Jonathan R. Walsh
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View article: Skimsat: a small-satellite platform exploiting very low-Earth orbit (VLEO): in-orbit demonstrator mission update
Skimsat: a small-satellite platform exploiting very low-Earth orbit (VLEO): in-orbit demonstrator mission update Open
View article: Accelerating randomized clinical trials in Alzheimer’s Disease using generative machine learning model forecasts of progression
Accelerating randomized clinical trials in Alzheimer’s Disease using generative machine learning model forecasts of progression Open
Background Pivotal Alzheimer’s Disease (AD) trials typically require thousands of participants, resulting in long enrollment timelines and substantial costs. We leverage deep learning predictive models to create prognostic scores (forecast…
View article: Assessment of AI‐generated digital twin (DT) methodology on reduction of treatment effect variance and potential clinical trial sample size saving using a Phase 2 trial dataset from patients with Alzheimer’s disease (AD)
Assessment of AI‐generated digital twin (DT) methodology on reduction of treatment effect variance and potential clinical trial sample size saving using a Phase 2 trial dataset from patients with Alzheimer’s disease (AD) Open
Background In Alzheimer’s Disease (AD) trials, clinical scales are used to assess treatment effect in patients. Minimizing statistical uncertainty of trial outcomes is an important consideration to increase statistical power. Machine learn…
View article: Increasing acceptance of <scp>AI</scp>‐generated digital twins through clinical trial applications
Increasing acceptance of <span>AI</span>‐generated digital twins through clinical trial applications Open
Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence…
View article: Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials
Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials Open
Randomized controlled trials (RCTs) with binary primary endpoints introduce novel challenges for inferring the causal effects of treatments. The most significant challenge is non-collapsibility, in which the conditional odds ratio estimand…
View article: A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials
A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials Open
A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect. A novel and effective strategy to obtain efficient and powerful tre…
View article: Forecasting progression of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) with digital twins
Forecasting progression of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) with digital twins Open
Background Machine learning models can leverage historical data to forecast disease progression. These predictions can be integrated in clinical trial design to reduce sample size or increase power, speeding up the evaluation of new drugs.…
View article: Do Mastectomy Skin Complications Delay Adjuvant Therapy after Autologous Breast Reconstruction?
Do Mastectomy Skin Complications Delay Adjuvant Therapy after Autologous Breast Reconstruction? Open
INTRODUCTION: Autologous breast reconstruction (ABR) is an important treatment modality to minimize postmastectomy deformity and restore body image in patients with breast cancer. However, it remains unclear what effect complications after…
View article: Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins Open
Alzheimer's Disease (AD) is a neurodegenerative disease that affects subjects in a broad range of severity and is assessed in clinical trials with multiple cognitive and functional instruments. As clinical trials in AD increasingly focus o…
View article: Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data
Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data Open
View article: Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks Open
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments. We use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to learn the relationshi…
View article: Generating Digital Twins with Multiple Sclerosis Using Probabilistic\n Neural Networks
Generating Digital Twins with Multiple Sclerosis Using Probabilistic\n Neural Networks Open
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a\ncomplex set of clinical assessments. We use an unsupervised machine learning\nmodel called a Conditional Restricted Boltzmann Machine (CRBM) to learn the\nrelation…
View article: Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
Machine learning for comprehensive forecasting of Alzheimer’s Disease progression Open
Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Di…
View article: Deep learning of representations for transcriptomics-based phenotype prediction
Deep learning of representations for transcriptomics-based phenotype prediction Open
The ability to predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. This task is complicated because expression data are high dimensional whereas each experiment is usually small (e.g., ∼ 20,00…
View article: Data: unsupervised gene expression
Data: unsupervised gene expression Open
These files contain gene expression data, which can be used to train unsupervised models. Each file is a separate gene set and normalization.A README with some additional details is also provided.
View article: Supervised results table
Supervised results table Open
This is a CSV-formatted table of results from the paper "Deep learning of representations for transcriptomics-based phenotype prediction" by Aaron M. Smith, et. al.The results listed in this file are all supervised evaluation results. Each…
View article: Erratum to: Non-global structure of the O(α[superscript 2][subscript s]) dijet
Erratum to: Non-global structure of the O(α[superscript 2][subscript s]) dijet Open
View article: Using deep learning for comprehensive, personalized forecasting of Alzheimer's Disease progression.
Using deep learning for comprehensive, personalized forecasting of Alzheimer's Disease progression. Open
Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-m…
View article: Boltzmann Encoded Adversarial Machines
Boltzmann Encoded Adversarial Machines Open
Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective doe…
View article: Who is this gene and what does it do? A toolkit for munging transcriptomics data in python
Who is this gene and what does it do? A toolkit for munging transcriptomics data in python Open
Transcriptional regulation is extremely complicated. Unfortunately, so is working with transcriptional data. Genes can be referred to using a multitude of different identifiers and are assigned to an ever increasing number of categories. G…
View article: Erratum to: Non-global structure of the O α s 2 $$ \mathcal{O}\left({\alpha}_s^2\right) $$ dijet soft function
Erratum to: Non-global structure of the O α s 2 $$ \mathcal{O}\left({\alpha}_s^2\right) $$ dijet soft function Open
View article: Erratum to: Non-global structure of the O(α[subscript s][superscript 2] dijet soft function
Erratum to: Non-global structure of the O(α[subscript s][superscript 2] dijet soft function Open
View article: Integrated and differential accuracy in resummed cross sections
Integrated and differential accuracy in resummed cross sections Open
Standard QCD resummation techniques provide precise predictions for the spectrum and the cumulant of a given observable. The integrated spectrum and the cumulant differ by higher-order terms which, however, can be numerically significant. …
View article: Non-Gaussian covariance of the matter power spectrum in the effective field theory of large scale structure
Non-Gaussian covariance of the matter power spectrum in the effective field theory of large scale structure Open
We compute the non-Gaussian contribution to the covariance of the matter power spectrum at one-loop order in standard perturbation theory (SPT), using the framework of the effective field theory (EFT) of large scale structure (LSS). The co…
View article: Volumetric nasal cavity analysis in children with unilateral and bilateral cleft lip and palate: Nasal Cavity Volume in Cleft Lip and Palate
Volumetric nasal cavity analysis in children with unilateral and bilateral cleft lip and palate: Nasal Cavity Volume in Cleft Lip and Palate Open
Children with cleft lip and palate (CLP) often suffer from nasal obstruction which may be related to effects on nasal volume. The objective of this study is to compare side:side volume ratios and nasal volume in patients with unilateral (U…
View article: N-jettiness subtractions for NNLO QCD calculations
N-jettiness subtractions for NNLO QCD calculations Open
We present a subtraction method utilizing the N -jettiness observable, T N , to perform QCD calculations for arbitrary processes at next-to-next-to-leading order (NNLO). Our method employs soft-collinear effective theory (SCET) to determin…
View article: N-jettiness Subtractions for NNLO QCD Calculations
N-jettiness Subtractions for NNLO QCD Calculations Open
We present a subtraction method utilizing the N-jettiness observable, Tau_N, to perform QCD calculations for arbitrary processes at next-to-next-to-leading order (NNLO). Our method employs soft-collinear effective theory (SCET) to determin…
View article: The first calculation of fractional jets
The first calculation of fractional jets Open