ArticlePLoS computational biology2024
Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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Who cites it
14 citing papers in PubMed.
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Brieflow: an integrated computational pipeline for high-throughput analysis of optical pooled screening data.Nature communications · 2026Article
- Identifying and targeting abnormal mitochondrial localization associated with psychosis.bioRxiv : the preprint server for biology · 2026Article
- TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects.Nature biotechnology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.Communications biology · 2026Article
- Genetic Convergence Analysis of CRISPR Perturbations Deciphers Gene Functional Similarity.bioRxiv : the preprint server for biology · 2025Article
- Integrated spatial morpho-transcriptomics predicts functional traits in pancreatic cancer.Science advances · 2025Article
- Article
- Identification of RMP24 and RMP64, human ribonuclease MRP-specific protein components.Cell reports · 2025Article
- A versatile information retrieval framework for evaluating profile strength and similarity.Nature communications · 2025Article
- A versatile information retrieval framework for evaluating profile strength and similarity.bioRxiv : the preprint server for biology · 2025Article
- A genome-wide atlas of human cell morphology.Nature methods · 2025Article
- MorphoDiff: Cellular Morphology Painting with Diffusion Models.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The continued scaling of genetic perturbation technologies combined with high-dimensional assays such as cellular microscopy and RNA-sequencing has enabled genome-scale reverse-genetics experiments that go beyond single-endpoint measurements of growth or lethality. Datasets emerging from these experiments can be combined to construct perturbative "maps of biology", in which readouts from various manipulations (e.g., CRISPR-Cas9 knockout, CRISPRi knockdown, compound treatment) are placed in unified, relatable embedding spaces allowing for the generation of genome-scale sets of pairwise comparisons. These maps of biology capture known biological relationships and uncover new associations which can be used for downstream discovery tasks. Construction of these maps involves many technical choices in both experimental and computational protocols, motivating the design of benchmark procedures to evaluate map quality in a systematic, unbiased manner. Here, we (1) establish a standardized terminology for the steps involved in perturbative map building, (2) introduce key classes of benchmarks to assess the quality of such maps, (3) construct 18 maps from four genome-scale datasets employing different cell types, perturbation technologies, and data readout modalities, (4) generate benchmark metrics for the constructed maps and investigate the reasons for performance variations, and (5) demonstrate utility of these maps to discover new biology by suggesting roles for two largely uncharacterized genes.
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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.