Integrated network analysis of symptom clusters across disease conditions

Lu, Kezhi; Yang, Kuo; Niyongabo, Edouard; Shu, Zixin; Wang, Jingjing; Chang, Kai Wei; Zou, Qunsheng; Jiang, Jiyue; Jia, Caiyan; Liu, Baoyan; Zhou, Xin

doi:10.1016/j.jbi.2020.103482

Cited by 15 publications

(8 citation statements)

References 70 publications

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“…The four SD diagnostics can be explored through specific combinations of disease characteristics and complex affairs, which have characteristic high dimensions [12,13]. Clustering analysis is an unsupervised learning method that improves the objectivity of data with complex characteristics, such as SD in medical research, especially for symptom terms [14,15]. Furthermore, clustering SD is important because early-stage data mining in TM will greatly impact the efficacy of the cure [16].…”

Section: Introductionmentioning

confidence: 99%

Exploring Traditional Medicine Diagnostic Classification for Parkinson’s Disease Using Hierarchical Clustering

Zhao,

Kwon,

Cha

et al. 2024

Complement Med Res

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Introduction: Personalized diagnosis and therapy for Parkinson’s disease (PD) are needed due to the clinical heterogeneity of PD. Syndrome differentiation (SD) in traditional medicine (TM) is a diagnostic method for customized therapy that comprehensively analyzes various symptoms and systemic syndromes. However, research identifying PD classification based on SD is limited. Methods: Ten electronic databases were systematically searched from inception to August 10, 2021. Clinical indicators, including 380 symptoms, 98 TM signs, and herbal medicine for PD diagnosed with SD, were extracted from 197 articles; frequency statistics on clinical indicators were conducted to classify several subtypes using hierarchical clustering. Results: Four distinct cluster groups were identified, each characterized by significant cluster-specific clinical indicators with 95% confidence intervals of distribution. Subtype 2 had the most severe progression, longest progressive duration, and highest association with greater late-stage PD-associated motor symptoms, including postural instability and gait disturbance. The action properties of the herbal formula and original SD presented in the data sources for subtype 2 were associated with Yin deficiency syndrome. Discussion/Conclusion: Hierarchical clustering analysis distinguished various symptoms and TM signs among patients with PD. These newly identified PD subtypes may optimize the diagnosis and treatment with TM and facilitate prognosis prediction. Our findings serve as a cornerstone for evidence-based guidelines for TM diagnosis and treatment.

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Section: Introductionmentioning

confidence: 99%

Exploring Traditional Medicine Diagnostic Classification for Parkinson’s Disease Using Hierarchical Clustering

Zhao,

Kwon,

Cha

et al. 2024

Complement Med Res

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“…We use two clustering methods (i.e., cluster affiliation model for big networks – BigCLAM, referred to as BC [78], and Markov Clustering – referred to as MCL [14]) as well as multiple parameter values for each clustering method. We use these clustering methods because they are highly prominent [15, 40, 41, 47, 53, 72], and also, MCL has been shown to perform consistently well in different contexts [11, 40, 47, 71, 72].…”

Section: Introductionmentioning

confidence: 99%

Enhancing gene co-expression network inference for the malaria parasitePlasmodium falciparum

Li,

Button-Simons,

Sievert

et al. 2023

Preprint

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Background: Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance. Results: Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out cross-validation. We assess overlaps of the different networks' edges (gene co-expression relationships) as well as predicted functional knowledge. The networks' edges are overall complementary: 47%-85% of all edges are unique to each network. In terms of accuracy of predicting gene functional annotations, all networks yield relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached is below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community. Conclusions: The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.

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“…Development and progression of most diseases have been indicated to be associated with the advent of a group of clinical symptoms at the same time, suggesting that disease subtypes should be classified based on clinical symptom groups rather than a single symptom. Similar to multitarget medications, symptom‐based diagnosis may assist physicians in correctly identifying disease subtypes and fostering the discovery of therapeutic combinations [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Ontology characterization, enrichment analysis, and similarity calculation‐based evaluation of disease–syndrome–formula associations by applying SoFDA

Liu

et al. 2023

iMeta

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Clinical symptom‐based diagnosis and therapy play a crucial role in personalized medicine and drug discovery. The syndromes, distinctive groups of clinical symptoms summarized by traditional Chinese medicine (TCM) theories and clinical experiences, are used as the core diagnostic criteria and therapeutic guidance in TCM. However, there is still a lack of standardized data, information, and intrinsic molecular basis to help TCM syndromes better classify diseases and guide tailored medications. To address this problem, we built the first integrated web platform, SoFDA (http://www.tcmip.cn/Syndrome/front/), with a curated ontology of 319 TCM syndromes, 8045 diseases, and 1359 TCM herbal formulas and their relationships with genes, diseases, and formulas. This platform proposed an association measurement by calculating Jaccard/Cosine similarities between TCM syndromes and their related biomedical entities with case and control validations. On this basis, the SoFDA platform enables biomedical and pharmaceutical scientists to rank and filter the most promising associations for disease diagnosis and tailored interventions. Conversely, the targeted gene sets and symptom sets can also be associated with TCM syndromes, formulas, and diseases for function illustration. Notably, SoFDA explores the multi‐way associations among diseases, TCM syndromes, symptom genes, herbal formulas, drug targets, and pathways in heterogeneous biomedical networks with lots of customization. The protocol here implements all the analyses above using the SoFDA platform. Collectively, SoFDA may provide insights into the biological basis of disease‐specific TCM syndromes and the underlying molecular mechanisms, as well as a tailored treatment for single or multiple symptoms within a syndrome.

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Integrated network analysis of symptom clusters across disease conditions

Cited by 15 publications

References 70 publications

Exploring Traditional Medicine Diagnostic Classification for Parkinson’s Disease Using Hierarchical Clustering

Exploring Traditional Medicine Diagnostic Classification for Parkinson’s Disease Using Hierarchical Clustering

Enhancing gene co-expression network inference for the malaria parasitePlasmodium falciparum

Ontology characterization, enrichment analysis, and similarity calculation‐based evaluation of disease–syndrome–formula associations by applying SoFDA

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