A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A multi-task learning model for slot tagging in biomedical domains.
Improves biomedical entity linking with latent type modeling.
New algorithm improves model generalization in structured biomedical domains.
Generates biomedical abstracts from titles, years, and keywords.
spex-LVM infers interpretable latent factors from biomedical data.
Locally sparse neural networks improve interpretability for biomedical tabular data.
In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data. However, current methods do not jointly optimize over structured covariates and time series …
SiMLR reduces complex biomedical data into simpler, interpretable forms.
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…
Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea is that two words that have close meaning gather similar contexts. …
We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature. First, we train a neural NER (NNER) model over a small seed of fully-labeled examples. Second, we use a reference s…
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…
LLM-Lasso uses LLMs to improve feature selection in Lasso regression.
ALPODS AI diagnoses high-dimensional biomedical data with human-understandable explanations.
AGATHA predicts promising research directions from scientific data.
This work abstracts deep neural networks into concept graphs for better interpretability in medical tasks.
Smile-GANs clusters brain MRI scans to reveal disease subtypes and progression.
Model predicts future term connections in biomedical research.
BIOMRC dataset improves MRC performance, especially for non-experts.
Enhancement attacks can falsely improve machine learning model performance in biomedical research.
Review of modern computational optimal transport methods for biomedical applications.
Non-negative blind source separation (non-negative BSS), which is also referred to as non-negative matrix factorization (NMF), is a very active field in domains as different as astrophysics, audio processing or biomedical signal processing. In this context, the efficient retrieval of the sources requires the use of sig…
Method integrates logical rules into neural multi-hop reasoning for drug repurposing.
Paper proposes a new method for imputing missing biomedical data.
GOPSA optimizes EEG data for cross-site age prediction, improving performance on multiple metrics.
Generative model learns shape drift for quantifying domain uncertainty in hemodynamics.
Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probabili…
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…
SigTime learns interpretable signatures from time series data.
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…
Study improves LLMs for PPI analysis by addressing uncertainty.
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…
Method cleans noisy training labels for biomedical data.
HOoD detects near-out-of-distribution groups in correlated biomedical assays.
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…
The paper advocates for interpretable, accountable, reproducible machine learning in medicine.
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…
New DL model handles missing data in biomedical datasets.
Machine learning has been gaining traction in recent years to meet the demand for tools that can efficiently analyze and make sense of the ever-growing databases of biomedical data in health care systems around the world. However, effectively using machine learning methods requires considerable domain expertise, which …
Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction
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…
DISCoVeR learns disentangled representations by separating shared and condition-specific factors.
Proposes a novel method to identify complex effects in multi-view datasets.
Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations …
Two case studies reveal hidden biases and confounders in machine learning models of biomedical data.
Extracts roles of authors from biomedical papers.
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…