Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …
space. There are, however, numerous contexts of considerable scientific interest in which …
[HTML][HTML] On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins
Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and
many diverging properties. IDPs—and intrinsically disordered regions (IDRs) interspersed …
many diverging properties. IDPs—and intrinsically disordered regions (IDRs) interspersed …
Riemannian diffusion models
Diffusion models are recent state-of-the-art methods for image generation and likelihood
estimation. In this work, we generalize continuous-time diffusion models to arbitrary …
estimation. In this work, we generalize continuous-time diffusion models to arbitrary …
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)
We describe AlphaFold, the protein structure prediction system that was entered by the
group A7D in CASP13. Submissions were made by three free‐modeling (FM) methods …
group A7D in CASP13. Submissions were made by three free‐modeling (FM) methods …
Expanding functional protein sequence spaces using generative adversarial networks
D Repecka, V Jauniskis, L Karpus… - Nature Machine …, 2021 - nature.com
De novo protein design for catalysis of any desired chemical reaction is a long-standing goal
in protein engineering because of the broad spectrum of technological, scientific and …
in protein engineering because of the broad spectrum of technological, scientific and …
Normalizing flows on tori and spheres
Normalizing flows are a powerful tool for building expressive distributions in high
dimensions. So far, most of the literature has concentrated on learning flows on Euclidean …
dimensions. So far, most of the literature has concentrated on learning flows on Euclidean …
[KİTAP][B] Statistical shape analysis: with applications in R
A thoroughly revised and updated edition of this introduction to modern statistical methods
for shape analysis Shape analysis is an important tool in the many disciplines where objects …
for shape analysis Shape analysis is an important tool in the many disciplines where objects …
Generative modeling for protein structures
Analyzing the structure and function of proteins is a key part of understanding biology at the
molecular and cellular level. In addition, a major engineering challenge is to design new …
molecular and cellular level. In addition, a major engineering challenge is to design new …
[KİTAP][B] Applications of circular statistics in plant phenology: a case studies approach
LPC Morellato, LF Alberti, IL Hudson - 2010 - Springer
Phenology is the study of recurring biological events and its relationship to climate. Circular
statistics is an area of statistics not very much used by ecologists nor by other researchers …
statistics is an area of statistics not very much used by ecologists nor by other researchers …
Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics
The accurate prediction of thermodynamic properties is crucial in various fields such as drug
discovery and materials design. This task relies on sampling from the underlying Boltzmann …
discovery and materials design. This task relies on sampling from the underlying Boltzmann …