AIMC Topic: Synthetic Lethal Mutations

Clear Filters Showing 1 to 10 of 11 articles

TARSL: Triple-Attention Cross-Network Representation Learning to Predict Synthetic Lethality for Anti-Cancer Drug Discovery.

IEEE journal of biomedical and health informatics Mar 6, 2025
Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic Lethality (SL) by targeting the SL partner of cancer gene. Since...
Humans Antineoplastic Agents Computational Biology Synthetic Lethal Mutations Machine Learning Neoplasms Drug Discovery
View on PubMed DOI

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-Based Synthetic Lethal Prediction.

IEEE transactions on visualization and computer graphics Nov 25, 2024
Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for in...
Algorithms Neoplasms Computational Biology Humans Neural Networks, Computer Artificial Intelligence Computer Graphics Synthetic Lethal Mutations
View on PubMed DOI

Graph based recurrent network for context specific synthetic lethality prediction.

Science China. Life sciences Oct 12, 2024
The concept of synthetic lethality (SL) has been successfully used for targeted therapies. To further explore SL for cancer therapy, identifying more SL interactions with therapeutic potential are essential. Recently, graph neural network-based deep ...
Neoplasms Deep Learning Synthetic Lethal Mutations Humans Neural Networks, Computer Algorithms
View on PubMed DOI

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

PLoS computational biology Aug 26, 2024
Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a...
Protein Interaction Maps Computational Biology Synthetic Lethal Mutations Algorithms Neoplasms Humans Machine Learning
View on PubMed DOI

An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.

Journal of advanced research Dec 2, 2023
INTRODUCTION: Synthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to ide...
Cell Line, Tumor Mice Antineoplastic Combined Chemotherapy Protocols Synthetic Lethal Mutations Animals Neoplasms Antineoplastic Agents Humans Artificial Intelligence
View on PubMed DOI

A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments.

Nature communications Feb 2, 2022
A genetic knockout can be lethal to one human cell type while increasing growth rate in another. This context specificity confounds genetic analysis and prevents reproducible genome engineering. Genome-wide CRISPR compendia across most common human c...
Cell Line CRISPR-Cas Systems Clustered Regularly Interspaced Short Palindromic Repeats Machine Learning Humans Gene Knockout Techniques Animals Genetic Engineering Genomics Synthetic Lethal Mutations
View on PubMed DOI

Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.

Molecular cancer Aug 28, 2021
BACKGROUND: Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells...
Cell Line, Tumor Genomics Computational Biology Genetic Predisposition to Disease Synthetic Lethal Mutations Humans Models, Biological Machine Learning Disease Susceptibility Neoplasms Gene Expression Profiling
View on PubMed DOI

SLMF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization.

IEEE/ACM transactions on computational biology and bioinformatics Apr 9, 2019
Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. The...
Humans Machine Learning Synthetic Lethal Mutations Protein Interaction Maps Algorithms Computational Biology Neoplasms Gene Ontology Antineoplastic Agents
View on PubMed DOI

Identification of synthetic lethality based on a functional network by using machine learning algorithms.

Journal of cellular biochemistry Aug 20, 2018
Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 ge...
Synthetic Lethal Mutations Genes, Neoplasm RNA Interference A549 Cells Data Accuracy HeLa Cells Sensitivity and Specificity Humans Gene Editing Neoplasms Machine Learning Gene Ontology Computational Biology
View on PubMed DOI

Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.

Briefings in bioinformatics Mar 4, 2025
Synthetic lethality (SL) is a promising gene interaction for cancer therapy. Recent SL prediction methods integrate knowledge graphs (KGs) into graph neural networks (GNNs) and employ attention mechanisms to extract local subgraphs as explanations fo...
Synthetic Lethal Mutations Humans Graph Neural Networks Neural Networks, Computer Neoplasms Algorithms Computational Biology
View on PubMed DOI
Popular Topics
Recent Journals