Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
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IN SHORT: This paper addresses the critical limitation of existing TF binding prediction methods that treat transcription factors as independent entities, failing to capture their cooperative regulatory mechanisms through multi-label classification.
核心创新
- Methodology First application of Temporal Convolutional Networks (TCNs) to multi-label transcription factor binding prediction, enabling simultaneous prediction of multiple TF binding events from DNA sequences.
- Methodology Development of three multi-label datasets (D-5TF-3CL, D-7TF-4CL, H-M-E2F) from ENCODE ChIP-seq data, specifically designed to study TF cooperativity.
- Biology Demonstration that deep learning models can learn biologically meaningful TF correlations and cooperative patterns directly from DNA sequence data, revealing both known and novel TF interactions.
主要结论
- TCN-based models significantly outperform RNN baselines in multi-label TF prediction, achieving average F1-score improvements of +0.17 to +0.26 across datasets (p<0.05).
- The model captures biologically relevant TF correlations, with TCN achieving AP scores of 0.73±0.01 on the H-M-E2F dataset compared to 0.52±0.00 for RNN baselines.
- TCNs demonstrate robust performance even with limited data, maintaining AP >0.7 on 152 out of 165 binary classification datasets despite moderate correlation (Pearson r=0.61) between performance and dataset size.
摘要: Transcription factors (TFs) regulate gene expression through complex and cooperative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactions is not fully understood yet. Most current approaches for TF binding site prediction focus on individual TFs and binary classification tasks, without a full analysis of the possible interactions among various TFs. In this paper we investigate DNA TF binding site recognition as a multi-label classification problem, achieving reliable predictions for multiple TFs on DNA sequences retrieved in public repositories. Our deep learning models are based on Temporal Convolutional Networks (TCNs), which are able to predict multiple TF binding profiles, capturing correlations among TFs and their cooperative regulatory mechanisms. Our results suggest that multi-label learning leading to reliable predictive performances can reveal biologically meaningful motifs and co-binding patterns consistent with known TF interactions, while also suggesting novel relationships and cooperation among TFs.