15 papers · ranked by Valyu relevance
Toshitake Asabuki, Claudia Clopath
Recurrent neural circuits often face inherent complexities in learning and generating their desired outputs, especially when they initially exhibit chaotic spontaneous activity. While the celebrated FORCE learning rule can train chaotic recurrent networks to produce coherent patterns by suppressing chaos, it requires…
Masaki Tagashira
To capture structural homology in RNAs, predicting RNA structural alignments has been a fundamental framework around RNA science. Learning simultaneous RNA structural alignments in their rich scoring parameterization is an undeveloped subject because evaluating them is computationally expensive in nature. We developed…
Samantha Petti, Nicholas Bhattacharya, Roshan Rao, Justas Dauparas + 5 more
Multiple Sequence Alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any…
Shuichiro Makigaki, Takashi Ishida
Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from…
Tao Fang, Damian Szklarczyk, Radja Hachilif, Christian von Mering
Protein-protein interactions play essential roles in almost all biological processes. The binding interfaces between interacting proteins impose evolutionary constraints, leading to co-evolutionary signals that have successfully been employed to predict protein interactions from multiple sequence alignments (MSAs).…
Sung Jong Lee, Keehyoung Joo, Sangjin Sim, Juyong Lee + 2 more
We built a method of sequence-structure alignment (called CRFalign) which improves upon a base alignment model based on HMM-HMM comparison by employing pairwise conditional random fields (pCRF) in combination with nonlinear scoring functions of structural and sequence features. The total scoring function consists of a…
Leonard Sasse, Casey Paquola, Juergen Dukart, Felix Hoffstaedter + 2 more
Functional connectivity (FC) gradients provide valuable insights into individual differences in brain organization, yet aligning these gradients across individuals poses challenges. Procrustes alignment is often employed to standardize gradients across multiple subjects, but the choice of the number of gradients used…
Hinze Hogendoorn, Anthony N Burkitt
Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical layers are connected by feedback connections carrying predictions, as well as feedforward connections carrying prediction errors. To date, however, predictive coding models have neglected to take into…
Nimrod Serok, Ksenia Polonsky, Haim Ashkenazy, Itay Mayrose + 2 more
Multiple sequence alignment (MSA) inference is a central task in molecular evolution and comparative genomics, and the reliability of downstream analyses, including phylogenetic inference, depends critically on alignment quality. Despite this importance, most widely used MSA methods optimize the sum-of-pairs (SP)…
Andrew McPherson, Sohrab Shah, S. Cenk Sahinalp
We propose that a breakpoint specific alignment procedure would improve breakpoint prediction. Our method, deStruct, uses multiple stages of realignment and clustering to progressively refine breakpoint prediction quality and accuracy. We show using simulated data that deStruct predicts breakpoints with higher…
Hugo Talibart, François Coste
To assign structural and functional annotations to the ever increasing amount of sequenced proteins, the main approach relies on sequence-based homology search methods, e.g. BLAST or the current state-of-the-art methods based on profile Hidden Markov Models (pHMM), which rely on significant alignments of query…
Andreas Grigorjew, Artur Gynter, Fernando Dias, Benjamin Buchfink + 2 more
Sequence alignments have become the foundation of life science research by unlocking biological mechanisms through protein comparisons. Despite its methodological success, most algorithmic innovation in the past decades focused on the optimal alignment problem, while often ignoring information derived from suboptimal…
A. Alcalá, G. Riera, I. García, R. Alberich + 1 more
Several protein-protein interaction networks (PPIN) aligners have been developed during the last 15 years. One of their goals is to help the functional annotation of proteins and the prediction of protein-protein interactions. A correct aligner must preserve the network’s topology as well as the biological coherence.…
Petar Arsic, Christoph Mayer
We report a convolutional transformer neural network that is capable of aligning multiple nucleotide sequences. The neural network is based on the U-Net commonly used in image segmentation which we employ to transform unaligned sequences to aligned sequences. For alignment scenarios our Ali-U-Net neural network has…
James H. Collier, Lloyd Allison, Arthur M. Lesk, Peter J. Stuckey + 2 more
Structural molecular biology depends crucially on computational techniques that compare protein three-dimensional structures and generate structural alignments (the assignment of one-to-one correspondences between subsets of amino acids based on atomic coordinates.) Despite its importance, the structural alignment…