ArticleIEEE/ACM transactions on computational biology and bioinformatics
Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Methods for processing and analyzing images of vascularized micro-organ and tumor systems.Frontiers in bioengineering and biotechnology · 2025Article
- Artificial intelligence as a tool in drug discovery and development.World journal of experimental medicine · 2024Review
- Vascularized organoid-on-a-chip: design, imaging, and analysis.Angiogenesis · 2024Review
- Improving tumor microenvironment assessment in chip systems through next-generation technology integration.Frontiers in bioengineering and biotechnology · 2024Review
- Establishing a Physiologic Human Vascularized Micro-Tumor Model for Cancer Research.Journal of visualized experiments : JoVE · 2023Article
- Vitreoretinal Surgical Instrument Tracking in Three Dimensions Using Deep Learning.Translational vision science & technology · 2023Article
- Organ-on-a-chip meets artificial intelligence in drug evaluation.Theranostics · 2023Review
- Weakly Supervised Polyp Segmentation in Colonoscopy Images Using Deep Neural Networks.Journal of imaging · 2022Article
- Computer vision-aided bioprinting for bone research.Bone research · 2022Review
- Deep learning to enable color vision in the dark.PloS one · 2022Article
- Deep Learning-Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity.Translational vision science & technology · 2021Article
- Patient-Specific Organoid and Organ-on-a-Chip: 3D Cell-Culture Meets 3D Printing and Numerical Simulation.Advanced biology · 2021Review
- Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches.Briefings in bioinformatics · 2021Article
- Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images.Assay and drug development technologies · 2020Article
- Classifying shoulder implants in X-ray images using deep learning.Computational and structural biotechnology journal · 2020Article
- Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration.Small (Weinheim an der Bergstrasse, Germany) · 2019Review
- Monitoring of Microphysiological Systems: Integrating Sensors and Real-Time Data Analysis toward Autonomous Decision-Making.ACS sensors · 2019Article
- Strategies for using mathematical modeling approaches to design and interpret multi-organ microphysiological systems (MPS).APL bioengineering · 2019Review
- Applications of tumor chip technology.Lab on a chip · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Likely drug candidates which are identified in traditional pre-clinical drug screens often fail in patient trials, increasing the societal burden of drug discovery. A major contributing factor to this phenomenon is the failure of traditional in vitro models of drug response to accurately mimic many of the more complex properties of human biology. We have recently introduced a new microphysiological system for growing vascularized, perfused microtissues that more accurately models human physiology and is suitable for large drug screens. In this work, we develop a machine learning model that can quickly and accurately flag compounds which effectively disrupt vascular networks from images taken before and after drug application in vitro. The system is based on a convolutional neural network and achieves near perfect accuracy while committing potentially no expensive false negatives.
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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.