Deep metric learning improves lab of origin prediction of genetically engineered plasmids

Abstract Genome engineering is undergoing unprecedented development and is now becoming widely available. To ensure responsible biotechnology innovation and to reduce misuse of engineered DNA sequences, it is vital to develop tools to identify the lab-of-origin of engineered plasmids. Genetic engineering attribution (GEA), the ability to make sequence-lab associations, would supportforensic experts in this process. Here, we propose a method, based on metric learning, that ranks the most likely labs-of-origin whilstsimultaneously generating embeddings for plasmid sequences and labs. These embeddings can be used to perform various downstreamtasks, such as clustering DNA sequences and labs, as well as using them as features in machine learning models. Our approach employsa circular shift augmentation approach and is able to correctly rank the lab-of-origin90%of the time within its top 10 predictions -outperforming all current state-of-the-art approaches. We also demonstrate that we can perform few-shot-learning and obtain76%top-10 accuracy using only10%of the sequences. This means, we outperform the previous CNN approach using only one-tenth of the data. We also demonstrate that we are able to extract key signatures in plasmid sequences for particular labs, allowing for an interpretable examination of the model’s outputs.CCS Concepts: Information systems→Similarity measures; Learning to rank.

Download Full-text

Ensemble-Based Out-of-Distribution Detection

Electronics ◽

10.3390/electronics10050567 ◽

2021 ◽

Vol 10 (5) ◽

pp. 567

Author(s):

Donghun Yang ◽

Kien Mai Mai Ngoc ◽

Iksoo Shin ◽

Kyong-Ha Lee ◽

Myunggwon Hwang

Keyword(s):

Detection Method ◽

State Of The Art ◽

Metric Learning ◽

Feature Space ◽

Confidence Score ◽

Distance Metric Learning ◽

Current State ◽

Overall Performance ◽

Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

Download Full-text

Unsupervised Action Proposals Using Support Vector Classifiers for Online Video Processing

Sensors ◽

10.3390/s20102953 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2953

Author(s):

Marcos Baptista Ríos ◽

Roberto Javier López-Sastre ◽

Francisco Javier Acevedo-Rodríguez ◽

Pilar Martín-Martín ◽

Saturnino Maldonado-Bascón

Keyword(s):

Video Processing ◽

State Of The Art ◽

Learning To Rank ◽

Support Vector ◽

New Approach ◽

Support Vector Classifier ◽

Current State ◽

Video Frames ◽

Unsupervised Approach ◽

Video Sensor

In this work, we introduce an intelligent video sensor for the problem of Action Proposals (AP). AP consists of localizing temporal segments in untrimmed videos that are likely to contain actions. Solving this problem can accelerate several video action understanding tasks, such as detection, retrieval, or indexing. All previous AP approaches are supervised and offline, i.e., they need both the temporal annotations of the datasets during training and access to the whole video to effectively cast the proposals. We propose here a new approach which, unlike the rest of the state-of-the-art models, is unsupervised. This implies that we do not allow it to see any labeled data during learning nor to work with any pre-trained feature on the used dataset. Moreover, our approach also operates in an online manner, which can be beneficial for many real-world applications where the video has to be processed as soon as it arrives at the sensor, e.g., robotics or video monitoring. The core of our method is based on a Support Vector Classifier (SVC) module which produces candidate segments for AP by distinguishing between sets of contiguous video frames. We further propose a mechanism to refine and filter those candidate segments. This filter optimizes a learning-to-rank formulation over the dynamics of the segments. An extensive experimental evaluation is conducted on Thumos’14 and ActivityNet datasets, and, to the best of our knowledge, this work supposes the first unsupervised approach on these main AP benchmarks. Finally, we also provide a thorough comparison to the current state-of-the-art supervised AP approaches. We achieve 41% and 59% of the performance of the best-supervised model on ActivityNet and Thumos’14, respectively, confirming our unsupervised solution as a correct option to tackle the AP problem. The code to reproduce all our results will be publicly released upon acceptance of the paper.

Download Full-text

Deep Metric Learning to Rank

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ◽

10.1109/cvpr.2019.00196 ◽

2019 ◽

Cited By ~ 15

Author(s):

Fatih Cakir ◽

Kun He ◽

Xide Xia ◽

Brian Kulis ◽

Stan Sclaroff

Keyword(s):

Learning To Rank ◽

Metric Learning ◽

Deep Metric Learning

Download Full-text

CRISPR/Cas-based genome engineering in natural product discovery

Natural Product Reports ◽

10.1039/c8np00089a ◽

2019 ◽

Vol 36 (9) ◽

pp. 1262-1280 ◽

Cited By ~ 30

Author(s):

Yaojun Tong ◽

Tilmann Weber ◽

Sang Yup Lee

Keyword(s):

Natural Product ◽

Genome Editing ◽

Genome Engineering ◽

State Of The Art ◽

Natural Product Discovery ◽

Current State

This review summarizes the current state of the art of CRISPR/Cas-based genome editing technologies for natural product producers.

Download Full-text

Optimal Feature Transport for Cross-View Image Geo-Localization

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6875 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11990-11997 ◽

Cited By ~ 1

Author(s):

Yujiao Shi ◽

Xin Yu ◽

Liu Liu ◽

Tong Zhang ◽

Hongdong Li

Keyword(s):

Large Scale ◽

State Of The Art ◽

Metric Learning ◽

Satellite Image ◽

Geographic Location ◽

Ground Level ◽

Aerial Images ◽

Query Image ◽

Aerial View ◽

Deep Metric Learning

This paper addresses the problem of cross-view image geo-localization, where the geographic location of a ground-level street-view query image is estimated by matching it against a large scale aerial map (e.g., a high-resolution satellite image). State-of-the-art deep-learning based methods tackle this problem as deep metric learning which aims to learn global feature representations of the scene seen by the two different views. Despite promising results are obtained by such deep metric learning methods, they, however, fail to exploit a crucial cue relevant for localization, namely, the spatial layout of local features. Moreover, little attention is paid to the obvious domain gap (between aerial view and ground view) in the context of cross-view localization. This paper proposes a novel Cross-View Feature Transport (CVFT) technique to explicitly establish cross-view domain transfer that facilitates feature alignment between ground and aerial images. Specifically, we implement the CVFT as network layers, which transports features from one domain to the other, leading to more meaningful feature similarity comparison. Our model is differentiable and can be learned end-to-end. Experiments on large-scale datasets have demonstrated that our method has remarkably boosted the state-of-the-art cross-view localization performance, e.g., on the CVUSA dataset, with significant improvements for top-1 recall from 40.79% to 61.43%, and for top-10 from 76.36% to 90.49%. We expect the key insight of the paper (i.e., explicitly handling domain difference via domain transport) will prove to be useful for other similar problems in computer vision as well.

Download Full-text

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

Journal of Speech Language and Hearing Research ◽

10.1044/jshr.3805.1126 ◽

1995 ◽

Vol 38 (5) ◽

pp. 1126-1142 ◽

Cited By ~ 14

Author(s):

Jeffrey W. Gilger

Keyword(s):

Language Development ◽

Behavioral Genetics ◽

State Of The Art ◽

Genetic Research ◽

Great Promise ◽

Behavioral Genetic ◽

Fine Grained ◽

Future Goals ◽

Current State ◽

Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Download Full-text

Schizophrenia Research: Current State of the Art

Contemporary Psychology ◽

10.1037/015267 ◽

1976 ◽

Vol 21 (7) ◽

pp. 497-498

Author(s):

STANLEY GRAND

Keyword(s):

State Of The Art ◽

Current State

Download Full-text

Umbral Calculus

The Electronic Journal of Combinatorics ◽

10.37236/24 ◽

2002 ◽

Vol 1000 ◽

Author(s):

A. Di Bucchianico ◽

D. Loeb

Keyword(s):

19Th Century ◽

Finite Differences ◽

State Of The Art ◽

Umbral Calculus ◽

Current State ◽

Logical Foundation ◽

Complete Bibliography ◽

The 19Th Century ◽

Mathematical Literature

We survey the mathematical literature on umbral calculus (otherwise known as the calculus of finite differences) from its roots in the 19th century (and earlier) as a set of “magic rules” for lowering and raising indices, through its rebirth in the 1970’s as Rota’s school set it on a firm logical foundation using operator methods, to the current state of the art with numerous generalizations and applications. The survey itself is complemented by a fairly complete bibliography (over 500 references) which we expect to update regularly.

Download Full-text