Investigating Genetic and Familial Risks in Childhood ALL: A Longitudinal Virtual Study Using aiHumanoid Simulations of TEL-AML1 Gene Fusion
Danter, W. R.
Show abstract
Acute Lymphoblastic Leukemia (ALL) is the most common childhood cancer, presenting significant challenges in early diagnosis and effective treatment. Recent advances in gene profiling have identified a pivotal role for the TEL-AML1 (ETV6-RUNX1) gene fusion, present in about one quarter of pediatric ALL cases. This gene fusion is often associated with a more benign course and requires additional genetic abnormalities known as Second Hits, to cause overt leukemia. Our current study utilizes Fuzzy Cognitive Maps (FCMs) to model the intricate genetic interplays and potential progression paths of ALL in children, focusing on those with the TEL-AML1 gene fusion within a familial leukemia context. In this virtual longitudinal study, we leverage cohorts of aiHumanoid simulations to explore the foundational role of the TEL-AML1 fusion gene in the genesis of ALL, examining its impact when combined with a family history of leukemia. The simulations predict how genetic and environmental factors might influence disease onset and progression, providing a platform for early diagnosis and progression monitoring. Our findings suggest that family history significantly increases the risk and modifies the disease course in carriers of the TEL-AML1 fusion gene, indicating a need for targeted surveillance and potential early interventions in these high-risk groups.
Matching journals
The top 12 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Investigation of HLA susceptibility alleles and genotypes with hematological disease among Chinese Han population 93%
- Prediction of myeloid malignant cells in Fanconi anemia using machine learning 92%
- LRRC33 is a novel binding and regulating protein of TGF-β1 function in human acute myeloid leukemia cells 92%
Similar papers in this journal
- Genome-wide association analyses identify variants in IRF4 associated with acute myeloid leukemia and myelodysplastic syndrome susceptibility 92%
- Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation 92%
- Transcriptome analyses of β-thalassemia -28 (A>G) mutation using isogenic cell models generated by CRISPR/Cas9 and asymmetric single-stranded oligodeoxynucleotides (assODN) 90%
Similar papers in this journal
- Stratified computational meta-analysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures 95%
- Cas9-directed long-read sequencing to resolve optical genome mapping findings in leukemia diagnostics. 92%
- Interplay of IL6 and CRIM1 on thiopurine-induced neutropenia in leukemic patients with wild-type NUDT15 and TPMT 91%
Similar papers in this journal
- Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning 92%
- HDAC3 inhibition as a therapeutic strategy in T-cell acute lymphoblastic leukemia via the TYK2-STAT1-BCL2 signaling pathway 91%
- Transcriptional Regulatory Logic Orchestrating Lymphoid and Myeloid Cell Fate Decisions 90%
Similar papers in this journal
- Tyrosine kinase inhibitor independent gene expression signature in CML offers new targets for LSPC eradication therapy 93%
- Modeling Global Genomic Instability in Chronic Myeloid Leukemia (CML) using patient-derived induced pluripotent stem cells (iPSC) 93%
- ARPP19 promotes MYC expression and associates with patient relapse in acute myeloid leukemia 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.