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Locality sensitive hashing

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https://www.readbyqxmd.com/read/29442040/lidocaine-sensitizes-the-cytotoxicity-of-5-fluorouacil-in-melanoma-cells-via-upregulation-of-microrna-493
#1
Yingbin Wang, Jianqin Xie, Wei Liu, Rongzhi Zhang, Shenghui Huang, Yanhong Xing
Lidocaine is a well-documented local anesthetic that has been reported to sensitize the cytotoxicity of cisplatin in cancer cells. However, little information is available concerning whether lidocaine sensitizes the cytotoxicity of 5-fluorouracil (5-FU) in melanoma cells. The study was aimed to explore the effects and mechanisms of lidocaine on the sensitivity to 5-FU in the melanoma cell line SK-MEL-2. Cell viability and apoptosis were analyzed after administration of different concentrations of lidocaine, 5-FU, or the combinations...
November 1, 2017: Die Pharmazie
https://www.readbyqxmd.com/read/29361178/dropclust-efficient-clustering-of-ultra-large-scrna-seq-data
#2
Debajyoti Sinha, Akhilesh Kumar, Himanshu Kumar, Sanghamitra Bandyopadhyay, Debarka Sengupta
Droplet based single cell transcriptomics has recently enabled parallel screening of tens of thousands of single cells. Clustering methods that scale for such high dimensional data without compromising accuracy are scarce. We exploit Locality Sensitive Hashing, an approximate nearest neighbour search technique to develop a de novo clustering algorithm for large-scale single cell data. On a number of real datasets, dropClust outperformed the existing best practice methods in terms of execution time, clustering accuracy and detectability of minor cell sub-types...
January 18, 2018: Nucleic Acids Research
https://www.readbyqxmd.com/read/29346410/an-evaluation-of-multi-probe-locality-sensitive-hashing-for-computing-similarities-over-web-scale-query-logs
#3
Graham Cormode, Anirban Dasgupta, Amit Goyal, Chi Hoon Lee
Many modern applications of AI such as web search, mobile browsing, image processing, and natural language processing rely on finding similar items from a large database of complex objects. Due to the very large scale of data involved (e.g., users' queries from commercial search engines), computing such near or nearest neighbors is a non-trivial task, as the computational cost grows significantly with the number of items. To address this challenge, we adopt Locality Sensitive Hashing (a.k.a, LSH) methods and evaluate four variants in a distributed computing environment (specifically, Hadoop)...
2018: PloS One
https://www.readbyqxmd.com/read/29342158/real-time-community-detection-in-full-social-networks-on-a-laptop
#4
Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth
For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain...
2018: PloS One
https://www.readbyqxmd.com/read/29260348/medical-image-retrieval-with-compact-binary-codes-generated-in-frequency-domain-using-highly-reactive-convolutional-features
#5
Jamil Ahmad, Khan Muhammad, Sung Wook Baik
Efficient retrieval of relevant medical cases using semantically similar medical images from large scale repositories can assist medical experts in timely decision making and diagnosis. However, the ever-increasing volume of images hinder performance of image retrieval systems. Recently, features from deep convolutional neural networks (CNN) have yielded state-of-the-art performance in image retrieval. Further, locality sensitive hashing based approaches have become popular for their ability to allow efficient retrieval in large scale datasets...
December 19, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29123069/a-neural-algorithm-for-a-fundamental-computing-problem
#6
Sanjoy Dasgupta, Charles F Stevens, Saket Navlakha
Similarity search-for example, identifying similar images in a database or similar documents on the web-is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches...
November 10, 2017: Science
https://www.readbyqxmd.com/read/29060551/collision-frequency-locality-sensitive-hashing-for-prediction-of-critical-events
#7
Y Bryce Kim, Erik Hemberg, Una-May O'Reilly
We present a fast, efficient method to predict future critical events for a patient. The prediction method is based on retrieving and leveraging similar waveform trajectories from a large medical database. Locality-sensitive hashing (LSH), our theoretical foundation, is a model-free, sub-linear time, approximate search method enabling a fast retrieval of a nearest neighbor set for a given query. We propose a new variant of LSH, namely Collision Frequency LSH (CFLSH), to further improve the prediction accuracy without sacrificing any speed...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29018478/ultrafast-comparison-of-personal-genomes-via-precomputed-genome-fingerprints
#8
Gustavo Glusman, Denise E Mauldin, Leroy E Hood, Max Robinson
We present an ultrafast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into "genome fingerprints" via locality sensitive hashing. The resulting genome fingerprints can be meaningfully compared even when the input data were obtained using different sequencing technologies, processed using different pipelines, represented in different data formats and relative to different reference versions. Furthermore, genome fingerprints are robust to up to 30% missing data...
2017: Frontiers in Genetics
https://www.readbyqxmd.com/read/28981422/phenotype-prediction-from-metagenomic-data-using-clustering-and-assembly-with-multiple-instance-learning-camil
#9
Mohammad Arifur Rahman, Nathan LaPierre, Huzefa Rangwala
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper we demonstrate a novel, efficient, and effective Multiple Instance Learning (MIL) based computational pipeline to predict patient phenotype from metagenomic data. MIL methods have the advantage that besides predicting the clinical phenotype, we can infer the instance level label or role of microbial sequence reads in the specific disease...
October 4, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28953425/shared-nearest-neighbor-clustering-in-a-locality-sensitive-hashing-framework
#10
Sawsan Kanj, Thomas Brüls, Stéphane Gazut
We present a new algorithm to cluster high-dimensional sequence data and its application to the field of metagenomics, which aims at reconstructing individual genomes from a mixture of genomes sampled from an environmental site, without any prior knowledge of reference data (genomes) or the shape of clusters. Such problems typically cannot be solved directly with classical approaches seeking to estimate the density of clusters, for example, using the shared nearest neighbors (SNN) rule, due to the prohibitive size of contemporary sequence datasets...
September 27, 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
https://www.readbyqxmd.com/read/28771497/sinc-saliency-injected-neural-codes-for-representation-and-efficient-retrieval-of-medical-radiographs
#11
Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik
Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images...
2017: PloS One
https://www.readbyqxmd.com/read/28508884/a-hybrid-cloud-read-aligner-based-on-minhash-and-kmer-voting-that-preserves-privacy
#12
Victoria Popic, Serafim Batzoglou
Low-cost clouds can alleviate the compute and storage burden of the genome sequencing data explosion. However, moving personal genome data analysis to the cloud can raise serious privacy concerns. Here, we devise a method named Balaur, a privacy preserving read mapper for hybrid clouds based on locality sensitive hashing and kmer voting. Balaur can securely outsource a substantial fraction of the computation to the public cloud, while being highly competitive in accuracy and speed with non-private state-of-the-art read aligners on short read data...
May 16, 2017: Nature Communications
https://www.readbyqxmd.com/read/28268827/stratified-locality-sensitive-hashing-for-accelerated-physiological-time-series-retrieval
#13
Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly
We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard family of locality-sensitive hash functions is limited to provide only a single perspective on the data due to its one-to-one relationship to a distinct distance function for measuring similarity. SLSH incorporates multiple locality-sensitive hash families with various distance functions enabling it to examine the data with more diverse and refined perspectives...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268443/analysis-of-locality-sensitive-hashing-for-fast-critical-event-prediction-on-physiological-time-series
#14
Yongwook Bryce Kim, Una-May O'Reilly
We apply the sublinear time, scalable locality-sensitive hashing (LSH) and majority discrimination to the problem of predicting critical events based on physiological waveform time series. Compared to using the linear exhaustive k-nearest neighbor search, our proposed method vastly speeds up prediction time up to 25 times while sacrificing only 1% of accuracy when demonstrated on an arterial blood pressure dataset extracted from the MIMIC2 database. We compare two widely used variants of LSH, the bit sampling based (L1LSH) and the random projection based (E2LSH) methods to measure their direct impact on retrieval and prediction accuracy...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227023/stratified-locality-sensitive-hashing-for-accelerated-physiological-time-series-retrieval
#15
Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly, Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly, Erik Hemberg
We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard family of locality-sensitive hash functions is limited to provide only a single perspective on the data due to its one-to-one relationship to a distinct distance function for measuring similarity. SLSH incorporates multiple locality-sensitive hash families with various distance functions enabling it to examine the data with more diverse and refined perspectives...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226614/analysis-of-locality-sensitive-hashing-for-fast-critical-event-prediction-on-physiological-time-series
#16
Yongwook Bryce Kim, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly
We apply the sublinear time, scalable locality-sensitive hashing (LSH) and majority discrimination to the problem of predicting critical events based on physiological waveform time series. Compared to using the linear exhaustive k-nearest neighbor search, our proposed method vastly speeds up prediction time up to 25 times while sacrificing only 1% of accuracy when demonstrated on an arterial blood pressure dataset extracted from the MIMIC2 database. We compare two widely used variants of LSH, the bit sampling based (L1LSH) and the random projection based (E2LSH) methods to measure their direct impact on retrieval and prediction accuracy...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28127592/low-density-locality-sensitive-hashing-boosts-metagenomic-binning
#17
Yunan Luo, Jianyang Zeng, Bonnie Berger, Jian Peng
No abstract text is available yet for this article.
April 2016: Research in Computational Molecular Biology: ... Annual International Conference, RECOMB ...: Proceedings
https://www.readbyqxmd.com/read/28114084/binary-set-embedding-for-cross-modal-retrieval
#18
Mengyang Yu, Li Liu, Ling Shao
Cross-modal retrieval is such a challenging topic that traditional global representations would fail to bridge the semantic gap between images and texts to a satisfactory level. Using local features from images and words from documents directly can be more robust for the scenario with large intraclass variations and small interclass discrepancies. In this paper, we propose a novel unsupervised binary coding algorithm called binary set embedding (BSE) to obtain meaningful hash codes for local features from the image domain and words from text domain...
September 27, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28113786/in-defense-of-locality-sensitive-hashing
#19
Kun Ding, Chunlei Huo, Bin Fan, Shiming Xiang, Chunhong Pan
Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of variables, thus usually requiring intensive computations. Despite simplicity and efficiency, locality-sensitive hashing (LSH) has never been recognized as a good way to generate such codes due to its poor performance in traditional approximate neighbor search...
October 24, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/27933251/robust-image-hashing-using-ring-partition-pgnmf-and-local-features
#20
Ram Kumar Karsh, R H Laskar, Bhanu Bhai Richhariya
BACKGROUND: Image authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustness, desirable discrimination, sensitivity and localization of forged area with minimum hash length...
2016: SpringerPlus
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