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22 papers · ranked by Valyu relevance

01
PPubMed23 Jul 2025
Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks
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…
83relevance
02
BbioRxiv17 Jul 2024
Taming the chaos gently: a Predictive Alignment learning rule in recurrent neural networks
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…
81relevance
03
PPubMed4 Jul 2007
Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method
Morten Nielsen, Claus Lundegaard, Ole Lund
Background Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules.…
81relevance
04
AarXiv25 Aug 2025
Aligning the Evaluation of Probabilistic Predictions with Downstream Value
Shahroudi, Novin, Komisarenko, Viacheslav + 2 more
Every prediction is ultimately used in a downstream task. Consequently, evaluating prediction quality is more meaningful when considered in the context of its downstream use. Metrics based solely on predictive performance often diverge from measures of real-world downstream impact. Existing approaches incorporate the…
79relevance
05
BbioRxiv28 Apr 2022
ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring
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…
76relevance
06
PPubMed24 Jan 2014
Sequence alignment by passing messages
Byung-Jun Yoon
Background Sequence alignment has become an indispensable tool in modern molecular biology research, and probabilistic sequence alignment models have been shown to provide an effective framework for building accurate sequence alignment tools. One such example is the pair hidden Markov model (pair-HMM), which has been…
74relevance
07
PPubMed28 Sep 2017
TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs
Zhen Tan, Yinghan Fu, Gaurav Sharma, David H. Mathews
This paper presents TurboFold II, an extension of the TurboFold algorithm for predicting secondary structures for multiple RNA homologs. TurboFold II augments the structure prediction capabilities of TurboFold by additionally providing multiple sequence alignments. Probabilities for alignment of nucleotide positions…
74relevance
08
PPubMed12 Mar 2024
Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration
Tao Fang, Damian Szklarczyk, Radja Hachilif, Christian von Mering
Protein-protein interactions (PPIs) play essential roles in most biological processes. The binding interfaces between interacting proteins impose evolutionary constraints that have successfully been employed to predict PPIs from multiple sequence alignments (MSAs). To construct MSAs, critical choices have to be made…
73relevance
09
CchemRxiv28 Jul 2025
Relating model performance to embedding distributions in molecular machine learning
Authors not listed
In molecular machine learning, the choice of the representation of molecules can have a significant impact on model performance. However, understanding the root causes of these performance differences often proves challenging. One promising approach to explore model behavior is representational alignment, which…
73relevance
10
AarXiv2023
Conditioning Predictive Models: Risks and Strategies
Evan Hubinger, Adam S. Jermyn, Johannes Treutlein, Rubi Hudson + 1 more
'Kate Woolverton'] Unfortunately, such approaches also raise a variety of potentially fatal safety problems, particularly surrounding situations where predictive models predict the output of other AI systems, potentially unbeknownst to us. There are numerous potential solutions to such problems, however, primarily via…
72relevance
11
BbioRxiv30 Jul 2023
Clade-wise alignment integration improves co-evolutionary signals for protein-protein interaction prediction
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).…
72relevance
12
PPubMed22 Nov 2024
Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions
Maryam Gillani, Gianluca Pollastri
Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular…
72relevance
13
PPubMed1 Nov 2018
Motif-Aware PRALINE: Improving the alignment of motif regions MA-PRALINE: Motif-aware alignment
Maurits Dijkstra, Punto Bawono, Sanne Abeln, K. Anton Feenstra + 3 more
'Wan Fokkink' 'Jaap Heringa' 'Ilya Ioshikhes'] Protein or DNA motifs are sequence regions which possess biological importance. These regions are often highly conserved among homologous sequences. The generation of multiple sequence alignments (MSAs) with a correct alignment of the conserved sequence motifs is still…
71relevance
14
BbioRxiv5 Feb 2026
A deep-learning-based score to evaluate multiple sequence alignments
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)…
71relevance
15
PPubMed1 Aug 2019
Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit
Maurits J J Dijkstra, Atze J van der Ploeg, K Anton Feenstra, Wan J Fokkink + 3 more
Multiple sequence alignment (MSA) is one of the fundamental tasks in bioinformatics, essential to a wide variety of workflows, including fold prediction, phylogenetic analysis and mutation impact prediction. The exact solution with dynamic programming is not feasible for more than a handful of sequences. For protein…
71relevance
16
BbioRxiv15 Jan 2023
Sensitive inference of alignment-safe intervals from biodiverse protein sequence clusters using EMERALD
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…
70relevance
17
BbioRxiv2 Jun 2016
Statistical inference of protein structural alignments using information and compression
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…
68relevance
18
CchemRxiv8 Aug 2024
Human-in-the-loop active learning for goal-oriented molecule generation
Yasmine Nahal, Janosch Menke, Julien Martinelli, Markus Heinonen + 5 more
Machine learning (ML) systems have enabled the modelling of quantitative structure-property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug…
63relevance
19
CchemRxiv15 Jun 2022
Krein Support Vector Machine Classification of Antimicrobial Peptides
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
58relevance
20
AarXiv2020
Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins
David Medina-Ortiz, Sebastián Contreras, Juan Amado-Hinojosa, Jorge Torres-Almonacid + 3 more
'Jorge Torres-Almonacid' 'Juan A. Asenjo' 'Marcelo A. Navarrete' 'Álvaro Olivera‐Nappa'] Predicting the effect of mutations in proteins is one of the most critical challenges in protein engineering; by knowing the effect a substitution of one (or several) residues in the protein's sequence has on its overall…
53relevance
21
CchemRxiv22 Aug 2025
Fingerprint-Based Machine Learning for SARS-CoV-2 and MERS-CoV Mpro Inhibition: Highlighting the Potential of Bayesian Neural Networks
Authors not listed
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV) are two important targets in current drug discovery, mainly due to the COVID-19 pandemic and the MERS-CoV outbreaks in recent years. An important target of both SARS-CoV-2 and MERS-CoV is the main…
53relevance
22
AarXiv17 Jun 2015
The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes
Z. Shreif, Deborah A. Striegel, Vipul Periwal
A nucleotide sequence 35 base pairs long can take 1,180,591,620,717,411,303,424 possible values. An example of systems biology datasets, protein binding microarrays, contain activity data from about 40000 such sequences. The discrepancy between the number of possible configurations and the available activities is…
52relevance

Results for Predictive Alignment

Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks

Taming the chaos gently: a Predictive Alignment learning rule in recurrent neural networks

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

Aligning the Evaluation of Probabilistic Predictions with Downstream Value

ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring

Sequence alignment by passing messages

TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs

Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration

Relating model performance to embedding distributions in molecular machine learning

Conditioning Predictive Models: Risks and Strategies

Clade-wise alignment integration improves co-evolutionary signals for protein-protein interaction prediction

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

Motif-Aware PRALINE: Improving the alignment of motif regions MA-PRALINE: Motif-aware alignment

A deep-learning-based score to evaluate multiple sequence alignments

Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit

Sensitive inference of alignment-safe intervals from biodiverse protein sequence clusters using EMERALD

Statistical inference of protein structural alignments using information and compression

Human-in-the-loop active learning for goal-oriented molecule generation

Krein Support Vector Machine Classification of Antimicrobial Peptides

Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins

Fingerprint-Based Machine Learning for SARS-CoV-2 and MERS-CoV Mpro Inhibition: Highlighting the Potential of Bayesian Neural Networks

The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes

Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks

Taming the chaos gently: a Predictive Alignment learning rule in recurrent neural networks

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

Aligning the Evaluation of Probabilistic Predictions with Downstream Value

ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring

Sequence alignment by passing messages

TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs

Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration

Relating model performance to embedding distributions in molecular machine learning

Conditioning Predictive Models: Risks and Strategies

Clade-wise alignment integration improves co-evolutionary signals for protein-protein interaction prediction

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

Motif-Aware PRALINE: Improving the alignment of motif regions MA-PRALINE: Motif-aware alignment

A deep-learning-based score to evaluate multiple sequence alignments

Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit

Sensitive inference of alignment-safe intervals from biodiverse protein sequence clusters using EMERALD

Statistical inference of protein structural alignments using information and compression

Human-in-the-loop active learning for goal-oriented molecule generation

Krein Support Vector Machine Classification of Antimicrobial Peptides

Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins

Fingerprint-Based Machine Learning for SARS-CoV-2 and MERS-CoV Mpro Inhibition: Highlighting the Potential of Bayesian Neural Networks

The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes