A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved protein structure classification using weighted graphlets and deep neural networks.
Article compares different machine learning techniques for protein classification.
TUNet improves protein classification in cell images.
Two proteins are homologous if they have a common evolutionary origin, and the binary classification problem is to identify proteins in a candidate set that are homologous to a particular native protein. The feature (explanatory) variables available for classification are various measures of similarity of proteins. The…
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on thei…
Artificial neural networks (ANNs) have gained a well-deserved popularity among machine learning tools upon their recent successful applications in image- and sound processing and classification problems. ANNs have also been applied for predicting the family or function of a protein, knowing its residue sequence. Here w…
We introduce a new model of proteins, which extends and enhances the traditional graphical representation by associating a combinatorial object called a fatgraph to any protein based upon its intrinsic geometry. Fatgraphs can easily be stored and manipulated as triples of permutations, and these methods are therefore a…
The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes. Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation. The laboratory experiments take a considerable amount of time for annotation of the sequence…
Machine learning predicts signaling peptides from protein star graphs.
Mathematical pipeline identifies structural homology of knotted proteins.
HopGAT improves node classification in sparsely labeled graphs by learning from distant neighbors.
Capsule Networks have great potential to tackle problems in structural biology because of their attention to hierarchical relationships. This paper describes the implementation and application of a Capsule Network architecture to the classification of RAS protein family structures on GPU-based computational resources. …
SGAS improves neural architecture search by choosing and pruning operations greedily.
CRF model improves protein secondary structure prediction.
Complete classification of knotoids up to seven crossings.
As high-throughput biological sequencing becomes faster and cheaper, the need to extract useful information from sequencing becomes ever more paramount, often limited by low-throughput experimental characterizations. For proteins, accurate prediction of their functions directly from their primary amino-acid sequences h…
Classifies uncolored bonded knots with up to 7 singularity points.
Capsule Neural Networks classify graphs from categorical features and relationships.
Graph embedding method captures both local and global network structure.
Motivated by applications in protein function prediction, we consider a challenging supervised classification setting in which positive labels are scarce and there are no explicit negative labels. The learning algorithm must thus select which unlabeled examples to use as negative training points, possibly ending up wit…
Deep learning model predicts protein-ligand binding modes from docking data.
Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties among these objects (binds-to, interacts-with, regulates). This approach has been highly successful owing to the theory, methodology and software that …
During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which …
Bayesian Active Learning improves protein docking accuracy and uncertainty quantification.
The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand base…
Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences. Standard supervised learning algorithms assume accurate in…
A new framework uses text descriptions to improve protein design.
Deep learning models optimize protein sequences.
A new approach to protein language models combines latent space prediction with masked language modeling.
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches …
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning ov…
ProGen models protein sequences for synthetic biology.
New 3D protein analysis methods improve accuracy.
New method detects and compares folding pathways of knotted proteins.
Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins' properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability are necessary to facilitate efficient protein design. However, accuracy of predic…
PANDA predicts protein binding affinity changes from sequences, outperforming existing methods.
Integrase proteins acting on circular double-stranded DNA often change its topology by transforming unknotted circles into torus knots and links. Two systems of tangle equations--corresponding to the two initial DNA sequences--arise when modelling this transformation: direct and inverted. With no a priori assumptions o…
We improve MoE models for classification with rigorous guarantees and practical methods.
A new model explains protein interactions via electron delocalization.
EBM predicts protein conformations at atomic scale using crystallized data.
Knot theory applied to proteins, distinguishing folded linear chains.
Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is essential to understand the principles of cellular organization and explore the causes of various diseases. It is critical to integrate multipl…
Mathematician summarizes protein geometry and mutation effects.
Machine learning predicts protein structures and simulates dynamics.
EGR refines and assesses protein complex structures.
ProteinNet provides a standardized data set for protein structure prediction.
Flexible Kernels for Protein Property Prediction
New method maps protein sequences to embeddings encoding structural information.