Neural networks enhance relation extraction from biomedical literature.
problem Automated extraction of relations between biomedical concepts.
method Use of multichannel architectures in deep neural networks with biomedical ontologies.
result State-of-the-art results in relation extraction tasks.
Improved NER in biomedical literature using weakly-supervised augmentation.
problem Challenging NER in scientific literature, especially for biomedical entities.
method Weakly-supervised data augmentation with a reference set.
result Iterative model retraining improves NER performance significantly.
Monitoring the biomedical literature for cases of Adverse Drug Reactions (ADRs) is a critically important and time consuming task in pharmacovigilance. The development of computer assisted approaches to aid this process in different forms has been the subject of many recent works. One particular area that has shown pro…
Improves biomedical entity linking with latent type modeling.
problem Lack of fine-grained type information for entity disambiguation.
method Jointly models entity disambiguation and latent type learning without direct supervision.
result Significant performance improvements over state-of-the-art techniques.
Study improves LLMs for PPI analysis by addressing uncertainty.
problem Uncertainty in LLM predictions for PPIs.
method Fine-tuned LLaMA-3 and BioMedGPT models, LoRA ensembles, Bayesian LoRA for UQ.
result Competitive PPI identification performance across diverse disease contexts.
Unified Bayesian model for multi-modal, small sample size biomedical data classification.
problem Classifying high-dimensional, multi-modal biomedical data with small sample sizes.
method Combines multi-modal data views into a latent space, prunes irrelevant features, and uses dual kernels for small sample size scenarios.
result Outperforms state-of-the-art models and identifies features aligned with existing markers.
Method cleans noisy training labels for biomedical data.
problem Accurately labeling biomedical data is challenging.
method Reliability-based training data cleaning with inductive conformal prediction.
result Significant enhancements in classification performance across multiple tasks.
Proposes a new Lasso method with performance constraints.
problem No control over prediction accuracy for certain individuals.
method Adds quadratic performance constraints to Lasso-based objective functions.
result Defines a constrained sparse regression model through nonlinear optimization.
In multi-label text classification, each textual document can be assigned with one or more labels. Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems. As an important task with broad applications in b…
ImJoy simplifies deep learning for biomedical research.
problem Computational barriers limit deep learning adoption in biomedical research.
method Open-source browser-based platform for deep learning.
result Facilitates widespread reuse of deep learning solutions.
Model predicts future term connections in biomedical research.
problem Capturing temporal dynamics and unobserved connections in biomedical term relationships.
method Variational inference model for positive-unlabeled learning on dynamic graphs.
result Model effectively predicts term relationships in real-world datasets.
Locally sparse neural networks improve interpretability for biomedical tabular data.
problem Overfitting and lack of interpretability in neural networks for tabular biomedical data.
method Locally sparse neural network with a gating network to select relevant features.
result The method outperforms state-of-the-art models in synthetic and real-world biomedical datasets.
BIOMRC dataset improves MRC performance, especially for non-experts.
problem Improving machine reading comprehension in biomedical texts.
method Developed a new dataset and tested two neural models, along with a BERT-based model.
result BERT-based model outperforms all other methods and reaches expert-level accuracy.
Enhancement attacks can falsely improve machine learning model performance in biomedical research.
problem The trustworthiness of machine learning in biomedical research is threatened by enhancement attacks.
method Developed two techniques to enhance prediction performance with minimal changes to features.
result Falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities.
Generates biomedical abstracts from titles, years, and keywords.
problem Difficulties in understanding biomedical research papers due to specialized language.
method Conditional transformer-based language model with metadata conditioning.
result Generated abstracts are more relevant and non-trivial than GPT-2.
Review of modern computational optimal transport methods for biomedical applications.
problem Efficient computation of optimal transport for big data.
method Regularization-based and projection-based computational methods.
result Advancements in computational optimal transport methods for biomedical research.
Method integrates logical rules into neural multi-hop reasoning for drug repurposing.
problem Capturing long-range dependencies in biomedical data.
method Combines logical rules with neural multi-hop reasoning using reinforcement learning.
result Our method outperforms baseline methods in drug repurposing tasks.
Paper proposes a new method for estimating treatment effects using interpretable deep learning models.
problem Estimating treatment effects from observational data with interpretability.
method Proposes a novel objective function using energy distance balancing score and neural additive models for improved interpretability.
result Demonstrates superior performance over state-of-the-art methods in semi-synthetic experiments.
Paper proposes a new method for imputing missing biomedical data.
problem Missing values in biomedical data.
method Monte Carlo Dropout within Autoencoders.
result The method improves imputation error and predictive similarity.
This study evaluates data pre-processing techniques for class imbalance in biomedical data.
problem Class imbalance in biomedical datasets affects model performance.
method Resampling and feature selection techniques evaluated using SVM, C4.5, LDA, and KNN classifiers.
result Feature Selection outperforms other methods in most cases, especially with SVM.
New method uses artificial data to improve biomedical text tagging.
problem Lack of labeled data for biomedical tasks.
method Fine-tuning a pre-trained encoder with additional artificial labeled instances.
result Achieved state-of-the-art performance in PICO annotation.
The support vector machine (SVM) is a widely used machine learning tool for classification based on statistical learning theory. Given a set of training data, the SVM finds a hyperplane that separates two different classes of data points by the largest distance. While the standard form of SVM uses L2-norm regularizatio…
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping. However, such approaches typically do not leverage available domain knowledge while extracting the phenotypes; hence, some of the su…
A multi-task learning model for slot tagging in biomedical domains.
problem Limited labeled data, memory constraints, and domain-specific slot types.
method Multi-task learning using deep bidirectional transformers.
result Outperforms previous state-of-the-art systems in efficiency and effectiveness.
Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domai…
New model improves DDI extraction with 78.5% macro F1-score.
problem Extracting Drug-Drug Interactions from text.
method Attention-Wrapped Hierarchical BLSTMs.
result Model outperforms state-of-the-art with macro F1-score of 0.785.
HOoD detects near-out-of-distribution groups in correlated biomedical assays.
problem Detecting near-out-of-distribution cases in biased or incomplete data.
method Projects correlated measurements through a trained model and uses permutation-based hypothesis tests.
result HOoD reliably identifies OoD groups, outperforming other detectors.
Deep learning has shown its great promise in various biomedical image segmentation tasks. Existing models are typically based on U-Net and rely on an encoder-decoder architecture with stacked local operators to aggregate long-range information gradually. However, only using the local operators limits the efficiency and…
New DL model handles missing data in biomedical datasets.
problem Handling missing data in modern biomedical datasets.
method Proposed a new DL architecture, dlglm, for generalized linear models.
result Outperforms existing methods in MNAR missingness scenarios.
Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction
problem Nonlinear models vs. linear models in biomedical prediction
method Measurement reliability
result Measurement noise blurs the population-optimal predictor
spex-LVM infers interpretable latent factors from biomedical data.
problem Inability to learn sparse and interpretable hidden states.
method Factorial latent variable model with sparse priors and domain-relevant annotations.
result Robustly identifies relevant structure in RNA-seq datasets.
Proposes a novel method to identify complex effects in multi-view datasets.
problem Challenges in analyzing multi-view biomedical datasets with complex interactions.
method Generalized kernel machine approach considering marginal and joint effects of features from different views.
result Effective identification of higher-order composite effects in multi-view datasets.
Paper proposes nearly unsupervised hashcode learning for relation extraction.
problem Relation extraction from text, especially in biomedical domains.
method Optimized hashcode representations learned from data points without class labels, followed by supervised classification.
result Significant accuracy improvements over state-of-the-art methods.
Two case studies reveal hidden biases and confounders in machine learning models of biomedical data.
problem Hidden biases and confounders in machine learning models of biomedical data.
method Two case studies examining biases and confounders in machine learning models of biomedical data.
result Prediction models performed well but hidden biases and confounders were revealed.
Extracts roles of authors from biomedical papers.
problem Lack of machine-readable author roles in biomedical papers.
method Statistical analysis of roles, Open Information Extraction, Naïve Bayes approach.
result Extracts roles with precision of 0.68, recall of 0.48, and F1 of 0.57.
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performan…
BioNAS optimizes deep learning models for biomedical research, revealing new knowledge.
problem Building interpretable deep learning models for biomedical research.
method Neural architecture search with knowledge dissimilarity functions for joint optimization of predictive power and biological knowledge.
result BioNAS optimal models reveal novel knowledge in both simulated and real functional genomics data.
VICatMix clusters categorical biomedical data efficiently and selects relevant variables.
problem Efficient clustering of high-dimensional categorical biomedical data.
method Variational Bayesian finite mixture model with variational inference.
result Improves clustering accuracy and variable selection on noisy, high-dimensional data.
Self-supervised method predicts clean signal and noise distribution from noisy images.
problem Blind denoising and noise estimation in biomedical images with limited clean data.
method Two neural networks jointly predict clean signal and noise distribution from noisy observations.
result Significantly outperforms state-of-the-art algorithms on six biomedical image datasets.
Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. [...] Biomedical literature mining can aid DDI research by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, …
Machine learning speeds up FLIM analysis in biomedical research.
problem Complex, slow, and computationally expensive FLIM analysis.
method Machine learning techniques for faster and smarter FLIM data extraction and interpretation.
result Higher accuracy in classifying and segmenting FLIM images compared to conventional methods.
SiMLR reduces complex biomedical data into simpler, interpretable forms.
problem Handling high-dimensional biomedical data for better understanding and prediction.
method Similarity-driven multi-view linear reconstruction (SiMLR) with novel objective function and regularization.
result SiMLR outperforms other methods in various biomedical datasets.
We present an analysis of the problem of identifying biological context and associating it with biochemical events in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type and cell type that are associated …
MedCAT extracts valuable medical information from unstructured text.
problem Extracting structured information from unstructured biomedical documents.
method Unsupervised machine learning for disambiguation of entities.
result Improved entity detection and linking compared to existing tools.
New algorithm improves model generalization in structured biomedical domains.
problem Improving model generalization in structured biomedical domains.
method Proposes a new regret minimization (RGM) algorithm and its structured extension for better performance in diverse environments.
result Significantly outperforms previous state-of-the-art baselines on molecular property prediction, protein homology, and stability prediction.
New algorithm improves causal discovery in biomedical data.
problem Stability and accuracy issues in causal discovery algorithms.
method Exploits temporal structure and tiered background knowledge.
result Increases accuracy in finite samples for causal structure estimation.
ROOFS helps researchers select robust biomarker features from complex data.
problem Challenges in feature selection for biomarker discovery and clinical models.
method ROOFS is a Python package that benchmarks multiple feature selection methods on user data.
result ROOFS identifies a filter method as optimal for identifying predictors of lung cancer resistance.
M3E2 neural network estimates multiple treatment effects.
problem Estimating effects of multiple treatments simultaneously.
method Multi-task learning neural network model for multiple treatments, continuous and binary.
result M3E2 outperforms baselines in synthetic datasets.