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.
Method optimizes knotting pathways in constrained polymers.
ECV method optimizes ensemble parameters for randomized ensembles.
In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . T…
Novel graph network learns hierarchical network structure.
New method handles missing data using AI for efficient inference.
Study shows torsion grows subexponentially in book of I-bundles but can grow exponentially in non-regular covers.
Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open question. …
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…
A new framework uses text descriptions to improve protein design.
The non-vanishing conjecture implies the abundance conjecture in certain cases.
Deep learning models optimize protein sequences.
Mathematical pipeline identifies structural homology of knotted proteins.
Introduce a thermodynamically informed, temperature-transferable MLCG framework for proteins.
Transforms curves and surfaces for efficient geometric analysis.
ProGen models protein sequences for synthetic biology.
Motivation: Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments. These biclustering techniques have focused on one data source, often gene expres…
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.
A new model explains protein interactions via electron delocalization.
EBM predicts protein conformations at atomic scale using crystallized data.
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…
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.
EGR refines and assesses protein complex structures.
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…
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…
As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem of protein structure classification, which computationally classifies the structures of proteins into pre-defined groups. We develop a weight…
Flexible Kernels for Protein Property Prediction
New method steers protein design towards desired properties.
A new diffusion model generates novel protein backbones without relying on pretrained networks.
Proteins are linear molecular chains that often fold to function. The topology of folding is widely believed to define its properties and function, and knot theory has been applied to study protein structure and its implications. More that 97% of proteins are, however, classified as unknots when intra-chain interaction…
Study of superintegrable systems linked to affine hypersurfaces.
ProtTrans models predict protein features without evolutionary info.
Study improves LLMs for PPI analysis by addressing uncertainty.
Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized data sets that facilitate fair assessment of new methods and lower the barrier to …
Few-step protein backbone generators reduce sampling time by over 20x.
Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions. We propose a multitask learning algorithm addressing both issues. Unlike standard multitask algorithm…
Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between prot…
Method uses network biology to construct gene expression models for cancer.
Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.
Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolution…
Amino acid sequence portrays most intrinsic form of a protein and expresses primary structure of protein. The order of amino acids in a sequence enables a protein to acquire a particular stable conformation that is responsible for the functions of the protein. This relationship between a sequence and its function motiv…
Protein-protein interaction (PPI) prediction is an important problem in machine learning and computational biology. However, there is no data set for training or evaluation purposes, where all the instances are accurately labeled. Instead, what is available are instances of positive class (with possibly noisy labels) a…
Generative model designs highly designable proteins using geometric algebra.
Tail-GNNs improve protein function prediction using relational reinforcement.
A new machine-learned CG model predicts protein structures efficiently.