ArticleEmerging topics in life sciences2017
Computational biology: deep learning.
Article in Emerging topics in life sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Genomic innovations in cancer prevention, diagnosis, prognosis and precision therapeutics.Frontiers in genetics · 2026Review
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.Journal of experimental botany · 2025Article
- Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis.Biophysical reviews · 2024Review
- Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.eLife · 2024Article
- Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis.Journal of translational medicine · 2024Review
- Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement.Diagnostics (Basel, Switzerland) · 2023Review
- In-silico generation of high-dimensional immune response data in patients using a deep neural network.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2023Article
- Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Plastic pollution of four understudied marine ecosystems: a review of mangroves, seagrass meadows, the Arctic Ocean and the deep seafloor.Emerging topics in life sciences · 2022Review
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Deep-learning microscopy image reconstruction with quality control reveals second-scale rearrangements in RNA polymerase II clusters.PNAS nexus · 2022Article
- Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection.Cancers · 2022Review
- Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature.Plant methods · 2022Article
- Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.SLAS discovery : advancing life sciences R & D · 2021Article
- Deep learning in cancer diagnosis, prognosis and treatment selection.Genome medicine · 2021Review
- Prediction of Genetic Groups withinJournal of fungi (Basel, Switzerland) · 2021Article
- Cancer models in preclinical research: A chronicle review of advancement in effective cancer research.Animal models and experimental medicine · 2021Review
- Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis.Pharmaceutics · 2021Article
- Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder.Sensors (Basel, Switzerland) · 2021Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.
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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.