Identification of disease genes, which are a set of genes associated with a disease, plays an important role in understanding and curing diseases. In this paper, we present a biomedical knowledge graph designed specifically for this problem, propose a novel machine learning method that identifies disease genes on such graphs by leveraging recent advances in network biology and graph representation learning, study the effects of various relation types on prediction performance, and empirically demonstrate that our algorithms outperform its closest state-of-the-art competitor in disease gene identification by 24.1%. We also show that we achieve higher precision than Open Targets, the leading initiative for target identification, with respect to predicting drug targets in clinical trials for Parkinson's disease.
Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
Washington University in St. Louis（圣路易斯华盛顿大学）；The Hong Kong Polytechnic University（香港理工大学）；Institute for Informatics (I2)（信息学研究所（I2））；Peking University（北京大学）；Washington University School of Medicine（华盛顿大学医学院）
Accurate identification of disease genes has consistently been one of the keys to decoding a disease's molecular mechanism. Most current approaches focus on constructing biological networks and utilizing machine learning, especially, deep learning to identify disease genes, but ignore the complex relations between entities in the biological knowledge graph. In this paper, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end Knowledge graph completion model for Disease Gene Prediction using interactional tensor decomposition (called KDGene). KDGene…
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；University of Maryland School of Medicine（马里兰大学医学院）；The University of Maryland Greenebaum Comprehensive Cancer Center（马里兰大学格林鲍姆综合癌症中心）
To date, there are no effective treatments for most neurodegenerative diseases. However, certain foods may be associated with these diseases and bring an opportunity to prevent or delay neurodegenerative progression. Our objective is to construct a knowledge graph for neurodegenerative diseases using literature mining to study their relations with diet. We collected biomedical annotations (Disease, Chemical, Gene, Species, SNP&Mutation) in the abstracts from 4,300 publications relevant to both neurodegenerative diseases and diet using PubTator, an NIH-supported tool that can extract biomedical…
At present, the identification of agricultural pests and diseases has the problem that the model is not lightweight enough and difficult to apply. Based on MobileNetV3, this paper introduces the Coordinate Attention block. The parameters of MobileNetV3-large are reduced by 22%, the model size is reduced by 19.7%, and the accuracy is improved by 0.92%. The parameters of MobileNetV3-small are reduced by 23.4%, the model size is reduced by 18.3%, and the accuracy is increased by 0.40%. In addition, the improved MobileNetV3-small was migrated to Jetson Nano for testing. The accuracy increased by 2…
Jamia Millia Islamia（贾米亚·米利亚·伊斯兰亚大学）；Sher-i-Kashmir University of Agricultural Sciences（谢里·克什米尔农业科学大学）
Diseases and pests cause huge economic loss to the apple industry every year. The identification of various apple diseases is challenging for the farmers as the symptoms produced by different diseases may be very similar, and may be present simultaneously. This paper is an attempt to provide the timely and accurate detection and identification of apple diseases. In this study, we propose a deep learning based approach for identification and classification of apple diseases. The first part of the study is dataset creation which includes data collection and data labelling. Next, we train a Convo…
New York University（纽约大学）；New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）
Early detection of many life-threatening diseases (e.g., prostate and breast cancer) within at-risk population can improve clinical outcomes and reduce cost of care. While numerous disease-specific "screening" tests that are closer to Point-of-Care (POC) are in use for this task, their low specificity results in unnecessary biopsies, leading to avoidable patient trauma and wasteful healthcare spending. On the other hand, despite the high accuracy of Magnetic Resonance (MR) imaging in disease diagnosis, it is not used as a POC disease identification tool because of poor accessibility. The root …
Institute for Applied Computing (IAC) “Mauro Picone”, National Research Council of Italy (CNR)（意大利国家研究委员会（CNR）“毛罗·皮科内”应用计算研究所（IAC））；Sapienza University of Rome（罗马第一大学）
Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning setting in which only a subset of instances are labeled as positive while the rest of the data set is unlabeled. In this work, we propose a set of effective network-based f…
Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection. We first improve on the s…
BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Cambridge（剑桥大学）；University of Sheffield（谢菲尔德大学）；The University of Georgia（佐治亚大学）
Plant disease detection is an essential factor in increasing agricultural production. Due to the difficulty of disease detection, farmers spray various pesticides on their crops to protect them, causing great harm to crop growth and food standards. Deep learning can offer critical aid in detecting such diseases. However, it is highly inconvenient to collect a large volume of data on all forms of the diseases afflicting a specific plant species. In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components…
A newly identified coronavirus disease called COVID-19 mainly affects the human respiratory system. COVID-19 is an infectious disease caused by a virus originating in Wuhan, China, in December 2019. Early diagnosis is the primary challenge of health care providers. In the earlier stage, medical organizations were dazzled because there were no proper health aids or medicine to detect a COVID-19. A new diagnostic tool RT-PCR (Reverse Transcription Polymerase Chain Reaction), was introduced. It collects swab specimens from the patient's nose or throat, where the COVID-19 virus gathers. This metho…
Universidade Estadual de Maringá (UEM)（马林加州立大学）；Universidade Federal do Paraná (UFPR)（巴拉那联邦大学）；Instituto Federal do Paraná (IFPR)（巴拉那联邦理工学院）
Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcr…
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the marker vocabulary to known entities. In this proof-of-concept study, we propose unsupervised identification of anomalies as candidates for markers in retinal Optical Coherence Tomography (OCT) imaging…
Dairy farming plays an important role in agriculture for thousands of years not only in Sri Lanka but also in so many other countries. When it comes to dairy farming cattle is an indispensable animal. According to the literature surveys almost 3.9 million cattle and calves die in a year due to different types of diseases. The causes of diseases are mainly bacteria, parasites, fungi, chemical poisons and etc. Infectious diseases can be a greatest threat to livestock health. The mortality rate of cattle causes a huge impact on social, economic and environmental damage. In order to decrease this …
The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and coronary angiography, an invasive tool, while could be probably identified in a non-invasive way by using parameters extracted from…
Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, A-1090 Wien, Austria（奥地利维也纳医科大学复杂系统科学中心（CeMSIIS）复杂系统科学部，维也纳1090）；Complexity Science Hub Vienna, Josefstädter Straße 39, A-1080 Wien, Austria（奥地利维也纳复杂性科学中心，维也纳1080）；IASA, Schlossplatz 1, A-2361 Laxenburg, Austria（奥地利拉克森堡国际应用系统分析研究所）；Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 85701, USA（美国新墨西哥州圣达菲研究所）；Gender Medicine Unit, Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Spitalgasse 23, A-1090 Wien, Austria（奥地利维也纳医科大学内科学III系内分泌与代谢科性别医学部，维也纳1090）；Gesundheit Österreich GmbH, Stubenring 6, A-1010 Wien, Austria（奥地利维也纳健康奥地利有限公司，维也纳1010）
Multimorbidity, the co-occurrence of two or more chronic diseases such as diabetes, obesity or cardiovascular diseases in one patient, is a frequent phenomenon. To make care more efficient, it is of relevance to understand how different diseases condition each other over the life time of a patient. However, most of our current knowledge on such patient careers is either confined to narrow time spans or specific (sets of) diseases. Here, we present a population-wide analysis of long-term patient trajectories by clustering them according to their disease history observed over 17 years. When pati…
Department of Computer Science University of Pisa（比萨大学计算机科学系（更准确的常见译法））
In this paper, we introduce a novel approach for diagnosis of Parkinson's Disease (PD) based on deep Echo State Networks (ESNs). The identification of PD is performed by analyzing the whole time-series collected from a tablet device during the sketching of spiral tests, without the need for feature extraction and data preprocessing. We evaluated the proposed approach on a public dataset of spiral tests. The results of experimental analysis show that DeepESNs perform significantly better than shallow ESN model. Overall, the proposed approach obtains state-of-the-art results in the identificatio…
Stevens Institute of Technology（史蒂文斯理工学院）；Syracuse University（雪城大学）；Dicle University（迪克莱大学）
Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is an atypical, aggressive tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Diagnosis of MPM is difficult and it accounts for about seventy-five percent of all mesothelioma diagnosed yearly in the United States of America. Being a fatal disease, early identification of MPM is crucial for patient survival. Our study implements logistic regression and develops association rules to identify early stage symptoms of MM. We retrieved medical reports generated by Dicle University and implemented …
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
BCN MedTech, Department of Engineering, Universitat Pompeu Fabra（BCN MedTech，工程系，庞培法布拉大学）；European Commission, Joint Research Centre (JRC)（欧洲委员会，联合研究中心（JRC））
Detecting microbial biomarkers used to predict disease phenotypes and clinical outcomes is crucial for disease early-stage screening and diagnosis. Most methods for biomarker identification are linear-based, which is very limited as biological processes are rarely fully linear. The introduction of machine learning to this field tends to bring a promising solution. However, identifying microbial biomarkers in an interpretable, data-driven and robust manner remains challenging. We present MIIDL, a Python package for the identification of microbial biomarkers based on interpretable deep learning.…
Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation
School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；Faculty of Computing, Riphah International University, Faisalabad Campus（里法国际大学费萨拉巴德校区计算学院）；COMSATS University Islamabad, Wah Campus（COMSATS伊斯兰堡大学瓦赫校区）；Washington University of Science and Technology（华盛顿科技大学）；State University of New York Polytechnic Institute（纽约州立大学理工学院）；University of Alaska Anchorage（阿拉斯加安克雷奇大学）
Lehigh University（里海大学）；University of Pennsylvania（宾夕法尼亚大学）
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting…
Concordia University（康考迪亚大学）；McGill University（麦吉尔大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）
The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted the world and led to one of the most challenging crisis across the globe since World War II. The early diagnosis and isolation of COVID-19 positive cases are considered as crucial steps towards preventing the spread of the disease and flattening the epidemic curve. Chest Computed Tomography (CT) scan is a highly sensitive, rapid, and accurate diagnostic technique that can complement Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Recently, deep learning-based models, mostly based on Convolu…
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
University of Georgia（佐治亚大学）；Beijing Normal University（北京师范大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Shanghai Jiao Tong University（上海交通大学）；The University of Virginia（弗吉尼亚大学）
A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification
Nanjing University（南京大学）；Tencent AI Lab（腾讯人工智能实验室）；National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences（中国科学院北京基因组研究所国家基因组科学数据中心）；China National Center for Bioinformation（国家生物信息中心）；College of Life Sciences, University of Chinese Academy of Sciences（中国科学院大学生命科学学院）
Johns Hopkins University（约翰斯·霍普金斯大学）；Lieber Institute for Brain Development（利伯大脑发育研究所）；University of Bari Aldo Moro（巴里大学阿尔多·莫罗大学）；Azienda Ospedaliero-Universitaria Consorziale Policlinico（巴里大学联合综合医院）
We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. Our model consists of an encoder, a decoder and a classifier. The encoder learns a non-linear subspace shared between the input data modalities. The classifier and the decoder act as regularizers to ensure that the low-dimensional encoding captures predictive differences between patients and controls. We use a learnable dropout layer to extract interpretable biomarkers from the data, and our unique training strategy can easily accommodate m…
RWTH Aachen University（亚琛工业大学）；University of Edinburgh（爱丁堡大学）；QuantumBlack；McGill University（麦吉尔大学）
Identification of disease subtypes and corresponding biomarkers can substantially improve clinical diagnosis and treatment selection. Discovering these subtypes in noisy, high dimensional biomedical data is often impossible for humans and challenging for machines. We introduce a new approach to facilitate the discovery of disease subtypes: Instead of analyzing the original data, we train a diagnostic classifier (healthy vs. diseased) and extract instance-wise explanations for the classifier's decisions. The distribution of instances in the explanation space of our diagnostic classifier amplifi…
HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；German Cancer Research Center Heidelberg（海德堡德国癌症研究中心）
The lack of fine-grained annotations hinders the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs. To that end, we introduce a novel loss function for training convolutional neural networks increasing the \emph{localization confidence} and assisting the overall \emph{disease identification}. The loss leverages both image- and patch-level predictions to generate auxiliary supervision. Rather than fo…
In this study, we executed a genomic analysis with the objective of selecting a set of genes (possibly small) that would help in the detection and classification of samples from patients affected by Parkinson Disease. We performed a complete data analysis and during the exploratory phase, we selected a list of differentially expressed genes. Despite their association with the diseased state, we could not use them as a biomarker tool. Therefore, our research was extended to include a multivariate analysis approach resulting in the identification and selection of a group of 20 genes that showed …
Ping An Technology（平安科技）；Saw Swee Hock School of Public Health, National University Health System, National University of Singapore（新加坡国立大学国立大学卫生系统苏瑞福公共卫生学院）；Yong Loo Lin School of Medicine, National University Health System, National University of Singapore（新加坡国立大学国立大学卫生系统杨潞龄医学院）；NUS Graduate School for Integrative Sciences & Engineering（新加坡国立大学综合科学与工程研究生院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）
Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG recordings.
Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data
Deakin University（迪肯大学）；The University of Sydney（悉尼大学）；The University of Queensland（昆士兰大学）；Western Sydney University（西悉尼大学）；Charles Sturt University（查尔斯特大学）；Nottingham Trent University（诺丁汉特伦特大学）；Macquarie University（麦考瑞大学）
Diagnosis of liver infection at preliminary stage is important for better treatment. In todays scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and…
LeukocyteCount: Automatic Identification and Counting for leukocytes using Deep Learning
School of Information Technology and Computer Science (ITCS)（信息科技与计算机科学学院）；Nile University（尼罗大学）；Computer Science Department（计算机科学系）；Faculty of Computers and Artificial Intelligence（计算机与人工智能学院）；Helwan University（海尔万大学）
