph Lippert, Pi-Chuan Chang, Mark DePristo
The human genome is now investigated through high throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits (eg. deleterious variants and disease). This review summarizes lessons learned from the large-scale analyses of genome and exome datasets, modeling of population data and machine learning strategies to solve complex genomic sequence regions. The review also portrays the rapid adoption of artificial intelligence/deep neural networks in genomics; in particular, deep learning approaches are well suited to model the complex dependencies in the regulatory landscape of the genome, and to provide predictors for genetic variant calling and interpretation...
April 10, 2018: Human Molecular Genetics
Haotian Teng, Minh Duc Cao, Michael B Hall, Tania Duarte, Sheng Wang, Lachlan J M Coin
Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling: directly translating the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4000 reads, we show that our model provides state-of-the-art basecalling accuracy even on previously unseen species...
April 10, 2018: GigaScience
Michael K K Leung, Andrew Delong, Brendan J Frey
Motivation: Processing of transcripts at the 3'-end involves cleavage at a polyadenylation site followed by the addition of a poly(A)-tail. By selecting which site is cleaved, the process of alternative polyadenylation enables genes to produce transcript isoforms with different 3'-ends. To facilitate the identification and treatment of disease-causing mutations that affect polyadenylation and to understand the sequence determinants underlying this regulatory process, a computational model that can accurately predict polyadenylation patterns from genomic features is desirable...
April 10, 2018: Bioinformatics
Russell A Brewer, Sarah Chrestman, Snigdha Mukherjee, Karen E Mason, Typhanye V Dyer, Peter Gamache, Mary Moore, DeAnn Gruber
We explored the correlates of linkage to HIV medical care and barriers to HIV care among PLWH in Louisiana. Of the 998 participants enrolled, 85.8% were successfully linked to HIV care within 3 months. The majority of participants were male (66.2%), African American (81.6%), and had limited education (74.4%). Approximately 22% of participants were Black gay and bisexual men. The most common reported barrier to care was lack of transportation (27.1%). Multivariable analysis revealed that compared with Black gay and bisexual men, White gay and bisexual men were significantly more likely to be linked to HIV care (adjusted prevalence ratio, aPR 1...
April 11, 2018: AIDS and Behavior
Muliani Joewono, I Nyoman Mangku Karmaya, Gede Wirata, Yuliana, I Gusti Ayu Widianti, I Nyoman Gede Wardana
The Chinese philosophy of Confucianism said "What I heard I forgot, what I see, I remember, what I do, I understand." During this time, most of the teaching and learning process relies on viewing and listening modalities only. As a result, much information does not last long in memory as well as the material understanding achieves became less deep. In studying anatomy science, drawing is one of effective important methods because it is an integration of ideas and knowledge of vision thereby increasing comprehension and learning motivation of college students...
March 2018: Anatomy & Cell Biology
Zhaodi Wang, Menghan Hu, Guangtao Zhai
Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers...
April 7, 2018: Sensors
Josip S Herman, Sagar, Dominic Grün
To understand stem cell differentiation along multiple lineages, it is necessary to resolve heterogeneous cellular states and the ancestral relationships between them. We developed a robotic miniaturized CEL-Seq2 implementation to carry out deep single-cell RNA-seq of ∼2,000 mouse hematopoietic progenitors enriched for lymphoid lineages, and used an improved clustering algorithm, RaceID3, to identify cell types. To resolve subtle transcriptome differences indicative of lineage biases, we developed FateID, an iterative supervised learning algorithm for the probabilistic quantification of cell fate bias in progenitor populations...
April 9, 2018: Nature Methods
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