Showing papers by "Yi-Ping Phoebe Chen published in 2021"

ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion

Abstract: The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a variety of human diseases. Identifying potential disease-related lncRNAs may benefit disease diagnosis, treatment and prognosis. A number of methods have been proposed to predict the potential lncRNA-disease relationships. However, most of them may give rise to incorrect results due to relying on single similarity measure. This article proposes a novel framework (ILDMSF) by fusing the lncRNA similarities and disease similarities, which are measured by lncRNA-related gene and known lncRNA-disease interaction and disease semantic interaction, and known lncRNA-disease interaction, respectively. Further, the support vector machine is employed to identify the potential lncRNA-disease associations based on the integrated similarity. The leave-one-out cross validation is performed to compare ILDMSF with other state of the art methods. The experimental results demonstrate our method is prospective in exploring potential correlations between lncRNA and disease.

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

Abstract: Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Existing data mining and machine learning methods are either shallow methods that could not effectively capture the complex interdependency of graph data or graph autoencoder methods that could not fully exploit the contextual information as supervision signals for effective anomaly detection. To overcome these challenges, in this paper, we propose a novel method, Self-Supervised Learning for Graph Anomaly Detection (SL-GAD). Our method constructs different contextual subgraphs (views) based on a target node and employs two modules, generative attribute regression and multi-view contrastive learning for anomaly detection. While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus it is able to capture the anomalies in the structure space, mixing of structure and attribute information. We conduct extensive experiments on six benchmark datasets and the results demonstrate that our method outperforms significantly state-of-the-art methods by a large margin.

Prediction of drug adverse events using deep learning in pharmaceutical discovery

Abstract: Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug-drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.

A template-free machine vision-based crop row detection algorithm

Abstract: Due to the increase in the use of precision agriculture, field trials have increased in size to allow for genomic selection tool development by linking quantitative phenotypic traits to sequence variations in the DNA of various crops Crop row detection is an important step to enable the development of an efficient downstream analysis pipeline for genomic selection In this paper, an efficient crop row detection algorithm was proposed that detected crop rows in colour images without the use of templates and most other pre-information such as number of rows and spacing between rows The method only requires input on field weed intensity The algorithm was robust in challenging field trial conditions such as variable light, sudden shadows, poor illumination, presence of weeds and noise and irregular crop shape The algorithm can be applied to crop images taken from the top and side views The algorithm was tested on a public dataset with side view images of crop rows and on Genomic Sub-Selection dataset in which images were taken from the top view Different analyses were performed to check the robustness of the algorithm and to the best of authors’ knowledge, the Receiver Operating Characteristic graph has been applied for the first time in crop row detection algorithm testing Lastly, comparing this algorithm with several state-of-the-art methods, it exhibited superior performance

Descriptive prediction of drug side-effects using a hybrid deep learning model

Abstract: In this study, we developed a hybrid deep learning (DL) model, which is one of the first interpretable hybrid DL models with Inception modules, to give a descriptive prediction of drug side‐effects. The model consists of a graph convolutional neural network (GCNN) with Inception modules to allow more efficient learning of drug molecular features and bidirectional long short‐term memory (BiLSTM) recurrent neural networks to associate drug structure with its associated side effects. The outputs from the two networks (GCNN and BiLSTM) are then concatenated and a fully connected network is used to predict the side effects of drugs. Our model achieves an AUC score of 0.846 irrespective of what classification threshold is chosen. It has a precision score of 0.925 and the Bilingual Evaluation Understudy (BLEU) scores obtained were 0.973, 0.938, 0.927, and 0.318 which show significant achievements despite the fact that a small drug data set is used for adverse drug reaction (ADR) prediction. Moreover, the model is capable of accurately structuring correct words to describe drug side‐effects and associates them with its drug name and molecular structure. The predicted drug structure and ADR relation will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during early phases of drug development. It can also help detect unknown ADRs embedded in existing drugs, hence contributing significantly to the science of pharmacovigilance.

Machine learning for medical imaging-based COVID-19 detection and diagnosis

Abstract: The novel coronavirus disease 2019 (COVID-19) is considered to be a significant health challenge worldwide because of its rapid human-to-human transmission, leading to a rise in the number of infected people and deaths. The detection of COVID-19 at the earliest stage is therefore of paramount importance for controlling the pandemic spread and reducing the mortality rate. The real-time reverse transcription-polymerase chain reaction, the primary method of diagnosis for coronavirus infection, has a relatively high false negative rate while detecting early stage disease. Meanwhile, the manifestations of COVID-19, as seen through medical imaging methods such as computed tomography (CT), radiograph (X-ray), and ultrasound imaging, show individual characteristics that differ from those of healthy cases or other types of pneumonia. Machine learning (ML) applications for COVID-19 diagnosis, detection, and the assessment of disease severity based on medical imaging have gained considerable attention. Herein, we review the recent progress of ML in COVID-19 detection with a particular focus on ML models using CT and X-ray images published in high-ranking journals, including a discussion of the predominant features of medical imaging in patients with COVID-19. Deep Learning algorithms, particularly convolutional neural networks, have been utilized widely for image segmentation and classification to identify patients with COVID-19 and many ML modules have achieved remarkable predictive results using datasets with limited sample sizes.

Network-based methods for gene function prediction.

Abstract: The rapid development of high-throughput technology has generated a large number of biological networks. Network-based methods are able to provide rich information for inferring gene function. This is composed of analyzing the topological characteristics of genes in related networks, integrating biological information, and considering data from different data sources. To promote network biology and related biotechnology research, this article provides a survey for the state of the art of advanced methods of network-based gene function prediction and discusses the potential challenges.

Perceptual image hashing using transform domain noise resistant local binary pattern

Abstract: A new Discrete Cosine Transform (DCT) domain Perceptual Image Hashing (PIH) scheme is proposed in this paper. PIH schemes are designed to extract a set of features from an image to form a compact representation that can be used for image integrity verification. A PIH scheme takes an image as the input, extracts its invariant features and constructs a fixed length output, which is called a hash value. The hash value generated by a PIH scheme is then used for image integrity verification. The basic requirement for any PIH scheme is its robustness to non-malicious distortions and discriminative ability to detect minute level of tampering. The feature extraction phase plays a major role in guaranteeing robustness and tamper detection ability of a PIH scheme. The proposed scheme fuses together the DCT and Noise Resistant Local Binary Pattern (NRLBP) to compute image hash. In this scheme, an input image is divided into non-overlapping blocks. Then, DCT of each non-overlapping block is computed to form a DCT based transformed image block. Subsequently, NRLBP is applied to calculate NRLBP histogram. Histograms of all the blocks are concatenated together to get a hash vector for a single image. It is observed that low frequency DCT coefficients inherently have quite high robustness against non-malicious distortions, hence the NRLBP features extracted from the low frequency DCT coefficients provide high robustness. Computational results exhibit that the proposed hashing scheme outperforms some of the existing hashing schemes as well as can detect localized tamper detection as small as 3% of the original image size and at the same time resilient against non-malicious distortions.

A Survey of Network Embedding for Drug Analysis and Prediction.

Abstract: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

A New Method for Extracting Individual Plant Bio-Characteristics from High-Resolution Digital Images

Abstract: The extraction of automated plant phenomics from digital images has advanced in recent years. However, the accuracy of extracted phenomics, especially for individual plants in a field environment, requires improvement. In this paper, a new and efficient method of extracting individual plant areas and their mean normalized difference vegetation index from high-resolution digital images is proposed. The algorithm was applied on perennial ryegrass row field data multispectral images taken from the top view. First, the center points of individual plants from digital images were located to exclude plant positions without plants. Second, the accurate area of each plant was extracted using its center point and radius. Third, the accurate mean normalized difference vegetation index of each plant was extracted and adjusted for overlapping plants. The correlation between the extracted individual plant phenomics and fresh weight ranged between 0.63 and 0.75 across four time points. The methods proposed are applicable to other crops where individual plant phenotypes are of interest.

New Insights Into Drug Repurposing for COVID-19 Using Deep Learning

Abstract: The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies have been implemented to find potential anti-COVID-19 drugs in a short span of time. Motivated by this critical global challenge, in this review, we detail approaches that have been used for drug repurposing for COVID-19 and suggest improvements to the existing deep learning (DL) approach to identify and repurpose drugs to treat this complex disease. By optimizing hyperparameter settings, deploying suitable activation functions, and designing optimization algorithms, the improved DL approach will be able to perform feature extraction from quality big data, turning the traditional DL approach, referred to as a “black box,” which generalizes and learns the transmitted data, into a “glass box” that will have the interpretability of its rationale while maintaining a high level of prediction accuracy. When adopted for drug repurposing for COVID-19, this improved approach will create a new generation of DL approaches that can establish a cause and effect relationship as to why the repurposed drugs are suitable for treating COVID-19. Its ability can also be extended to repurpose drugs for other complex diseases, develop appropriate treatment strategies for new diseases, and provide precision medical treatment to patients, thus paving the way to discover new drugs that can potentially be effective for treating COVID-19.

Tracking Neutrophil Migration in Zebrafish Model Using Multi-Channel Feature Learning

Abstract: Tracking cells over time is crucial in the fields of computer vision and biomedical science. Studying neutrophils and their migratory profile is the highly topical fields in inflammation research due to determining role of these cells during immune responses. As neutrophils generally are of various shapes and motion, it remains challenging to track and describe their behaviours from multi-dimensional microscopy datasets. In this study, we propose a robust novel multi-channel feature learning (MCFL) model inspired by deep learning to extract the complex behaviour of neutrophils moved in time lapse images. In this model, the convolutional neural networks along with cell relocation distance and orientation channels learn the robust significant spatial and temporal features of an individual neutrophil. Additionally, we also proposed a new cell tracking framework to detect and track neutrophils in the original time-laps microscopy images, entails sampling, observation, and visualisation functions. Our proposed cell tracking-based-multi channel feature learning method has remarkable performance in rectifying common cell tracking problem compared with state-of the-art methods.

Mortality Prediction of Patients With Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study

Abstract: Background: Cardiovascular disease (CVD) is the greatest health problem in Australia, which kills more people than any other disease and incurs enormous costs for the health care system. In this study, we present a benchmark comparison of various artificial intelligence (AI) architectures for predicting the mortality rate of patients with CVD using structured medical claims data. Compared with other research in the clinical literature, our models are more efficient because we use a smaller number of features, and this study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit. Objective: This study aims to support health clinicians in accurately predicting mortality among patients with CVD using only claims data before a clinic visit. Methods: The data set was obtained from the Medicare Benefits Scheme and Pharmaceutical Benefits Scheme service information in the period between 2004 and 2014, released by the Department of Health Australia in 2016. It included 346,201 records, corresponding to 346,201 patients. A total of five AI algorithms, including four classical machine learning algorithms (logistic regression [LR], random forest [RF], extra trees [ET], and gradient boosting trees [GBT]) and a deep learning algorithm, which is a densely connected neural network (DNN), were developed and compared in this study. In addition, because of the minority of deceased patients in the data set, a separate experiment using the Synthetic Minority Oversampling Technique (SMOTE) was conducted to enrich the data. Results: Regarding model performance, in terms of discrimination, GBT and RF were the models with the highest area under the receiver operating characteristic curve (97.8% and 97.7%, respectively), followed by ET (96.8%) and LR (96.4%), whereas DNN was the least discriminative (95.3%). In terms of reliability, LR predictions were the least calibrated compared with the other four algorithms. In this study, despite increasing the training time, SMOTE was proven to further improve the model performance of LR, whereas other algorithms, especially GBT and DNN, worked well with class imbalanced data. Conclusions: Compared with other research in the clinical literature involving AI models using claims data to predict patient health outcomes, our models are more efficient because we use a smaller number of features but still achieve high performance. This study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit.

Shot Boundary Detection Through Multi-stage Deep Convolution Neural Network

Abstract: Fast and accurate shot segmentation is very important for content-based video analysis. However, existing solutions have not yet achieved the ideal balance of speed and accuracy. In this paper, we propose a multi-stage shot boundary detection framework based on deep CNN for shot segmentation tasks. The process is composed of three stages, which are respectively for candidate boundary detection, abrupt detection and gradual transition detection. At each stage, deep CNN is used to extract image features, which overcomes the disadvantages of hand-craft feature-based methods such as poor scalability and complex calculation. Besides, we also set a variety of constraints to filter as many non-boundaries as possible to improve the processing speed of the model. In gradual transition detection, we introduce a scheme that can infer the gradual position by computing the probability signals of the start, mid and end of the gradual transition. We conduct experiments on ClipShots and the experimental results show that the proposed model achieves better performance on abrupt and gradual transition detection.

FexRNA: Exploratory Data Analysis and Feature Selection of non-coding RNA.

Abstract: Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species Many ncRNA datasets exist that provide a sequential representation of data that best suits biomedical purposes However, for ncRNA identification and analysis, statistical learning methods require hidden numerical features from the data The extraction of hidden features, their analysis, and usage of a suitable set of features is crucial towards any statistical learning methods performance Furthermore, a wealth of sequence intrinsic features has been proposed for ncRNA identification Therefore, a systematic review and selection of these features are warranted First, fasta format sequence datasets are generated from RNACentral representing many ncRNA types across a number of species Next, a features dataset is created per fasta dataset consisting of 17 most frequently reported sequence intrinsic features The features dataset is available from the FexRNA platform developed as part of this work In addition, the features datasets are explored and analysed in terms of statistical information, univariate and bivariate analysis For the feature selection (FS), a two-fold hierarchal FS framework based on majority voting and correlation is proposed and evaluated Therefore, the FexRNA platform provides a useful platform for information about ncRNA features datasets, features analysis, and selection

Genetic source completeness of HIV-1 circulating recombinant forms (CRFs) predicted by multi-label learning.

Abstract: Motivation Infection with strains of different subtypes and the subsequent crossover reading between the two strands of genomic RNAs by host cells' reverse transcriptase are the main causes of the vast HIV-1 sequence diversity. Such inter-subtype genomic recombinants can become circulating recombinant forms (CRFs) after widespread transmissions in a population. Complete prediction of all the subtype sources of a CRF strain is a complicated machine learning problem. It is also difficult to understand whether a strain is an emerging new subtype and if so, how to accurately identify the new components of the genetic source. Results We introduce a multi-label learning algorithm for the complete prediction of multiple sources of a CRF sequence as well as the prediction of its chronological number. The prediction is strengthened by a voting of various multi-label learning methods to avoid biased decisions. In our steps, frequency and position features of the sequences are both extracted to capture signature patterns of pure subtypes and CRFs. The method was applied to 7185 HIV-1 sequences, comprising 5530 pure subtype sequences and 1655 CRF sequences. Results have demonstrated that the method can achieve very high accuracy (reaching 99%) in the prediction of the complete set of labels of HIV-1 recombinant forms. A few wrong predictions are actually incomplete predictions, very close to the complete set of genuine labels. Availability and implementation https://github.com/Runbin-tang/The-source-of-HIV-CRFs-prediction. Supplementary information Supplementary data are available at Bioinformatics online.

Joint knowledge-powered topic level attention for a convolutional text summarization model

Abstract: Abstractive text summarization (ATS) often fails to capture salient information and preserve the original meaning of the content in the generated summaries due to a lack of background knowledge. We present a method to provide the topic information based on the background knowledge of documents to a deep learning-based summarization model. This method comprises a topic knowledge base (TKB) and convolutional sequence network-based text summarization model with knowledge-powered topic level attention (KTOPAS). TKB employs conceptualization to retrieve the semantic salient knowledge of documents and the knowledge-powered topic model (KPTopicM) to generate coherent and meaningful topic information by utilizing the knowledge that represents the documents well. KTOPAS obtains knowledge-powered topic information (also called topic knowledge) from TKB and incorporates the topic knowledge into the convolutional sequence network through a high-level topic level attention to resolve the existing issues in ATS. KTOPAS introduces a tri-attention channel to jointly learn the attention of the source elements over the summary elements, the source elements over topic knowledge, and topic knowledge over the summary elements to present the contextual alignment information from three aspects and combine them using the softmax function to generate the final probability distribution which enables the model to produce coherent, concise, and human-like summaries with word diversity. By conducting experiments on datasets, namely CNN/Daily Mail and Gigaword, the results show that our proposed method consistently outperforms the competing baselines. Moreover, TKB improves the effectiveness of the resulting summaries by providing topic knowledge to KTOPAS and demonstrates the quality of the proposed method.

Distractor-Aware Tracker with a Domain-Special Optimized Benchmark for Soccer Player Tracking

Abstract: Player tracking in broadcast soccer videos has received widespread attention in the field of sports video analysis, however, we note that there is not a suitable tracking algorithm specifically for soccer video, and the existing benchmarks used for soccer player tracking cover few scenarios with low difficulties. From the observation of the soccer scene that interference and occlusion are knotty problems because the distractors are extremely similar to the targets, a distractor-aware player tracking algorithm and a high-quality benchmark for soccer play tracking (BSPT) have been presented. The distractor-aware player tracking algorithm is able to perceive semantic information about distracting players in the background by similarity judgment, the semantic distractor-aware information is encoded into a context vector and is constantly updated as the objects move through a video sequence. Distractor-aware information is then appended to the tracking result of the baseline tracker to improve the intra-class discriminative power. BSPT contains a total of 120 sequences with rich annotations. Each sequence covers 8 specialized frame-level attributes from soccer scenarios and the player occlusion situations are finely divided into 4 categories for a more comprehensive comparison. In the experimental section, the performance of our algorithm and the other 14 compared trackers are evaluated on BSPT with detailed analysis. Experimental results reveal the effectiveness of the proposed distractor-aware model especially under the attribute of occlusion. The BSPT benchmark and raw experimental results are available on the project page at http://media.hust.edu.cn/BSPT.htm.

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

Abstract: Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Existing data mining and machine learning methods are either shallow methods that could not effectively capture the complex interdependency of graph data or graph autoencoder methods that could not fully exploit the contextual information as supervision signals for effective anomaly detection. To overcome these challenges, in this paper, we propose a novel method, Self-Supervised Learning for Graph Anomaly Detection (SL-GAD). Our method constructs different contextual subgraphs (views) based on a target node and employs two modules, generative attribute regression and multi-view contrastive learning for anomaly detection. While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information. We conduct extensive experiments on six benchmark datasets and the results demonstrate that our method outperforms state-of-the-art methods by a large margin.

Multi-Directional Convolution Networks with Spatial-Temporal Feature Pyramid Module for Action Recognition

Abstract: Recent attempts show that factorizing 3D convolutional filters into separate spatial and temporal components brings impressive improvement in action recognition. However, traditional temporal convolution operating along the temporal dimension will aggregate unrelated features, since the feature maps of fast-moving objects have shifted spatial positions. In this paper, we propose a novel and effective Multi-Directional Convolution (MDConv), which extracts features along different spatial-temporal orientations. Especially, MDConv has the same FLOPs and parameters as the traditional 1D temporal convolution. Also, we propose the Spatial-Temporal Feature Pyramid Module (STFPM) to fuse spatial semantics in different scales in a light-weight way. Our extensive experiments show that the models which integrate with MDConv achieve better accuracy on several large-scale action recognition benchmarks such as Kinetics, AVA and Something-Something V1&V2 datasets.

Concept-based Topic Attention for a Convolutional Sequence Document Summarization Model

Abstract: Neural network-based document summarization often suffers from the problem of summarizing irrelevant topic content regarding the main idea. One of the main reasons leading to this problem is a lack of human common-sense knowledge which generates facts that are not decipherable. We propose a document summarization framework called Document Summarization with Concept-based Topic Triple Attention (DOSCTTA). The framework incorporates concept-based topic information into a convolutional sequence document summarization model. We propose a concept-based topic model (CTM) to generate semantic topic information using conceptual information or knowledge which is retrieved from a knowledge base. We introduce a triple attention mechanism (TAM) to not only measure the importance of each topic concept and source element to the output elements but also the importance of the topic concept to the source element. TAM presents contextual information from three aspects and then combines them using a softmax activation to acquire the final probability distribution to enable the model to produce coherent and meaningful summaries with a wide range of rich vocabulary. The experimental evaluations which are conducted over the Gigaword and CNN/Daily Mail (CNN/DM) datasets reveal that DOSCTTA surpasses the various widely recognized state-of-the-art models (WSOTA) such as Seq2Seq, PGEN, CSM and TopicCSM. DOSCTTA achieves competitive results by generating coherent and informative summaries.