Shortening the Candidate List for Similarity Searching Using Inverted Index

Candidate List Obtained from Metric Inverted Index for Similarity Searching

Advances in Computational Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-030-60887-3_3 ◽

2020 ◽

pp. 29-38

Author(s):

Karina Figueroa ◽

Nora Reyes ◽

Antonio Camarena-Ibarrola

Keyword(s):

Inverted Index ◽

Similarity Searching ◽

Candidate List

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Medicinal Chemistry Database GDBMedChem

10.26434/chemrxiv.7770809.v1 ◽

2019 ◽

Author(s):

Mahendra Awale ◽

Finton Sirockin ◽

Nikolaus Stiefl ◽

Jean-Louis Reymond

Keyword(s):

Small Molecules ◽

Natural Product ◽

Medicinal Chemistry ◽

3D Visualization ◽

Molecular Size ◽

Similarity Searching ◽

Complex Molecules ◽

Synthetic Accessibility ◽

Simple Chemical ◽

Reduced Complexity

<div>The generated database GDB17 enumerates 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogens following simple chemical stability and synthetic feasibility rules, however medicinal chemistry criteria are not taken into account. Here we applied rules inspired by medicinal chemistry to exclude problematic functional groups and complex molecules from GDB17, and sampled the resulting subset evenly across molecular size, stereochemistry and polarity to form GDBMedChem as a compact collection of 10 million small molecules.</div><div><br></div><div>This collection has reduced complexity and better synthetic accessibility than the entire GDB17 but retains higher sp 3 - carbon fraction and natural product likeness scores compared to known drugs. GDBMedChem molecules are more diverse and very different from known molecules in terms of substructures and represent an unprecedented source of diversity for drug design. GDBMedChem is available for 3D-visualization, similarity searching and for download at http://gdb.unibe.ch.</div>

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An Inverted Index Based Audio Retrieval Method

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2012.00510 ◽

2013 ◽

Vol 34 (11) ◽

pp. 2561-2567 ◽

Cited By ~ 3

Author(s):

Xue-yuan Zhang ◽

Qian-hua He ◽

Yan-xiong Li ◽

Wan-ling Ye

Keyword(s):

Inverted Index ◽

Retrieval Method ◽

Audio Retrieval

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A Similarity Searching System for Biological Phenotype Images Using Deep Convolutional Encoder-decoder Architecture

Current Bioinformatics ◽

10.2174/1574893614666190204150109 ◽

2019 ◽

Vol 14 (7) ◽

pp. 628-639 ◽

Cited By ~ 10

Author(s):

Bizhi Wu ◽

Hangxiao Zhang ◽

Limei Lin ◽

Huiyuan Wang ◽

Yubang Gao ◽

...

Keyword(s):

Neural Network ◽

Retrieval System ◽

Sequence Similarity ◽

Local Alignment ◽

Similarity Searching ◽

Loss Of Function ◽

Biological Images ◽

The Neural Network ◽

Convolutional Autoencoder ◽

Biological Phenotype

Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.

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Neural methods for effective, efficient, and exposure-aware information retrieval

ACM SIGIR Forum ◽

10.1145/3476415.3476434 ◽

2021 ◽

Vol 55 (1) ◽

pp. 1-2

Author(s):

Bhaskar Mitra

Keyword(s):

Information Retrieval ◽

Language Processing ◽

Large Scale ◽

Web Search ◽

Real Life ◽

Inverted Index ◽

Information Need ◽

Product Model ◽

Performance Improvements ◽

Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

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