12 papers · ranked by Valyu relevance
Han Li, Zisen Shan, Hongyu Fu, Yiwei Du + 6 more
The emergence of large-scale biobanks has opened unprecedented opportunities for the development of data-driven approaches, especially deep learning-based methods, for genotype-to-phenotype (G2P) prediction. However, designing an end-to-end framework capable of directly leveraging extremely high-dimensional genotypic…
Kartik Jhawar, Tapasvi Bhatt, Rohan Sunil, Wang Lipo
Accurately annotating the functions of uncharacterised human proteins remains a major bottleneck in biology. We present MkAtt–SDN2GO, a neural architecture that extends SDN2GO by integrating adaptive multi-kernel convolution and attention mechanisms to predict Gene Ontology terms from protein sequences, domains, and…
Mustafa Coşkun, Filipa Blasco Lopes, Pınar Kubilay Tolunay, Mark R. Chance + 1 more
Novel technologies for the acquisition of protein expression data at the single cell level are emerging rapidly. Although there exists a substantial body of computational algorithms and tools for the analysis of single cell gene expression (scRNAseq) data, tools for even basic tasks such as clustering or cell type…
Fangfang Guo, Sharmodeep Bhattacharyya, Shirshendu Chatterjee, David Gent + 1 more
Identifying spatial origins of biological invasions, disease outbreaks, or environmental contaminants is critical for timely intervention. However, existing methods struggle to resolve overlapping signals from multiple sources or account for extreme zero/one inflation in bounded data. We developed HiBASIL (Hierarchical…
Anirban Chakraborty, Chloe Mattila, Debashis Ghosh, Brian Neelon + 1 more
High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which…
Souvik Seal, Anirban Chakraborty, Chloe Mattila, Mark Rubinstein + 4 more
High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of…
Yuhan Lu, Zhuoran Li, Hangze Mao, Qinsiyuan Lyu + 5 more
The most critical attribute of the brain is its ability to coordinate perception, thoughts, and action across different timescales. A prominent theory holds that the cortex is organized along a unidimensional hierarchy, where higher-order regions operate over longer intrinsic timescales than sensory areas, a view…
M. L. Kringelbach, G. Deco
Brain dynamics can be described in three different convenient mathematical languages, namely connectome harmonics, turbulence and complex harmonics (CHARM). Here we demonstrate that these theoretical frameworks can be rigorously unified, under the functional calculus, as one self-adjoint operator and its single…
Sir-Lord Wiafe, Najme Soleimani, Vince D. Calhoun
Many complex systems comprise interacting components whose relationships evolve over time, posing a fundamental challenge across physics, engineering, and biology: quantifying time-varying interactions between variables. Despite decades of methodological development, existing approaches remain fragmented, obscuring the…
Alper Karagöl, Taner Karagöl
Amino acid substitutions are often directionally asymmetric due to underlying biophysical constraints and diverse evolutionary pressures. We introduce T_ν_ (variant tension), a kernel regression-based metric that quantifies this directional asymmetry directly from aligned multiple sequence alignments (MSAs). T_ν_…
Marc Sturrock, Vahid Shahrezaei
Approximate Bayesian computation sequential Monte Carlo (ABC-SMC) propagates its particles with a perturbation kernel, and with the standard Normal kernel it degrades sharply as the parameter dimension grows, a failure usually attributed to dimension itself. We show instead that it is governed by the quality of the…
Alexandria McPherson, Sepp Sanchirico, Albert Xu, Eric Larson + 4 more
Magnetoencephalography (MEG) measures human neural activity non-invasively with spatio-temporal precision, and has been foundational in enabling impactful discoveries in cognitive neuroscience. New on-scalp MEG sensor technologies, such as OPM-MEG, offer the opportunity to capture more information about the neuronal…