Martin Ester
YOU?
Author Swipe
View article: A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery
A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery Open
Bioactive peptides such as anticancer peptides (ACPs) offer a promising therapeutic alternative to small molecules due to their efficiency and selectivity against tumors and minimal toxicity towards healthy human cells. However, their rati…
View article: Why Pool When You Can Flow? Active Learning with GFlowNets
Why Pool When You Can Flow? Active Learning with GFlowNets Open
The scalability of pool-based active learning is limited by the computational cost of evaluating large unlabeled datasets, a challenge that is particularly acute in virtual screening for drug discovery. While active learning strategies suc…
View article: ABS0711 PRIORITY SETTING OF PHYSICAL ACTIVITY BARRIERS AND FACILITATORS AMONG INDIVIDUALS WITH RHEUMATOID ARTHRITIS: A NOMINAL GROUP TECHNIQUE STUDY
ABS0711 PRIORITY SETTING OF PHYSICAL ACTIVITY BARRIERS AND FACILITATORS AMONG INDIVIDUALS WITH RHEUMATOID ARTHRITIS: A NOMINAL GROUP TECHNIQUE STUDY Open
View article: scMUSCL: multi-source transfer learning for clustering scRNA-seq data
scMUSCL: multi-source transfer learning for clustering scRNA-seq data Open
Motivation Single-cell RNA sequencing (scRNA-seq) analysis relies heavily on effective clustering to facilitate numerous downstream applications. Although several machine learning methods have been developed to enhance single-cell clusteri…
View article: Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation Open
Generative Flow Networks (GFlowNets) have recently emerged as a suitable framework for generating diverse and high-quality molecular structures by learning from rewards treated as unnormalized distributions. Previous works in this framewor…
View article: Improving OOD Generalization of Pre-trained Encoders via Aligned Embedding-Space Ensembles
Improving OOD Generalization of Pre-trained Encoders via Aligned Embedding-Space Ensembles Open
The quality of self-supervised pre-trained embeddings on out-of-distribution (OOD) data is poor without fine-tuning. A straightforward and simple approach to improving the generalization of pre-trained representation to OOD data is the use…
View article: Causal Order Discovery based on Monotonic SCMs
Causal Order Discovery based on Monotonic SCMs Open
In this paper, we consider the problem of causal order discovery within the framework of monotonic Structural Causal Models (SCMs), which have gained attention for their potential to enable causal inference and causal discovery from observ…
View article: A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions Open
Clustering is a fundamental machine learning task, which aim at assigning instances into groups so that similar samples belong to the same cluster while dissimilar samples belong to different clusters. Shallow clustering methods usually as…
View article: Generative Flows on Synthetic Pathway for Drug Design
Generative Flows on Synthetic Pathway for Drug Design Open
Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synthesizability, limiting their practical use in real-world sc…
View article: UnPaSt: unsupervised patient stratification by differentially expressed biclusters in omics data
UnPaSt: unsupervised patient stratification by differentially expressed biclusters in omics data Open
Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical approaches. However, existing computational patient stratification methods have been benchmarke…
View article: Geometric-informed GFlowNets for Structure-Based Drug Design
Geometric-informed GFlowNets for Structure-Based Drug Design Open
The rise of cost involved with drug discovery and current speed of which they are discover, underscore the need for more efficient structure-based drug design (SBDD) methods. We employ Generative Flow Networks (GFlowNets), to effectively e…
View article: IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs
IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs Open
One limitation of existing Transformer-based models is that they cannot handle very long sequences as input since their self-attention operations exhibit quadratic time and space complexity. This problem becomes especially acute when Trans…
View article: scMUSCL: Multi-Source Transfer Learning for Clustering scRNA-seq Data
scMUSCL: Multi-Source Transfer Learning for Clustering scRNA-seq Data Open
Motivation scRNA-seq analysis relies heavily on single-cell clustering to perform many downstream functions. Several machine learning methods have been proposed to improve the clustering of single cells, yet most of these methods are fully…
View article: Adversarially Balanced Representation for Continuous Treatment Effect Estimation
Adversarially Balanced Representation for Continuous Treatment Effect Estimation Open
Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covari…
View article: Phenotype prediction from single-cell RNA-seq data using attention-based neural networks
Phenotype prediction from single-cell RNA-seq data using attention-based neural networks Open
Motivation A patient’s disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent…
View article: Label Efficient Plant Damage Estimation Using Pixel-Level Contrastive Learning
Label Efficient Plant Damage Estimation Using Pixel-Level Contrastive Learning Open
View article: Adversarially Balanced Representation for Continuous Treatment Effect Estimation
Adversarially Balanced Representation for Continuous Treatment Effect Estimation Open
Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covari…
View article: TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design Open
Searching the vast chemical space for drug-like molecules that bind with a protein pocket is a challenging task in drug discovery. Recently, structure-based generative models have been introduced which promise to be more efficient by learn…
View article: Clinical Phenotype Prediction From Single-cell RNA-seq Data using Attention-Based Neural Networks
Clinical Phenotype Prediction From Single-cell RNA-seq Data using Attention-Based Neural Networks Open
Motivation A patient’s disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown, or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recen…
View article: Multi-treatment Effect Estimation from Biomedical Data
Multi-treatment Effect Estimation from Biomedical Data Open
Several biomedical applications contain multiple treatments from which we want to estimate the causal effect on a given outcome. Most existing Causal Inference methods, however, focus on single treatments. In this work, we propose a neural…
View article: DESMOND 2.0: Identification of differentially expressed biclusters for unsupervised patient stratification
DESMOND 2.0: Identification of differentially expressed biclusters for unsupervised patient stratification Open
Unsupervised patient stratification based on omics data is traditionally approached by clustering methods which may be inefficient for datasets with multiple patterns overlapping in rows and columns. Biclustering methods that are searching…
View article: Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks
Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks Open
Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provi…
View article: Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction
Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction Open
Molecular Representation Learning is essential to solving many drug discovery and computational chemistry problems. It is a challenging problem due to the complex structure of molecules and the vast chemical space. Graph representations of…
View article: Subgroup Discovery in Unstructured Data
Subgroup Discovery in Unstructured Data Open
Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest. Subgroup discovery has numerous applications in knowl…
View article: A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions Open
Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as features in a vectorized form through various representation l…
View article: Causal Inference from Small High-dimensional Datasets
Causal Inference from Small High-dimensional Datasets Open
Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexi…
View article: Ligand Binding Prediction using Protein Structure Graphs and Residual Graph Attention Networks
Ligand Binding Prediction using Protein Structure Graphs and Residual Graph Attention Networks Open
Motivation Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulate…
View article: Aristotle: stratified causal discovery for omics data
Aristotle: stratified causal discovery for omics data Open
View article: Healthy memory aging - the benefits of regular daily activities increase with age
Healthy memory aging - the benefits of regular daily activities increase with age Open
As the number of older adults increases, so does the pressure on health care systems due to age-related disorders. Attempts to reduce cognitive decline have focused on individual interventions such as exercise or diet, with limited success…
View article: Multi-treatment Effect Estimation from Biomedical Data
Multi-treatment Effect Estimation from Biomedical Data Open
This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments. In contrast to existing methods, M3E2 can handle multiple treatment effects applied simultaneously to the same unit, con…