scholarly journals Decomposing Retrosynthesis into Reactive Center Prediction and Molecule Generation

2019 ◽  
Author(s):  
Xianggen Liu ◽  
Pengyong Li ◽  
Sen Song
Keyword(s):  
Deep Learning ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Early Years ◽  
Computational Tool ◽  
Rule Based ◽  
Learning Framework ◽  
Reactive Center ◽  
Retrosynthetic Analysis ◽  
Previous State

AbstractChemical retrosynthesis has been a crucial and challenging task in organic chemistry for several decades. In early years, retrosynthesis is accomplished by the disconnection approach which is labor-intensive and requires expert knowledge. Afterward, rule-based methods have dominated in retrosynthesis for years. In this study, we revisit the disconnection approach by leveraging deep learning (DL) to boost its performance and increase the explainability of DL. Concretely, we propose a novel graph-based deep-learning framework, named DeRetro, to predict the set of reactants for a target product by executing the process of disconnection and reactant generation orderly. Experimental results report that DeRetro achieves new state-of-the-art performance in predicting the reactants. In-depth analyses also demonstrate that even without the reaction type as input, DeRetro retains its retrosynthesis performance while other methods show a significant decrease, resulting in a large margin of 19% between DeRetro and previous state-of-the-art rule-based method. These results have established DeRetro as a powerful and useful computational tool in solving the challenging problem of retrosynthetic analysis.

BEHRT-HF: an interpretable transformer-based, deep learning model for prediction of incident heart failure

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Rao ◽  
Y Li ◽  
R Ramakrishnan ◽  
A Hassaine ◽  
D Canoy ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
State Of The Art ◽  
Failure Prediction ◽  
Predictive Performance ◽  
Learning Model ◽  
Learning Framework ◽  
Incident Heart Failure ◽  
Ablation Study ◽  
Deep Learning Model

Abstract Background/Introduction Predicting incident heart failure has been challenging. Deep learning models when applied to rich electronic health records (EHR) offer some theoretical advantages. However, empirical evidence for their superior performance is limited and they remain commonly uninterpretable, hampering their wider use in medical practice. Purpose We developed a deep learning framework for more accurate and yet interpretable prediction of incident heart failure. Methods We used longitudinally linked EHR from practices across England, involving 100,071 patients, 13% of whom had been diagnosed with incident heart failure during follow-up. We investigated the predictive performance of a novel transformer deep learning model, “Transformer for Heart Failure” (BEHRT-HF), and validated it using both an external held-out dataset and an internal five-fold cross-validation mechanism using area under receiver operating characteristic (AUROC) and area under the precision recall curve (AUPRC). Predictor groups included all outpatient and inpatient diagnoses within their temporal context, medications, age, and calendar year for each encounter. By treating diagnoses as anchors, we alternatively removed different modalities (ablation study) to understand the importance of individual modalities to the performance of incident heart failure prediction. Using perturbation-based techniques, we investigated the importance of associations between selected predictors and heart failure to improve model interpretability. Results BEHRT-HF achieved high accuracy with AUROC 0.932 and AUPRC 0.695 for external validation, and AUROC 0.933 (95% CI: 0.928, 0.938) and AUPRC 0.700 (95% CI: 0.682, 0.718) for internal validation. Compared to the state-of-the-art recurrent deep learning model, RETAIN-EX, BEHRT-HF outperformed it by 0.079 and 0.030 in terms of AUPRC and AUROC. Ablation study showed that medications were strong predictors, and calendar year was more important than age. Utilising perturbation, we identified and ranked the intensity of associations between diagnoses and heart failure. For instance, the method showed that established risk factors including myocardial infarction, atrial fibrillation and flutter, and hypertension all strongly associated with the heart failure prediction. Additionally, when population was stratified into different age groups, incident occurrence of a given disease had generally a higher contribution to heart failure prediction in younger ages than when diagnosed later in life. Conclusions Our state-of-the-art deep learning framework outperforms the predictive performance of existing models whilst enabling a data-driven way of exploring the relative contribution of a range of risk factors in the context of other temporal information. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): National Institute for Health Research, Oxford Martin School, Oxford Biomedical Research Centre

scholarly journals Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1136
Author(s):  
David Augusto Ribeiro ◽  
Juan Casavílca Silva ◽  
Renata Lopes Rosa ◽  
Muhammad Saadi ◽  
Shahid Mumtaz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Intelligent Transportation Systems ◽  
Light Field ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Learning Framework ◽  
Reconstruction Methods

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

scholarly journals A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

2019 ◽  
Vol 11 (6) ◽  
pp. 684 ◽  
Author(s):  
Maria Papadomanolaki ◽  
Maria Vakalopoulou ◽  
Konstantinos Karantzalos
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Novel Object ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Object Based ◽  
Deep Networks ◽  
Very High

Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks. Most of these deep networks are based on either convolutional or fully convolutional architectures. In this paper, we propose a novel object-based deep-learning framework for semantic segmentation in very high-resolution satellite data. In particular, we exploit object-based priors integrated into a fully convolutional neural network by incorporating an anisotropic diffusion data preprocessing step and an additional loss term during the training process. Under this constrained framework, the goal is to enforce pixels that belong to the same object to be classified at the same semantic category. We compared thoroughly the novel object-based framework with the currently dominating convolutional and fully convolutional deep networks. In particular, numerous experiments were conducted on the publicly available ISPRS WGII/4 benchmark datasets, namely Vaihingen and Potsdam, for validation and inter-comparison based on a variety of metrics. Quantitatively, experimental results indicate that, overall, the proposed object-based framework slightly outperformed the current state-of-the-art fully convolutional networks by more than 1% in terms of overall accuracy, while intersection over union results are improved for all semantic categories. Qualitatively, man-made classes with more strict geometry such as buildings were the ones that benefit most from our method, especially along object boundaries, highlighting the great potential of the developed approach.

scholarly journals DBTagger

2021 ◽  
Vol 14 (5) ◽  
pp. 813-821
Author(s):  
Arif Usta ◽  
Akifhan Karakayali ◽  
Özgür Ulusoy
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Relational Database ◽  
Relational Databases ◽  
Query Language ◽  
State Of The Art ◽  
Independent Solution ◽  
Rule Based ◽  
Mapping Problem ◽  
Stop Word

Translating Natural Language Queries (NLQs) to Structured Query Language (SQL) in interfaces deployed in relational databases is a challenging task, which has been widely studied in database community recently. Conventional rule based systems utilize series of solutions as a pipeline to deal with each step of this task, namely stop word filtering, tokenization, stemming/lemmatization, parsing, tagging, and translation. Recent works have mostly focused on the translation step overlooking the earlier steps by using adhoc solutions. In the pipeline, one of the most critical and challenging problems is keyword mapping; constructing a mapping between tokens in the query and relational database elements (tables, attributes, values, etc.). We define the keyword mapping problem as a sequence tagging problem, and propose a novel deep learning based supervised approach that utilizes POS tags of NLQs. Our proposed approach, called DBTagger (DataBase Tagger), is an end-to-end and schema independent solution, which makes it practical for various relational databases. We evaluate our approach on eight different datasets, and report new state-of-the-art accuracy results, 92.4% on the average. Our results also indicate that DBTagger is faster than its counterparts up to 10000 times and scalable for bigger databases.

scholarly journals A structure-based deep learning framework for protein engineering

2019 ◽  
Author(s):  
Raghav Shroff ◽  
Austin W. Cole ◽  
Barrett R. Morrow ◽  
Daniel J. Diaz ◽  
Isaac Donnell ◽  
...  
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Deep Learning ◽  
Protein Engineering ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
A Priori ◽  
Novel Proteins ◽  
Learning Framework

AbstractWhile deep learning methods exist to guide protein optimization, examples of novel proteins generated with these techniques require a priori mutational data. Here we report a 3D convolutional neural network that associates amino acids with neighboring chemical microenvironments at state-of-the-art accuracy. This algorithm enables identification of novel gain-of-function mutations, and subsequent experiments confirm substantive phenotypic improvements in stability-associated phenotypes in vivo across three diverse proteins.

A Deep Learning Framework to Predict Routability for FPGA Circuit Placement

2021 ◽  
Vol 14 (3) ◽  
pp. 1-28
Author(s):  
Abeer Al-Hyari ◽  
Hannah Szentimrey ◽  
Ahmed Shamli ◽  
Timothy Martin ◽  
Gary Gréwal ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Learning Framework ◽  
Proposed Model ◽  
Field Programmable ◽  
Circuit Placement ◽  
Detailed Placement ◽  
Place And Route ◽  
Deep Learning Model

The ability to accurately and efficiently estimate the routability of a circuit based on its placement is one of the most challenging and difficult tasks in the Field Programmable Gate Array (FPGA) flow. In this article, we present a novel, deep learning framework based on a Convolutional Neural Network (CNN) model for predicting the routability of a placement. Since the performance of the CNN model is strongly dependent on the hyper-parameters selected for the model, we perform an exhaustive parameter tuning that significantly improves the model’s performance and we also avoid overfitting the model. We also incorporate the deep learning model into a state-of-the-art placement tool and show how the model can be used to (1) avoid costly, but futile, place-and-route iterations, and (2) improve the placer’s ability to produce routable placements for hard-to-route circuits using feedback based on routability estimates generated by the proposed model. The model is trained and evaluated using over 26K placement images derived from 372 benchmarks supplied by Xilinx Inc. We also explore several opportunities to further improve the reliability of the predictions made by the proposed DLRoute technique by splitting the model into two separate deep learning models for (a) global and (b) detailed placement during the optimization process. Experimental results show that the proposed framework achieves a routability prediction accuracy of 97% while exhibiting runtimes of only a few milliseconds.

scholarly journals A Simple and Efficient Tensor Calculus for Machine Learning

2020 ◽  
Vol 177 (2) ◽  
pp. 157-179
Author(s):  
Sören Laue ◽  
Matthias Mitterreiter ◽  
Joachim Giesen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Efficient Method ◽  
Automatic Differentiation ◽  
State Of The Art ◽  
Tensor Representation ◽  
Tensor Calculus ◽  
Online Tool ◽  
Previous State ◽  
Derivatives Of

Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in machine learning. A key concern is the efficiency of evaluating the expressions and their derivatives that hinges on the representation of these expressions. Recently, an algorithm for computing higher order derivatives of tensor expressions like Jacobians or Hessians has been introduced that is a few orders of magnitude faster than previous state-of-the-art approaches. Unfortunately, the approach is based on Ricci notation and hence cannot be incorporated into automatic differentiation frameworks from deep learning like TensorFlow, PyTorch, autograd, or JAX that use the simpler Einstein notation. This leaves two options, to either change the underlying tensor representation in these frameworks or to develop a new, provably correct algorithm based on Einstein notation. Obviously, the first option is impractical. Hence, we pursue the second option. Here, we show that using Ricci notation is not necessary for an efficient tensor calculus and develop an equally efficient method for the simpler Einstein notation. It turns out that turning to Einstein notation enables further improvements that lead to even better efficiency. The methods that are described in this paper for computing derivatives of matrix and tensor expressions have been implemented in the online tool www.MatrixCalculus.org.

scholarly journals A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paramita Ray ◽  
Amlan Chakrabarti
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Learning Method ◽  
Rule Based ◽  
Aspect Extraction ◽  
Art Methods ◽  
Rule Based Approach

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

scholarly journals A Deep Learning Framework for Malware Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 90-108
Author(s):  
Mahmoud Kalash ◽  
Mrigank Rochan ◽  
Noman Mohammed ◽  
Neil Bruce ◽  
Yang Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Superior Performance ◽  
Traditional Learning ◽  
Security Threats ◽  
Learning Approaches ◽  
Learning Framework ◽  
Malware Classification ◽  
New Strategies

In this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial institutions, businesses, and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples. Nowadays, machine learning approaches are becoming popular for malware classification. However, most of these approaches are based on shallow learning algorithms (e.g. SVM). Recently, convolutional neural networks (CNNs), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Inspired by this, the authors propose a CNN-based architecture to classify malware samples. They convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, namely Malimg and Microsoft, demonstrate that their method outperforms competing state-of-the-art algorithms.

scholarly journals Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer

2017 ◽  
Author(s):  
Seungwhan Moon ◽  
Jaime Carbonell
Keyword(s):  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
A Priori ◽  
Classification Task ◽  
Learning Approach ◽  
Learning Framework ◽  
Previous State

We study a transfer learning framework where source and target datasets are heterogeneous in both feature and label spaces. Specifically, we do not assume explicit relations between source and target tasks a priori, and thus it is crucial to determine what and what not to transfer from source knowledge. Towards this goal, we define a new heterogeneous transfer learning approach that (1) selects and attends to an optimized subset of source samples to transfer knowledge from, and (2) builds a unified transfer network that learns from both source and target knowledge. This method, termed "Attentional Heterogeneous Transfer", along with a newly proposed unsupervised transfer loss, improve upon the previous state-of-the-art approaches on extensive simulations as well as a challenging hetero-lingual text classification task.

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