3 citations · 5 across the 11 of their papers we have counts for
12 papers
Generating eukaryotic reference genome assemblies: Earth BioGenome Project quality standards and recommendations
Kerstin Howe, Dolores R. Agius, Tyler Alioto +45
Reference genome assemblies are foundational resources across the biological sciences as they begin to expose the fundamental building blocks of each species and the molecular tool…
Doubly robust target inference for generalized linear regression with completely missing covariates
Huali Zhao, Ke Deng
Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses. We study target-population inference for generalized linear regr…
uMOF: A Universal Database, Benchmark, and Machine Learning Interatomic Potentials for Metal-Organic Frameworks
Théo Jaffrelot Inizan, Prathami Divakar Kamath, Alin Marin Elena +1
Foundation machine learning interatomic potentials (MLIPs) deliver near-ab-initio accuracy at a fraction of the computational cost, yet their promise for Metal-organic Frameworks (…
KANResDiff: Learning Local Residual Diffusion via Kolmogorov-Arnold Network for Ambiguous Medical Image Segmentation
Fanding Li, Chenglin Wang, Xiangyu Li +9
Ambiguous medical image segmentation aims to provide a series of diverse but plausible segmentation hypotheses. However, existing methods introduce stochasticity in a fixed and pre…
Confidence intervals for two-stage adaptive designs with subpopulation selection
Enyu Li, Nigel Stallard, Ekkehard Glimm +2
We consider clinical trials in which an experimental treatment is compared with a control in pre-specified patient subpopulations. In such settings, adaptive enrichment designs all…
Ambiguity-aware Truncated Flow Matching for Ambiguous Medical Image Segmentation
Fanding Li, Xiangyu Li, Xianghe Su +6
A simultaneous enhancement of accuracy and diversity of predictions remains a challenge in ambiguous medical image segmentation (AMIS) due to the inherent trade-offs. While truncat…
Combined machine learning for stock selection strategy based on dynamic weighting methods
Lin Cai, Zhiyang He, Caiya Zhang
This paper proposes a novel stock selection strategy framework based on combined machine learning algorithms. Two types of weighting methods for three representative machine learni…
Bayesian Nonparametric Clustering with Feature Selection for Spatially Resolved Transcriptomics Data
Bencong Zhu, Guanyu Hu, Yang Xie +3
The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profilin…
Decoding the Molecular Universe -- Workshop Report
Thomas O. Metz, Joshua N. Adkins, Peter B. Armentrout +30
On August 9-10, 2023, a workshop was convened at the Pacific Northwest National Laboratory (PNNL) in Richland, WA that brought together a group of internationally recognized expert…
Integration of Clinical, Biological, and Computational Perspectives to Support Cerebral Autoregulatory Informed Clinical Decision Making Decomposing Cerebral Autoregulation using Mechanistic Timescales to Support Clinical Decision-Making
J. K. Briggs, J. N. Stroh, T. D. Bennett +2
Adequate brain perfusion is required for proper brain function and life. Maintaining optimal brain perfusion to avoid secondary brain injury is one of the main concerns of neurocri…
Doubly robust estimation of average treatment effect revisited
Keli Guo, Chuyun Ye, Jun Fan +1
The research described herewith is to re-visit the classical doubly robust estimation of average treatment effect by conducting a systematic study on the comparisons, in the sense…
Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases
Dmitrii Shadrin, Mariia Pukalchik, Anastasia Uryasheva +4
Plant diseases can lead to dramatic losses in yield and quality of food, becoming a problem of high priority for farmers. Apple scab, moniliasis, and powdery mildew are the most si…