10 papers · ranked by Valyu relevance
Anas Al-okaily, Abdelghani Tbakhi
Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage has never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated…
Kevin D. Volkel, Paul W. Hook, Albert Keung, Winston Timp + 1 more
As nanopore technology reaches ever higher throughput and accuracy, it becomes an increasingly viable candidate for reading out DNA data storage. Nanopore sequencing offers considerable flexibility by allowing long reads, real-time signal analysis, and the ability to read both DNA and RNA. We need flexible and…
Roland Wittler
To index or compare sequences efficiently, often k-mers, i.e., substrings of fixed length k, are used. For efficient indexing or storage, k-mers are often encoded as integers, e.g., applying some bijective mapping between all possible σ^k^ k-mers and the interval [0, σ^k^ −1], where σ is the alphabet size. In many…
Inbal Preuss, Michael Rosenberg, Zohar Yakhini, Leon Anavy
With the world generating digital data at an exponential rate, DNA has emerged as a promising archival medium. It offers a more efficient and long-lasting digital storage solution due to its durability, physical density, and high information capacity. Research in the field includes the development of encoding schemes…
Ibrahim Nawaz, Parv Agarwal, Thomas Heinis
DNA storage is a developing field that uses DNA to archive digital data owing to its superior information density and stability. Although DNA storage has been performed on a significant scale, challenges arise from the synthesis and sequencing of data-encoded oligonucleotides. Synthesis of DNA introduces significant…
Rıza Özçelik, Laura van Weesep, Sarah de Ruiter, Francesca Grisoni
In this work, we introduce peptidy -- a lightweight Python library that facilitates converting peptides (expressed as aminoacid sequences) to numerical representations suited to machine learning. peptidy is free from external dependencies, integrates seamlessly into modern Python environments, and supports a range of…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…