Biomedical text tagging systems are plagued by the dearth of labeled training data. There have been recent attempts at using pre-trained encoders to deal with this issue. Pre-trained encoder provides representation of the input text which is then fed to task-specific layers for classification. The entire network is fin…
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48 results for “Biomedical texts”
We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nod…
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…
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 …
BioFinBERT analyzes sentiment of biotech press releases and financial text around inflection points.
problem Analyzing sentiment of biotech press releases and financial text around inflection points.
method Finetuning BioBERT on financial datasets to create BioFinBERT for sentiment analysis.
result BioFinBERT accurately analyzes sentiment of biotech press releases and financial text around inflection points.
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.
Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized co…
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…
Biomedical research papers use significantly different language and jargon when compared to typical English text, which reduces the utility of pre-trained NLP models in this domain. Meanwhile Medline, a database of biomedical abstracts, introduces nearly a million new documents per-year. Applications that could benefit…
Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only u…
Biomedical documents such as Electronic Health Records (EHRs) contain a large amount of information in an unstructured format. The data in EHRs is a hugely valuable resource documenting clinical narratives and decisions, but whilst the text can be easily understood by human doctors it is challenging to use in research …
Proposes BONMI for integrating noisy matrices from multi-source data.
problem Integrating noisy matrices from multi-source data with block-wise missingness.
method Exploits orthogonal Procrustes problem to align eigenspaces and completes missing blocks.
result Statistical rate for eigenspace of underlying matrix comparable to independently missing assumption.
Machine learning methods have recently achieved high-performance in biomedical text analysis. However, a major bottleneck in the widespread application of these methods is obtaining the required large amounts of annotated training data, which is resource intensive and time consuming. Recent progress in self-supervised …
Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical and clinical domains. The BioCreative/OHNLP organizers have made the first attemp…
Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been propos…
Drug-Drug Interactions (DDIs) Extraction refers to the efforts to generate hand-made or automatic tools to extract embedded information from text and literature in the biomedical domain. Because of restrictions in hand-made efforts and their lower speed, Machine-Learning, or Deep-Learning approaches have become more po…
The study examines how much data is needed for generative and vision-language models to make reliable predictions.
problem Ensuring reliable predictions with low data for models used in medical decision support.
method Analyzes uniform convergence bounds for VLM-induced classifiers under low-dimensional semantic representations.
result Finite-sample uniform convergence bounds for accuracy and calibration functionals of VLM-induced classifiers.
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.
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.
A Review on Modern Computational Optimal Transport Methods with Applications in Biomedical Researchstat.ML
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.
DISPR uses diffusion models to predict 3D cell shapes from 2D images.
problem Predicting 3D cell shapes from 2D microscopy images.
method Diffusion model trained to predict 3D shapes from 2D microscopy images as a prior.
result Adding DISPR predictions to minority cell classes improves classification accuracy.
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.
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…
Robust biclustering method tackles heavy-tailed data issues.
problem Discovering local correlation in heavy-tailed data.
method Convex biclustering with Huber loss and tuning-free parameter selection.
result Outperforms traditional biclustering methods in heavy-tailed noise.
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.
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.
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.
Biomedical data are widely accepted in developing prediction models for identifying a specific tumor, drug discovery and classification of human cancers. However, previous studies usually focused on different classifiers, and overlook the class imbalance problem in real-world biomedical datasets. There are a lack of st…
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
Originally designed to model text, topic modeling has become a powerful tool for uncovering latent structure in domains including medicine, finance, and vision. The goals for the model vary depending on the application: in some cases, the discovered topics may be used for prediction or some other downstream task. In ot…
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.
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…
Deep learning methods have shown extraordinary potential for analyzing very diverse biomedical data, but their dissemination beyond developers is hindered by important computational hurdles. We introduce ImJoy (https://imjoy.io/), a flexible and open-source browser-based platform designed to facilitate widespread reuse…
Linguistic calibration improves long-form text confidence.
problem LMs hallucinate, leading to suboptimal decisions.
method Defining linguistic calibration, training framework, reinforcement learning.
result Llama 2 7B is significantly more calibrated than baselines.
VICatMix: variational Bayesian clustering and variable selection for discrete biomedical datastat.ML
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.
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.
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data. Leaning on recent theoretical insigh…
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.
The digitalization of stored information in hospitals now allows for the exploitation of medical data in text format, as electronic health records (EHRs), initially gathered for other purposes than epidemiology. Manual search and analysis operations on such data become tedious. In recent years, the use of natural langu…
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.