Healthcare

Idiographic Personality Gaussian Process (IPGP) offers a novel measurement framework that captures both shared and individual-specific psychological traits, improving prediction accuracy and revealing…

GEMCODE, a hybrid AI pipeline, automates co-crystal design for enhanced drug tabletability by combining deep generative models and evolutionary optimization, predicting numerous novel co-crystals.

Boost AI predictions by using human judgment on algorithmically indistinguishable inputs!

HEALNet: a novel multimodal fusion network achieving state-of-the-art performance on biomedical survival analysis by effectively integrating heterogeneous data while handling missing modalities.

Graph Diffusion Transformer (Graph DiT) masters multi-conditional molecular generation by cleverly integrating property representations into a graph-dependent noise model, achieving superior performan…

GFNSeqEditor, a novel biological sequence editing algorithm, efficiently enhances desired properties while minimizing edits using generative flow networks, surpassing existing methods.

ConStruct: Generating realistic graphs with guaranteed structural properties via constrained diffusion.

FUSE, a novel framework, efficiently predicts continuous fields & estimates discrete parameters in PDEs, significantly improving accuracy and robustness.

Boost pathology model accuracy with Concept Anchor-guided Task-specific Feature Enhancement (CATE)! This adaptable paradigm enhances feature extraction for specific tasks using task-relevant concepts,…

Fine-grained control is added to generative models for IoT sensing data augmentation, tailoring synthetic data to specific application needs by leveraging domain expertise and statistical metrics of s…

FAST, a novel dual-tier few-shot learning paradigm, significantly boosts whole slide image (WSI) classification accuracy by efficiently using limited annotations and all available WSIs.

KalmanNet, a novel BMI decoder, achieves state-of-the-art performance by integrating recurrent neural networks into Kalman filtering, balancing accuracy and explainability.

ET-Flow, a novel equivariant flow-matching model, generates highly accurate and physically realistic molecular conformers significantly faster than existing methods.

Faster, more efficient deep learning for diffusion MRI deconvolution is achieved using spatio-hemispherical networks, improving fiber tractography.

A new dynamic subgroup identification strategy, using covariate-adjusted response-adaptive randomization, efficiently identifies best-performing subgroups in clinical trials, improving resource alloca…

Du-IN: Revolutionizing speech decoding from intracranial neural signals with discrete units-guided mask modeling, achieving state-of-the-art performance on a challenging Chinese word-reading sEEG dat…

Double-Ended Synthesis Planning (DESP) significantly boosts computer-aided synthesis planning by using a bidirectional search, outperforming existing methods on multiple benchmarks, especially when sp…

DMNet: A novel self-comparison driven model significantly improves subject-independent seizure detection from intracranial EEG, outperforming existing methods.

Researchers developed a data-efficient method using conservative model-based optimization to design cell-type-specific promoters for gene therapy, significantly improving cell-type specificity.

DeltaDock: a novel two-stage framework revolutionizes molecular docking by improving accuracy and reliability, achieving a 300% increase in success rate compared to the prior state-of-the-art in blind…