3,131 research outputs found
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
Advances in sequencing techniques have led to exponential growth in
biological data, demanding the development of large-scale bioinformatics
experiments. Because these experiments are computation- and data-intensive,
they require high-performance computing (HPC) techniques and can benefit from
specialized technologies such as Scientific Workflow Management Systems (SWfMS)
and databases. In this work, we present BioWorkbench, a framework for managing
and analyzing bioinformatics experiments. This framework automatically collects
provenance data, including both performance data from workflow execution and
data from the scientific domain of the workflow application. Provenance data
can be analyzed through a web application that abstracts a set of queries to
the provenance database, simplifying access to provenance information. We
evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree
assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a
RASopathy analysis workflow. We analyze each workflow from both computational
and scientific domain perspectives, by using queries to a provenance and
annotation database. Some of these queries are available as a pre-built feature
of the BioWorkbench web application. Through the provenance data, we show that
the framework is scalable and achieves high-performance, reducing up to 98% of
the case studies execution time. We also show how the application of machine
learning techniques can enrich the analysis process
BioCloud Search EnGene: Surfing Biological Data on the Cloud
The massive production and spread of biomedical data around the web introduces new challenges related to identify computational approaches for providing quality search and browsing of web resources. This papers presents BioCloud Search EnGene (BSE), a cloud application that facilitates searching and integration of the many layers of biological information offered by public large-scale genomic repositories. Grounding on the concept of dataspace, BSE is built on top of a cloud platform that severely curtails issues associated with scalability and performance. Like popular online gene portals, BSE adopts a gene-centric approach: researchers can find their information of interest by means of a simple “Google-like” query interface that accepts standard gene identification as keywords. We present BSE architecture and functionality and discuss how our strategies contribute to successfully tackle big data problems in querying gene-based web resources. BSE is publically available at: http://biocloud-unica.appspot.com/
Evaluation patterns and algorithm for cancer identifications using dynamic clustering
The domain of knowledge discovery and deep data extraction is quite prominent and used in assorted domains including engineering, mathematics and even in medical diagnosis. A number of benchmark datasets are available in which huge research work is going on with the enormous aspects of genomics that is associated with the medical data analytics. In this research manuscript, the work presents the evaluation patterns and the approaches which are used for the cancer identification with the use of dynamic clustering and deep data analytics. The work is having the elements with the medical datasets and their key features by which the training of data in the data mining algorithm can be integrated and then the overall predictions can be done on assorted parameters
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