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0
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ALFAssay: A feed‑forward neural network for quantitative fragmentomics‑based ctDNA profiling in breast cancer.
(journals.plos.org)
|
Stanciu A
…
Ignatiadis M
PLOS Computational Biology
2026-07-27
|
#ctdna
#breast cancer
#machine learning
#fragmentomics
#liquid biopsy
|
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0
|
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Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.
(linkinghub.elsevier.com)
|
Cui H
…
Guo FB
iScience
2026-07-27
|
#drug response
#oncology
#machine learning
#precision medicine
#essentiality
|
|
0
|
|
Designing fiber-gut microbiome interactions with active learning.
(nature.com)
|
Connors BM
…
Venturelli OS
Nature Chemical Biology
2026-07-27
|
#microbiome
#fermentation
#metabolomics
#synthetic biology
#machine learning
|
|
0
|
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Advancing cancer detection and treatment using longitudinal routine clinical data.
(linkinghub.elsevier.com)
|
Liu F
…
Zhang K
Cell
2026-07-27
|
#cancer
#prediction
#machine learning
#prognosis
#imaging
|
|
0
|
|
Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.
(pnas.org)
|
Shan X
…
Newman DK
PNAS
2026-07-27
|
#machine learning
#enzyme discovery
#protein design
#natural products
#bioinformatics
|
|
0
|
|
IDEAL-Age: an interpretable deep learning framework for single-cell resolution profiling of immunological aging.
(link.springer.com)
|
Xu Y
…
Han D
Genome Biology
2026-07-27
|
#transcriptomics
#single-cell
#immunosenescence
#aging
#machine learning
|
|
0
|
|
Development and external validation of a machine learning model for predicting tigecycline-associated drug-induced liver injury.
(linkinghub.elsevier.com)
|
Zhang Y
…
Sun W
iScience
2026-07-27
|
#machine learning
#hepatotoxicity
#adverse_effects
#pharmacovigilance
#amr
|
|
0
|
|
Toward generalizable and interpretable AI in regulatory genomics.
(nature.com)
|
Nagai M
…
Koo PK
Nature Genetics
2026-07-27
|
#machine learning
#regulatory-genomics
#sequence prediction
#interpretability
#variant-effects
|
|
0
|
|
Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.
(nature.com)
|
Xu L
…
Yang L
Nature Communications
2026-07-27
|
#prediction
#aki
#machine learning
#clinical decision support
#explainability
|