While the genome for a given organism stores the information necessary for the organism to function and flourish it is the proteins that are encoded by the genome that perhaps more than anything else characterize the phenotype for that organism. It is therefore not surprising that one of the many approaches to understanding and predicting protein folding and properties has come from genomics and more specifically from multiple sequence alignments. In this work I explore ways in which data derived from sequence alignment data can be used to investigate in a predictive way three different aspects of protein structure: secondary structures, inter-residue contacts and the dynamics of switching between different states of the protein. In particular the use of Kolmogorov complexity has identified a novel pathway towards achieving these goals.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4391790 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0119306 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Deep neural networks have an inbuilt Occam's razor.
Nat Commun
January 2025
Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK.
The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the data. To disentangle these three components for supervised learning, we apply a Bayesian picture based on the functions expressed by a DNN. The prior over functions is determined by the network architecture, which we vary by exploiting a transition between ordered and chaotic regimes.
View Article and Find Full Text PDFKan-AAE-driven synthetic SERS spectra generation method for Precise cancer identification.
Spectrochim Acta A Mol Biomol Spectrosc
January 2025
School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China. Electronic address:
Surface-Enhanced Raman Spectroscopy (SERS) is gaining popularity in cancer detection studies because it offers a non-invasive and rapid approach. Label-free SERS detection techniques often needs machine learning, which depends on adequate data for training. The scarcity of blood serum samples from cancer patients, due to challenges in collection linked to confidentiality concerns and other restrictions, can result in model overfitting and poor generalization ability.
View Article and Find Full Text PDFHKAN: A Hybrid Kolmogorov-Arnold Network for Robust Fabric Defect Segmentation.
Sensors (Basel)
December 2024
School of Computer and Artificial Intelligence, Wuhan Textile Unversity, Wuhan 430200, China.
Currently, fabric defect detection methods predominantly rely on CNN models. However, due to the inherent limitations of CNNs, such models struggle to capture long-distance dependencies in images and fail to accurately detect complex defect features. While Transformers excel at modeling long-range dependencies, their quadratic computational complexity poses significant challenges.
View Article and Find Full Text PDFLong-read sequencing of hundreds of diverse brains provides insight into the impact of structural variation on gene expression and DNA methylation.
bioRxiv
December 2024
Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
Structural variants (SVs) drive gene expression in the human brain and are causative of many neurological conditions. However, most existing genetic studies have been based on short-read sequencing methods, which capture fewer than half of the SVs present in any one individual. Long-read sequencing (LRS) enhances our ability to detect disease-associated and functionally relevant structural variants (SVs); however, its application in large-scale genomic studies has been limited by challenges in sample preparation and high costs.
View Article and Find Full Text PDFSimulating fish autonomous swimming behaviours using deep reinforcement learning based on Kolmogorov-Arnold Networks.
Bioinspir Biomim
January 2025
Southwest Research Institute for Hydraulic and Water Transport Engineering, Chongqing Jiaotong University, Chongqing, People's Republic of China.
The study of fish swimming behaviours and locomotion mechanisms holds significant scientific and engineering value. With the rapid advancements in artificial intelligence, a new method combining deep reinforcement learning (DRL) with computational fluid dynamics has emerged and been applied to simulate the fish's adaptive swimming behaviour, where the complex fish behaviour is decoupled to focus on the fish's response to the hydrodynamic field, and the simulation is driven by reward-based objectives to model the fish's swimming behaviour. However, the scale of this cross-disciplinary method is directly affected by the efficiency of the DRL model.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!