AIMC Topic: DNA

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Predicting sequence-specific amplification efficiency in multi-template PCR with deep learning.

Nature communications
Multi-template polymerase chain reaction (PCR) is a critical technique enabling the parallel amplification of diverse DNA molecules, thereby facilitating applications in fields from quantitative molecular biology to DNA data storage. However, non-hom...

Predictive Modeling of DNA Damage Outcomes: Classification of Mutational Determinants Using Augmented Machine Learning Techniques.

Chemical research in toxicology
The mutational outcome of DNA damage as a direct result of constant chemical assault is governed by major factors, including the structure and nature of damage, replication, and repair machinery . The role of the size of the adduct, adduct-flanking b...

Machine Learning-Enhanced Analysis of miRNA Biomarkers for Accurate Breast Cancer Diagnosis Using DNA Seagrass.

Analytical chemistry
As potential biomarkers for breast cancer, microRNAs (miRNAs) have demonstrated significant promise in clinical applications. However, accurate miRNA-based breast cancer diagnosis is hindered by the lack of simple, ultrasensitive, and highly specific...

Enabling Open Machine Learning of Deoxyribonucleic Acid-Encoded Library Selections to Accelerate the Discovery of Small Molecule Protein Binders.

Journal of medicinal chemistry
Machine learning (ML) is increasingly used in DNA-encoded library (DEL) screening for ligand discovery, but its success depends on access to suitable data sets, which are often proprietary and costly. To overcome this, we present the first fully open...

DNA Nanostructures Characterized via Dual Nanopore Resensing.

ACS nano
DNA nanotechnology uses predictable interactions of nucleic acids to precisely engineer complex nanostructures. Characterizing these self-assembled structures at the single-structure level is crucial for validating their design and functionality. Nan...

Predicting chromatin conformation contact maps.

PloS one
Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin a...

Programmable Biointerfaces and Adaptive Functionality in Next-Generation Green Nanomaterials.

ACS synthetic biology
Recent advances in green nanomaterials have primarily focused on mitigating toxicity through passive approaches, yet emerging technologies suggest a transformative paradigm shift toward programmable nanomaterials with dynamic biointerfaces. This Revi...

Machine learning-assisted detection of single-point mutations DNA-templated gold nanoparticle growth.

Nanoscale
Detecting single point mutations, such as PIK3CA mutations, is vital for precision diagnostics but remains challenging due to subtle sequence differences. This study introduces a machine learning-assisted colorimetric biosensor that utilizes DNA-temp...

Development and Validation of an Automated DNA-Encoded Library Screening Data Analysis Platform: PB-DEL Autoscreening Analysis (PB-DELASA).

Journal of chemical information and modeling
Tools available for analyzing next-generation sequencing (NGS) data produced from DNA-encoded library (DEL) screening campaigns are often constrained to customized methods developed internally by individual institutes, which usually generate data spe...

Multiview Deep Learning Framework for Precise Prediction of Transcription Factor Binding Sites.

Journal of chemical information and modeling
Transcription factors (TFs) are essential proteins that regulate gene expression by specifically binding to transcription factor binding sites (TFBSs) within DNA sequences. Their ability to precisely control the transcription process is crucial for u...