ReviewBriefings in bioinformatics2017
Graphics processing units in bioinformatics, computational biology and systems biology.
Review in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 148 citations in OpenAlex.
- GPU-Accelerated Artificial Intelligence Applications in Cancer Diagnosis, Imaging, and Treatment Planning.Asian Pacific journal of cancer prevention : APJCP · 2025Review
- Heterogeneous bioinformatic data encryption on portable devices.Scientific reports · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Mining biology for antibiotic discovery.PLoS biology · 2024Article
- Article
- Torch-eCpG: a fast and scalable eQTM mapper for thousands of molecular phenotypes with graphical processing units.BMC bioinformatics · 2024Article
- Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code.BMC medical research methodology · 2024Article
- BMX: Biological modelling and interface exchange.Scientific reports · 2023Article
- Article
- Efficient enumeration-selection computational strategy for adaptive chemistry.Scientific reports · 2022Article
- Structure-based screening of natural product libraries in search of potential antiviral drug-leads as first-line treatment to COVID-19 infection.Microbial pathogenesis · 2022Article
- Progress on deep learning in digital pathology of breast cancer: a narrative review.Gland surgery · 2022Review
- Mini-batch optimization enables training of ODE models on large-scale datasets.Nature communications · 2022Article
- Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs.Frontiers in medicine · 2022Review
- Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps.PloS one · 2022Article
- Article
- FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.PLoS computational biology · 2021Article
- Identification and verification of HCAR3 and INSL5 as new potential therapeutic targets of colorectal cancer.World journal of surgical oncology · 2021Article
- 0s and 1s in marine molecular research: a regional HPC perspective.GigaScience · 2021Article
- Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.International journal of molecular sciences · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.
Indexed as
Identifiers
What OpenQuestion holds
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.