AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

DNA

Showing 311 to 320 of 429 articles

Clear Filters

An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA.

BMC genomics
BACKGROUND: The ability to engineer zinc finger proteins binding to a DNA sequence of choice is essential for targeted genome editing to be possible. Experimental techniques and molecular docking have been successful in predicting protein-DNA interac...

acdc - Automated Contamination Detection and Confidence estimation for single-cell genome data.

BMC bioinformatics
BACKGROUND: A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put...

PACE: Probabilistic Assessment for Contributor Estimation- A machine learning-based assessment of the number of contributors in DNA mixtures.

Forensic science international. Genetics
The deconvolution of DNA mixtures remains one of the most critical challenges in the field of forensic DNA analysis. In addition, of all the data features required to perform such deconvolution, the number of contributors in the sample is widely cons...

Development of amino functionalized carbon coated magnetic nanoparticles and their application to electrochemical detection of hybridization of nucleic acids.

Talanta
In our study, the development of amino functionalized carbon coated magnetic nanoparticles (NH-CC-MNPs) and their usage for electrochemical detection of hybridization of nucleic acids have been aimed. Firstly, NH-CC-MNPs were prepared by coating of p...

Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, sinc...

Restarting and recentering genetic algorithm variations for DNA fragment assembly: The necessity of a multi-strategy approach.

Bio Systems
DNA Fragment assembly - an NP-Hard problem - is one of the major steps in of DNA sequencing. Multiple strategies have been used for this problem, including greedy graph-based algorithms, deBruijn graphs, and the overlap-layout-consensus approach. Thi...

gDNA-Prot: Predict DNA-binding proteins by employing support vector machine and a novel numerical characterization of protein sequence.

Journal of theoretical biology
DNA-binding proteins are the functional proteins in cells, which play an important role in various essential biological activities. An effective and fast computational method gDNA-Prot is proposed to predict DNA-binding proteins in this paper, which ...

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.

Nucleic acids research
Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of fun...

Classifying Force Spectroscopy of DNA Pulling Measurements Using Supervised and Unsupervised Machine Learning Methods.

Journal of chemical information and modeling
Dynamic force spectroscopy (DFS) measurements on biomolecules typically require classifying thousands of repeated force spectra prior to data analysis. Here, we study classification of atomic force microscope-based DFS measurements using machine-lear...