scholarly journals Development of a biomarker database toward performing disease classification and finding disease interrelations

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Shaikh Farhad Hossain ◽  
Ming Huang ◽  
Naoaki Ono ◽  
Aki Morita ◽  
Shigehiko Kanaya ◽  
...  
Keyword(s):  
Rapid Detection ◽  
Molecular Mechanisms ◽  
Clustering Algorithm ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Network Clustering ◽  
Medical Field ◽  
Database Development ◽  
Abnormal State ◽  
Comprehensive Information

Abstract A biomarker is a measurable indicator of a disease or abnormal state of a body that plays an important role in disease diagnosis, prognosis and treatment. The biomarker has become a significant topic due to its versatile usage in the medical field and in rapid detection of the presence or severity of some diseases. The volume of biomarker data is rapidly increasing and the identified data are scattered. To provide comprehensive information, the explosively growing data need to be recorded in a single platform. There is no open-source freely available comprehensive online biomarker database. To fulfill this purpose, we have developed a human biomarker database as part of the KNApSAcK family databases which contain a vast quantity of information on the relationships between biomarkers and diseases. We have classified the diseases into 18 disease classes, mostly according to the National Center for Biotechnology Information definitions. Apart from this database development, we also have performed disease classification by separately using protein and metabolite biomarkers based on the network clustering algorithm DPClusO and hierarchical clustering. Finally, we reached a conclusion about the relationships among the disease classes. The human biomarker database can be accessed online and the inter-disease relationships may be helpful in understanding the molecular mechanisms of diseases. To our knowledge, this is one of the first approaches to classify diseases based on biomarkers. Database URL:  http://www.knapsackfamily.com/Biomarker/top.php

Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

2020 ◽  
Vol 13 ◽  
Author(s):  
Pramila Arulanthu ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Field ◽  
Data Set ◽  
Medical Issues ◽  
Health Organization

: The medical data has an enormous quantity of information. This data set requires effective classification for accurate prediction. Predicting medical issues is an extremely difficult task in which Chronic Kidney Disease (CKD) is one of the major unpredictable diseases in medical field. Perhaps certain medical experts do not have identical awareness and skill to solve the issues of their patients. Most of the medical experts may have underprivileged results on disease diagnosis of their patients. Sometimes patients may lose their life in nature. As per the Global Burden of Disease (GBD-2015) study, death by CKD was ranked 17th place and GBD-2010 report 27th among the causes of death globally. Death by CKD is constituted 2·9% of all death between the year 2010 and 2013 among people from 15 to 69 age. As per World Health Organization (WHO-2005) report, 58 million people expired by CKD. Hence, this article presents the state of art review on Chronic Kidney Disease (CKD) classification and prediction. Normally, advanced data mining techniques, fuzzy and machine learning algorithms are used to classify medical data and disease diagnosis. This study reviews and summarizes many classification techniques and disease diagnosis methods presented earlier. The main intention of this review is to point out and address some of the issues and complications of the existing methods. It is also attempts to discuss the limitations and accuracy level of the existing CKD classification and disease diagnosis methods.

Lightweight deep residual network for alzheimer’s disease classification using sMRI slices

2021 ◽  
pp. 1-9
Author(s):  
Yanteng Zhang ◽  
Qizhi Teng ◽  
Linbo Qing ◽  
Yan Liu ◽  
Xiaohai He
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Computational Cost ◽  
Disease Classification ◽  
Learning Networks ◽  
Feature Maps ◽  
Medical Field ◽  
Widespread Application ◽  
Healthy Control

Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. In recent years, with the widespread application of artificial intelligence in the medical field, various deep learning-based methods have been applied for AD detection using sMRI images. Many of these networks achieved AD vs HC (Healthy Control) classification accuracy of up to 90%but with a large number of computational parameters and floating point operations (FLOPs). In this paper, we adopt a novel ghost module, which uses a series of cheap operations of linear transformation to generate more feature maps, embedded into our designed ResNet architecture for task of AD vs HC classification. According to experiments on the OASIS dataset, our lightweight network achieves an optimistic accuracy of 97.92%and its total parameters are dozens of times smaller than state-of-the-art deep learning networks. Our proposed AD classification network achieves better performance while the computational cost is reduced significantly.

scholarly journals CASS: A distributed network clustering algorithm based on structure similarity for large-scale network

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0203670 ◽  
Author(s):  
Jungrim Kim ◽  
Mincheol Shin ◽  
Jeongwoo Kim ◽  
Chihyun Park ◽  
Sujin Lee ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Network Clustering ◽  
Distributed Network ◽  
Large Scale Network ◽  
Scale Network ◽  
Structure Similarity

scholarly journals Analysis of disease comorbidity patterns in a large-scale China population

2019 ◽  
Vol 12 (S12) ◽  
Author(s):  
Mengfei Guo ◽  
Yanan Yu ◽  
Tiancai Wen ◽  
Xiaoping Zhang ◽  
Baoyan Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
Disease Classification ◽  
Psychiatric Disease ◽  
Power Law Distribution ◽  
Data Set ◽  
Chinese Populations ◽  
Diagnosis And Prognosis ◽  
Disease Occurrence ◽  
Anonymized Data

Abstract Background Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set. Methods We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients. Results We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease). Conclusions Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.

scholarly journals manta: a Clustering Algorithm for Weighted Ecological Networks

mSystems ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Lisa Röttjers ◽  
Karoline Faust
Keyword(s):  
Network Inference ◽  
Clustering Algorithm ◽  
Transition Period ◽  
Parameter Tuning ◽  
Network Clustering ◽  
Sequencing Data ◽  
Cluster Assignment ◽  
Real World Data ◽  
Data Set ◽  
Microbial Network

ABSTRACT Microbial network inference and analysis have become successful approaches to extract biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic network clustering algorithm, manta, which clusters nodes in weighted networks. In contrast to existing algorithms, manta exploits negative edges while differentiating between weak and strong cluster assignments. For this reason, manta can tackle gradients and is able to avoid clustering problematic nodes. In addition, manta assesses the robustness of cluster assignment, which makes it more robust to noisy data than most existing tools. On noise-free synthetic data, manta equals or outperforms existing algorithms, while it identifies biologically relevant subcompositions in real-world data sets. On a cheese rind data set, manta identifies groups of taxa that correspond to intermediate moisture content in the rinds, while on an ocean data set, the algorithm identifies a cluster of organisms that were reduced in abundance during a transition period but did not correlate strongly to biochemical parameters that changed during the transition period. These case studies demonstrate the power of manta as a tool that identifies biologically informative groups within microbial networks. IMPORTANCE manta comes with unique strengths, such as the abilities to identify nodes that represent an intermediate between clusters, to exploit negative edges, and to assess the robustness of cluster membership. manta does not require parameter tuning, is straightforward to install and run, and can be easily combined with existing microbial network inference tools.

scholarly journals A Novel Method for Real-Time, Continuous, Fluorescence-Based Analysis of Anti-DNA Abzyme Activity in Systemic Lupus

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Michelle F. Cavallo ◽  
Anna M. Kats ◽  
Ran Chen ◽  
James X. Hartmann ◽  
Mirjana Pavlovic
Keyword(s):  
Lupus Erythematosus ◽  
Molecular Mechanisms ◽  
Hydrolytic Activity ◽  
Disease Diagnosis ◽  
Response To Therapy ◽  
Systemic Lupus ◽  
Lupus Pathogenesis ◽  
Indirect Identification ◽  
Dna Antibodies ◽  
Novel Method

Systemic Lupus Erythematosus (SLE) is an autoimmune disease characterized by the production of antibodies against a variety of self-antigens including nucleic acids. These antibodies are cytotoxic, catalytic (hydrolyzing DNA, RNA, and protein), and nephritogenic. Current methods for investigating catalytic activities of natural abzymes produced by individuals suffering from autoimmunity are mostly discontinuous and often employ hazardous reagents. Here we demonstrate the utility of dual-labeled, fluorogenic DNA hydrolysis probes in highly specific, sensitive, continuous, fluorescence-based measurement of DNA hydrolytic activity of anti-ssDNA abzymes purified from the serum of patients suffering from SLE. An assay for the presence and levels of antibodies exhibiting hydrolytic activity could facilitate disease diagnosis, prediction of flares, monitoring of disease state, and response to therapy. The assay may allow indirect identification of additional targets of anti-DNA antibodies and the discovery of molecules that inhibit their activity. Combined, these approaches may provide new insights into molecular mechanisms of lupus pathogenesis.

AUTOMATED DIAGNOSIS OF BRAIN TUMOURS ASTROCYTOMAS USING PROBABILISTIC NEURAL NETWORK CLUSTERING AND SUPPORT VECTOR MACHINES

2005 ◽  
Vol 15 (01n02) ◽  
pp. 1-11 ◽  
Author(s):  
DIMITRIS GLOTSOS ◽  
JUSSI TOHKA ◽  
PANAGIOTA RAVAZOULA ◽  
DIONISIS CAVOURAS ◽  
GEORGE NIKIFORIDIS
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Network Clustering ◽  
High Grade ◽  
Cell Nuclei ◽  
Decision Tree Classification ◽  
Average Accuracy ◽  
Malignancy Grading

A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.

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