AIMC Topic: DNA Replication

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Ensemble model for neoadjuvant chemotherapy response prediction and treatment sensitivity in TNBC based on DNA replication stress signatures.

Scientific reports
Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer. Although neoadjuvant chemotherapy (NACT) has some effectiveness in TNBC, a portion of patients still do not benefit from them. The critical role of DNA replication ...

A high-resolution, nanopore-based artificial intelligence assay for DNA replication stress in human cancer cells.

Nature communications
DNA replication stress is a hallmark of cancer that is exploited by chemotherapies. Current assays for replication stress have low throughput and poor resolution whilst being unable to map the movement of replication forks genome-wide. We present a n...

Quantifying complexity in DNA structures with high resolution Atomic Force Microscopy.

Nature communications
DNA topology is essential for regulating cellular processes and maintaining genome stability, yet it is challenging to quantify due to the size and complexity of topologically constrained DNA molecules. By combining high-resolution Atomic Force Micro...

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics.

Communications biology
In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous hum...

Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

Nature communications
Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell c...

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides.

Nature communications
Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low...

CNN-Pred: Prediction of single-stranded and double-stranded DNA-binding protein using convolutional neural networks.

Gene
DNA-binding proteins play a vital role in biological activity including DNA replication, DNA packing, and DNA reparation. DNA-binding proteins can be classified into single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins ...

Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation.

PloS one
In contrast to the well characterized mitotic machinery in eukaryotes it seems as if there is no universal mechanism organizing chromosome segregation in all bacteria. Apparently, some bacteria even use combinations of different segregation mechanism...

A deep learning framework combined with word embedding to identify DNA replication origins.

Scientific reports
The DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs ...

A symbolic network-based nonlinear theory for dynamical systems observability.

Scientific reports
When the state of the whole reaction network can be inferred by just measuring the dynamics of a limited set of nodes the system is said to be fully observable. However, as the number of all possible combinations of measured variables and time deriva...