Disease classification: from phenotypic similarity to integrative genomics and beyond

2019 ◽  
Vol 20 (5) ◽  
pp. 1769-1780 ◽  
Author(s):  
Mikhail G Dozmorov
Keyword(s):  
Molecular Mechanisms ◽  
Drug Repositioning ◽  
Real Life ◽  
Disease Classification ◽  
Data Sets ◽  
Integrative Genomics ◽  
Cellular Functions ◽  
Complementary Approach ◽  
Disease Similarity ◽  
Based Medicine

Abstract A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

scholarly journals On the Use of Correlation and MI as a Measure of Metabolite—Metabolite Association for Network Differential Connectivity Analysis

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 171
Author(s):  
Sanjeevan Jahagirdar ◽  
Edoardo Saccenti
Keyword(s):  
Molecular Mechanisms ◽  
Real Life ◽  
Simulated Data ◽  
Metabolic Model ◽  
Data Sets ◽  
Connectivity Analysis ◽  
Estimation Algorithms ◽  
Metabolomic Data ◽  
Spearman’S Correlation ◽  
Differential Connectivity

Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite—metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson’s or Spearman’s correlation when the application is to quantify and detect differentially connected metabolites.

scholarly journals Luteal Phase Support in IVF: Comparison Between Evidence-Based Medicine and Real-Life Practices

2020 ◽  
Vol 11 ◽  
Author(s):  
Federica Di Guardo ◽  
Habib Midassi ◽  
Annalisa Racca ◽  
Herman Tournaye ◽  
Michel De Vos ◽  
...  
Keyword(s):  
Evidence Based Medicine ◽  
Luteal Phase ◽  
Real Life ◽  
Evidence Based ◽  
Luteal Phase Support ◽  
Based Medicine

scholarly journals Mitochondrial Dynamics, ROS, and Cell Signaling: A Blended Overview

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 332
Author(s):  
Valentina Brillo ◽  
Leonardo Chieregato ◽  
Luigi Leanza ◽  
Silvia Muccioli ◽  
Roberto Costa
Keyword(s):  
Molecular Mechanisms ◽  
Mitochondrial Dynamics ◽  
Eukaryotic Cells ◽  
Therapeutic Approaches ◽  
Cellular Functions ◽  
Lipid Trafficking ◽  
Mitochondrial Ros ◽  
Intracellular Organelles ◽  
Proliferation Regulation ◽  
Dynamic Plasticity

Mitochondria are key intracellular organelles involved not only in the metabolic state of the cell, but also in several cellular functions, such as proliferation, Calcium signaling, and lipid trafficking. Indeed, these organelles are characterized by continuous events of fission and fusion which contribute to the dynamic plasticity of their network, also strongly influenced by mitochondrial contacts with other subcellular organelles. Nevertheless, mitochondria release a major amount of reactive oxygen species (ROS) inside eukaryotic cells, which are reported to mediate a plethora of both physiological and pathological cellular functions, such as growth and proliferation, regulation of autophagy, apoptosis, and metastasis. Therefore, targeting mitochondrial ROS could be a promising strategy to overcome and hinder the development of diseases such as cancer, where malignant cells, possessing a higher amount of ROS with respect to healthy ones, could be specifically targeted by therapeutic treatments. In this review, we collected the ultimate findings on the blended interplay among mitochondrial shaping, mitochondrial ROS, and several signaling pathways, in order to contribute to the dissection of intracellular molecular mechanisms involved in the pathophysiology of eukaryotic cells, possibly improving future therapeutic approaches.

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

scholarly journals LOWER BOUNDS OF CHARACTERISTIC SCALE OF TOPOLOGICAL MODIFICATION OF THE NEWTONIAN GRAVITATION

2012 ◽  
Vol 21 (05) ◽  
pp. 1250048
Author(s):  
L. IORIO
Keyword(s):  
Semimajor Axis ◽  
Data Sets ◽  
Orbital Elements ◽  
Dynamical Models ◽  
Spatial Direction ◽  
Newtonian Gravitation ◽  
Complementary Approach ◽  
Local Modification

We analytically work out the long-term orbital perturbations induced by the leading order of perturbing potential arising from the local modification of the Newton's inverse square law due to a topology ℝ2 × 𝕊1 with a compactified dimension of radius R recently proposed by Floratos and Leontaris. We neither restrict to any specific spatial direction [Formula: see text] for the asymmetry axis nor to particular orbital configurations of the test particle. Thus, our results are quite general. Nonvanishing long-term variations occur for all the usual osculating Keplerian orbital elements, apart from the semimajor axis which is left unaffected. By using recent improvements in the determination of the orbital motion of Saturn from Cassini data, we preliminarily inferred R ≳ 4-6 kau . As a complementary approach, the putative topological effects should be explicitly modeled and solved-for with a modified version of the ephemerides dynamical models with which the same data sets should be reprocessed.

scholarly journals An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Mustafa Yuksel ◽  
Suat Gonul ◽  
Gokce Banu Laleci Erturkmen ◽  
Ali Anil Sinaci ◽  
Paolo Invernizzi ◽  
...  
Keyword(s):  
Real Life ◽  
Case Series ◽  
Background Information ◽  
Data Sets ◽  
Local Data ◽  
Lombardy Region ◽  
Common Information ◽  
Spontaneous Reports ◽  
Wide Range ◽  
Common Information Model

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

scholarly journals Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

2019 ◽  
Vol 78 (8) ◽  
pp. 1127-1134 ◽  
Author(s):  
Paul Martin ◽  
James Ding ◽  
Kate Duffus ◽  
Vasanthi Priyadarshini Gaddi ◽  
Amanda McGovern ◽  
...  
Keyword(s):  
Rheumatic Diseases ◽  
Pathway Analysis ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Healthcare Providers ◽  
Cell Types ◽  
Data Sets ◽  
High Confidence ◽  
Causal Genes ◽  
Additional Cell

ObjectivesThere is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

scholarly journals Alteration of transporter activities in the epididymides of infertile initial segment-specific Pten knockout mice†

2018 ◽  
Vol 99 (3) ◽  
pp. 536-545 ◽  
Author(s):  
Bingfang Xu ◽  
Stephen D Turner ◽  
Barry T Hinton
Keyword(s):  
Male Fertility ◽  
Initial Segment ◽  
Molecular Mechanisms ◽  
Fluorescein Isothiocyanate ◽  
Proximal Region ◽  
Sperm Maturation ◽  
Cellular Functions ◽  
Permeability Characteristics ◽  
Protein Levels ◽  
Tensin Homolog

Abstract A fully functional initial segment, the most proximal region of the epididymis, is important for male fertility. Our previous study generated a mouse model to investigate the importance of initial segment function in male fertility. In that model, phosphatase and tensin homolog (Pten) was conditionally removed from the initial segment epithelium, which resulted in epithelial de-differentiation. When spermatozoa progressed through the de-differentiated epithelial duct, they developed angled flagella, suggesting compromised sperm maturation, which eventually resulted in male infertility. To understand the molecular mechanisms, by which PTEN regulates epididymal sperm maturation, we compared the transcriptome profile of the initial segment between controls and initial segment-specific Pten knockouts and revealed that water, ion, and organic solute transporter activities were one of the top molecular and cellular functions altered following loss of Pten. Alteration in protein levels and localization of several transporters following loss of Pten were also observed by immunofluorescence analysis. Epithelial cells of the initial segment from knockouts were more permeable to fluorescein isothiocyanate–dextran (4000 Da) compared to controls. Interestingly, conditional deletion of Pten from other organs also resulted in changes in transporter activity, suggesting a common role of PTEN in regulation of transporter activity. Taken together, our data support the hypothesis that loss of Pten from the initial segment epithelium results in changes in the transporting and permeability characteristics of the epithelium, which in turn altered the luminal fluid microenvironment that is so important for sperm maturation and male fertility.

scholarly journals The migraine–stroke connection: A genetic perspective

Cephalalgia ◽  
2015 ◽  
Vol 36 (7) ◽  
pp. 658-668 ◽  
Author(s):  
Rainer Malik ◽  
Bendik Winsvold ◽  
Eva Auffenberg ◽  
Martin Dichgans ◽  
Tobias Freilinger
Keyword(s):  
Ischemic Stroke ◽  
Vascular Disease ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Familial Hemiplegic Migraine ◽  
Artery Dissection ◽  
Cervical Artery Dissection ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Disease Manifestation

Background A complex relationship between migraine and vascular disease has long been recognized. The pathophysiological basis underlying this correlation is incompletely understood. Aim The aim of this review is to focus on the migraine–vascular disorders connection from a genetic perspective, illustrating potentially shared (molecular) mechanisms. Results We first summarize the clinical presentation and genetic basis of CADASIL and other monogenic vascular syndromes with migraine as a prominent disease manifestation. Based on data from transgenic mouse models for familial hemiplegic migraine, we then discuss cortical spreading depression as a potential mechanistic link between migraine and ischemic stroke. Finally, we review data from genome-wide association studies, with a focus on overlapping findings with cervical artery dissection, ischemic stroke in general and cardiovascular disease. Conclusion A wealth of data supports a genetic link between migraine and vascular disease. Based on growing high-throughput data-sets, new genotyping techniques and in-depth phenotyping, further insights are expected for the future.

scholarly journals Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing

2020 ◽  
Author(s):  
Kashyap Chhatbar ◽  
Justyna Cholewa-Waclaw ◽  
Ruth Shah ◽  
Adrian Bird ◽  
Guido Sanguinetti
Keyword(s):  
Alternative Splicing ◽  
Dna Sequences ◽  
Molecular Mechanisms ◽  
Data Sets ◽  
Learning Approaches ◽  
Global Regulator ◽  
Major Function ◽  
Quantitative Analyses ◽  
Different Levels

AbstractMeCP2 is an abundant protein in mature nerve cells, where it binds to DNA sequences containing methylated cytosine. Mutations in the MECP2 gene cause the severe neurological disorder Rett syndrome (RTT), provoking intensive study of the underlying molecular mechanisms. Multiple functions have been proposed, one of which involves a regulatory role in splicing. Here we leverage the recent availability of high-quality transcriptomic data sets to probe quantitatively the potential influence of MeCP2 on alternative splicing. Using a variety of machine learning approaches that can capture both linear and non-linear associations, we show that widely different levels of MeCP2 have a minimal effect on alternative splicing in three different systems. Alternative splicing was also apparently indifferent to developmental changes in DNA methylation levels. Our results suggest that regulation of splicing is not a major function of MeCP2. They also highlight the importance of multi-variate quantitative analyses in the formulation of biological hypotheses.

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