Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs.

ABSTRACT: Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.

SUBMITTER: Jiang P

PROVIDER: S-EPMC6655478 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs.

Jiang Peng P Sellers William R WR Liu X Shirley XS

Annual review of biomedical data science 20180425

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeuti ...[more]

PMID: 31342013

Similar Datasets

Modeling Familial Cancer with iPSC Approaches

Project description:In vitro modeling of human disease has recently become feasible with the adoption of induced pluripotent stem cell (iPSC) technology. Here, we established patient-derived iPSCs from an Li-Fraumeni Syndrome (LFS) family and investigated the role of mutant p53 in the development of osteosarcoma (OS). Several members of this family carried a heterozygous p53(G245D) mutation and presented with a broad spectrum of tumors including OS. Osteoblasts (OBs) differentiated from iPSC-derived mesenchymal stem cells (MSCs) recapitulated OS features including defective osteoblastic differentiation (OB differentiation) as well as tumorigenic ability. Systematic analyses revealed that the expression of genes enriched in LFS-derived OBs strongly correlated with decreased time to tumor recurrence and poor patient survival. In silico cytogenetic region enrichment analysis (CREA) demonstrated that LFS-derived OBs do not have genomic rearrangements and hence are a particularly valuable tool for elucidating early oncogenic events prior to the accumulation of secondary alterations. LFS OBs exhibited impaired upregulation of the imprinted gene H19 during osteogenesis. Restoration of H19 expression in LFS OBs facilitated osteogenic differentiation and repressed tumorigenic potential. By integrating human imprinted gene network (IGN) and functional genomic analyses, we found that H19-mediates suppression of LFS-associated OS through the IGN component DECORIN (DCN). Downregulation of DCN impairs H19-mediated osteogenic differentiation and tumor suppression. In summary, these findings demonstrate the feasibility of studying inherited human cancer syndromes with iPSCs and also provide molecular insights into the role of the IGN in p53 mutation-mediated tumorigenesis. mRNAseq profiling during mesenchymal stem cell differentiation to osteoblasts.

2015-04-10 | E-GEOD-58123 | biostudies-arrayexpress

Is dementia research ready for big data approaches?

Project description:The "big data" paradigm has gained a lot of attention recently, in particular in those areas of biomedicine where we face clear unmet medical needs. Coined as a new paradigm for complex problem solving, big data approaches seem to open promising perspectives in particular for a better understanding of complex diseases such as Alzheimer's disease and other dementias. In this commentary, we will provide a brief overview on big data principles and the potential they may bring to dementia research, and - most importantly - we will do a reality check in order to provide an answer to the question of whether dementia research is ready for big data approaches.

| S-EPMC4476175 | biostudies-literature

"Big Data" Approaches for Prevention of the Metabolic Syndrome.

Project description:Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these "big data", the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of "big data" applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

| S-EPMC9095427 | biostudies-literature

Modeling Familial Cancer with iPSC Approaches

2015-04-10 | GSE58123 | GEO

The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics.

Project description:The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) has standardized data submission and dissemination of mass spectrometry proteomics data worldwide since 2012. In this paper, we describe the main developments since the previous update manuscript was published in Nucleic Acids Research in 2017. Since then, in addition to the four PX existing members at the time (PRIDE, PeptideAtlas including the PASSEL resource, MassIVE and jPOST), two new resources have joined PX: iProX (China) and Panorama Public (USA). We first describe the updated submission guidelines, now expanded to include six members. Next, with current data submission statistics, we demonstrate that the proteomics field is now actively embracing public open data policies. At the end of June 2019, more than 14 100 datasets had been submitted to PX resources since 2012, and from those, more than 9 500 in just the last three years. In parallel, an unprecedented increase of data re-use activities in the field, including 'big data' approaches, is enabling novel research and new data resources. At last, we also outline some of our future plans for the coming years.

| S-EPMC7145525 | biostudies-literature

Computationally efficient joint species distribution modeling of big spatial data.

Project description:The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive process and nearest-neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant data set of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the hierarchical modeling of species communities framework.

| S-EPMC7027487 | biostudies-literature

Systems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective.

Project description:Big Data, and in particular Electronic Health Records, provide the medical community with a great opportunity to analyze multiple pathological conditions at an unprecedented depth for many complex diseases, including diabetes. How can we infer on diabetes from large heterogeneous datasets? A possible solution is provided by invoking next-generation computational methods and data analytics tools within systems medicine approaches. By deciphering the multi-faceted complexity of biological systems, the potential of emerging diagnostic tools and therapeutic functions can be ultimately revealed. In diabetes, a multidimensional approach to data analysis is needed to better understand the disease conditions, trajectories and the associated comorbidities. Elucidation of multidimensionality comes from the analysis of factors such as disease phenotypes, marker types, and biological motifs while seeking to make use of multiple levels of information including genetics, omics, clinical data, and environmental and lifestyle factors. Examining the synergy between multiple dimensions represents a challenge. In such regard, the role of Big Data fuels the rise of Precision Medicine by allowing an increasing number of descriptions to be captured from individuals. Thus, data curations and analyses should be designed to deliver highly accurate predicted risk profiles and treatment recommendations. It is important to establish linkages between systems and precision medicine in order to translate their principles into clinical practice. Equivalently, to realize their full potential, the involved multiple dimensions must be able to process information ensuring inter-exchange, reducing ambiguities and redundancies, and ultimately improving health care solutions by introducing clinical decision support systems focused on reclassified phenotypes (or digital biomarkers) and community-driven patient stratifications.

| S-EPMC5526830 | biostudies-other

Big Data Analytical Approaches to the NACC Dataset: Aiding Preclinical Trial Enrichment.

Project description:BACKGROUND:Clinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive. METHODS:We used only noninvasive clinical variables collected in the National Alzheimer's Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years. RESULTS:A total of 31,872 unique subjects, 748 clinical variables, and additional 128 derived variables in NACC data sets were used. About 15 noninvasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval. CONCLUSIONS:We achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in preclinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.

| S-EPMC5854492 | biostudies-literature

Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis.

Project description:Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale.

| S-EPMC5428445 | biostudies-literature

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data.

Project description:Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

| S-EPMC8778254 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data