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Multi-omics Analysis
Published in Altuna Akalin, Computational Genomics with R, 2020
The property displayed above, where each feature is predominantly associated with only a single factor, is termed disentangledness, i.e. it leads to disentangled latent variable representations, as changing one input feature only affects a single latent variable. This property is very desirable as it greatly simplifies the biological interpretation of modules. Here, we have two modules with a set of co-occurring molecular signatures which merit deeper investigation into the mechanisms by which these different omics features are related. For this reason, NMF is widely used in computational biology today.
Integrated system biology approaches to fetal medicine problems
Published in Moshe Hod, Vincenzo Berghella, Mary E. D'Alton, Gian Carlo Di Renzo, Eduard Gratacós, Vassilios Fanos, New Technologies and Perinatal Medicine, 2019
Jezid Miranda, Fátima Crispi, Eduard Gratacós
Biomarker discovery has played an important role in medicine and is defined as an indicator that “signals” events in biological samples or systems. It is clear now that pregnancy complications such as preeclampsia or spontaneous preterm birth are multifactorial diseases, and no single biomarker can predict at the population level the risk of these diseases due to their complex etiology. Perhaps the biggest difference between classical approaches and computational biology is the move from hypothesis-directed toward hypothesis-generating research combining clinical information with big data generated by omics techniques (Figure 12.1). Yet, several challenges to this approach need to be taken into consideration.
Biological data: The use of -omics in outcome models
Published in Issam El Naqa, A Guide to Outcome Modeling in Radiotherapy and Oncology, 2018
Issam El Naqa, Sarah L. Kerns, James Coates, Yi Luo, Corey Speers, Randall K. Ten Haken, Catharine M.L. West, Barry S. Rosenstein
There are no dedicated web resources for outcome modeling studies in oncology per se. Nevertheless, oncology biological markers studies can still benefit from existing bioinformatics resources for pharmacogenomic studies that contain databases and tools for genomic, proteomic, and functional analysis as reviewed by Yan [250]. For example, the National Center for Biotechnology Information (NCBI) site hosts databases such as GenBank, dbSNP, Online Mendelian Inheritance in Man (OMIM), and genetic search tools such as BLAST. In addition, the Protein Data Bank (PDB) and the program CPHmodels are useful for protein structure three-dimensional modeling. The Human Genome Variation Database (HGVbase) contains information on physical and functional relationships between sequence variations and neighboring genes. Pattern analysis using PROSITE and Pfam databases can help correlate sequence structures to functional motifs such as phosphorylation [250]. Biological pathways construction and analysis is an emerging field in computational biology that aims to bridge the gap between biomarkers findings in clinical studies with underlying biological processes. Several public databases and tools are being established for annotating and storing known pathways such as KEGG and Reactome projects or commercial ones such as the IPA or MetaCore [251]. Statistical tools are used to properly map data from gene/protein differential experiments into the different pathways such as mixed effect models [252] or enrichment analysis [253].
The National PTSD Brain Bank: Progress, Promise, and Vision
Published in Psychiatry, 2022
These new experimental modalities generate enormous volumes of data, far exceeding the storage and computational capabilities of modern personal computers. This necessitates the use of high performance and cloud computing infrastructure to store, harmonize, and analyze the data. These new computing technologies are also evolving rapidly, and the knowledge and skills needed to leverage them are substantial and in high demand. Integrating data from different subjects, modalities, and brain regions holds great potential to further our understanding of the PTSD brain. The bioinformatic and computational biology approaches needed to achieve this integration are currently at the cutting edge of methodological development, and we are only just beginning to understand how to synthesize these heterogeneous data types together into knowledge and understanding.
Investigating the role of EGF-CFC gene family in recurrent pregnancy loss through bioinformatics and molecular approaches
Published in Systems Biology in Reproductive Medicine, 2021
João Matheus Bremm, Juliano André Boquett, Marcus Silva Michels, Thayne Woycinck Kowalski, Flávia Gobetti Gomes, Fernanda Sales Luiz Vianna, Maria Teresa Vieira Sanseverino, Lucas Rosa Fraga
To the best of our knowledge, this was the first study to evaluate the EGF-CFC family in RPL. This study included multiple approaches, such as expression analysis, case-control, network, and ontology analysis. The use of computational biology combined with molecular biology are complementary approaches that can help to a better understanding of a given biological condition. Even though the precise molecular mechanisms are still unknown, the gene expression data here presented suggest that TDGF1 and CFC1 genes might play a role in RPL. Since TDGF1 and CFC1 are highly conserved genes, analysis in their upstream/promoter regions or in their regulatory pathways could help to better understand their regulation and, hence, their role in RPL. Further studies on these genes, as well as in genes in related signaling pathways are necessary to elucidate the mechanisms that could lead to the RPL.
The value and consequences of using public health technology assessments for private payer decision-making in Canada: one size does not fit all
Published in Journal of Medical Economics, 2019
Louisa Pericleous, Mo Amin, Ron Goeree
Over the past century, the life expectancy of the average Canadian has increased by roughly 20 years1. This improvement has been attributable in large measure to advances in medicine, including a revolution in the use of therapeutic and prescription drugs2,3. In fact, the pace of scientific discovery in areas like genomics, molecular, and computational biology has resulted in a proliferation of innovative medicines. While these innovations offer enormous benefits to patients and to society at large, slower economic growth, coupled with demographic changes, has been projected in many developed economies4, and rising spending on new health technologies is of concern for many jurisdictions. For Canada, this economic projection raises concerns related to the funding and evaluation of innovative medicines and new health technologies, in particular for prescription drugs.