22 papers · ranked by Valyu relevance
Aial Sobeh, Simone Shamay-Tsoory, Bruno Kluwe-Schiavon, Giorgio Manenti
Human behavior is shaped by a pervasive motive to align with others, manifesting across a wide range of tendencies-from motor synchrony and emotional contagion to convergence in beliefs and choices. Existing accounts explain how alignment arises through predictive coding and observation-execution mechanisms, but they…
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…
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…
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…
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…
Andrew Konya, Deger Turan, Aviv Ovadya, Lina Qui + 5 more
'Flynn Devine' 'Lisa Schirch' 'Isabella Roberts' 'Deliberative Alignment Forum'] For humanity to maintain and expand its agency into the future, the most powerful systems we create must be those which act to align the future with the will of humanity. The most powerful systems today are massive institutions like…
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…
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…
Yu Gui, Ying Jin, Zhimei Ren
Guarantees Authors: ['Yu Gui' 'Ying Jin' 'Zhimei Ren'] Before deploying outputs from foundation models in high-stakes tasks, it is imperative to ensure that they align with human values. For instance, in radiology report generation, reports generated by a vision-language model must align with human evaluations before…
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…
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).…
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…
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)…
Nimrod Serok, Ksenia Polonsky, Haim Ashkenazy, Itay Mayrose + 3 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 (SoP)…
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…
Daniel Wolf, Gasser Farrag, Tabea Flügge, Lan Huong Timm + 4 more
'Vincenzo Grassia' 'Letizia Perillo' 'Fabrizia d’Apuzzo' 'Juan Martin Palomo'] Background/Objectives: Machine learning (ML) models predicting the risk of refinement (i.e., a subsequent course of treatment being necessary) in clear aligner therapy (CAT) were developed and evaluated. Methods: An anonymized sample of 9942…
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…
Vanessa Ferdinand, Amy Yu, Sarah Marzen
Organisms can solve complex tasks despite having limited cognitive resources when those resources are used optimally. Doing so optimally makes an organism “resource-rational”. In this paper, we show for the first time that humans are resource-rational at prediction. In a novel sequence learning experiment, participants…
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…
Smet, Pieter
Predict-Then-Optimize (PTO) methods address optimization under uncertainty by first predicting unknown parameters and then solving the resulting deterministic optimization problem. The implicit, but rarely tested, assumption behind PTO is that improved predictive accuracy leads to better downstream decisions.…
Clara Fannjiang, Jennifer Listgarten
Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal implications spanning drug development and manufacturing, plastic degradation, and carbon sequestration. When designing objects to achieve novel property values with…
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…