Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning
Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in solution, thus enabling search and pattern-matching capabilities. Being able to control and predict the process of DNA hybridisation is crucial for the ambitious future of Hybrid Molecular-Electronic Computing. Current tools are, however, limited in terms of throughput and applicability to large-scale problems. We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. For this purpose, we introduce an in silico-generated hybridisation dataset of over 2.5 million data points, enabling the use of deep learning. Depending on hardware, we achieve a reduction in inference time ranging from one to over two orders of magnitude compared to the state-of-the-art, while retaining high fidelity. We then discuss the integration of our methods in modern, scalable workflows.
Code (1)
Similar Papers 제목 키워드 기반
Scaling Training Data with Lossy Image Compression
Empirically-determined scaling laws have been broadly successful in predicting the evolution of large machine learning models with training data and number of parameters. As a consequence, they have been useful for optim…
Data CompressionImage CompressionEfficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning
Retrieving images transmitted through multi-mode fibers is of growing interest, thanks to their ability to confine and transport light efficiently in a compact system. Here, we demonstrate machine-learning-based decoding…
Image RetrievalRetrievalVocal Bursts Intensity PredictionVLSI Design Of Advanced Digital Filters
The Cascaded Integrator Comb filters (CIC) find many applications in recent electronic devices such as frequency selection functions in a digital radio or modem and any filter structure that is required to efficiently pr…
UnityEnsemble Reservoir Computing for Dynamical Systems: Prediction of Phase-Space Stable Region for Hadron Storage Rings
We investigate the ability of an ensemble reservoir computing approach to predict the long-term behaviour of the phase-space region in which the motion of charged particles in hadron storage rings is bounded, the so-call…
LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent Training
Digital agents require diverse, large-scale UI trajectories to generalize across real-world tasks, yet collecting such data is prohibitively expensive in both human annotation, infra and engineering perspectives. To this…