ReviewComputational and structural biotechnology journal2017
Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.
Review in Computational and structural biotechnology journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- In-silico matching of SARS-CoV-2 genes and oligonucleotides using the Simple Oligo Matching Tool.Genes & genomics · 2025Article
- RabbitSketch: a high-performance sketching library for genome analysis.Bioinformatics (Oxford, England) · 2025Article
- Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime.Exploration of targeted anti-tumor therapy · 2024Review
- A comparison of machine learning methods for quantifying self-grooming behavior in mice.Frontiers in behavioral neuroscience · 2024Article
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- Canis MitoSNP database: a functional tool useful for comparative analyses of human and canine mitochondrial genomes.Journal of applied genetics · 2023Article
- TheAnnual review of biomedical data science · 2023Review
- The past, present and future of genomics and bioinformatics: A survey of Brazilian scientists.Genetics and molecular biology · 2022Article
- Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.PeerJ. Computer science · 2021Article
- Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma.Frontiers in immunology · 2021Article
- Gene Expression Analysis Platform (GEAP): A highly customizable, fast, versatile and ready-to-use microarray analysis platform.Genetics and molecular biology · 2021Article
- Integrative Analysis of MAPK14 as a Potential Biomarker for Cardioembolic Stroke.BioMed research international · 2020Article
- Exploring TCGA database for identification of potential prognostic genes in stomach adenocarcinoma.Cancer cell international · 2020Article
- A Genocentric Approach to Discovery of Mendelian Disorders.American journal of human genetics · 2019Article
- Synthetic biology for fibres, adhesives and active camouflage materials in protection and aerospace.MRS communications · 2019Article
- The High-Throughput Analyses Era: Are We Ready for the Data Struggle?High-throughput · 2018Article
- Genome Sequencing of Ancient Plant Remains: Findings, Uses and Potential Applications for the Study and Improvement of Modern Crops.Frontiers in plant science · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.