305 citations · 466 across the 10 of their papers we have counts for
10 papers
Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts
Aakash Tripathi, Ian E. Nielsen, Muhammad Umer +2
Transcription Factor Binding Site (TFBS) prediction is crucial for understanding gene regulation and various biological processes. This study introduces a novel Mixture of Experts…
EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis
Aakash Tripathi, Asim Waqas, Matthew B. Schabath +2
Accurate cancer survival prediction requires integration of diverse data modalities that reflect the complex interplay between imaging, clinical parameters, and textual reports. Ho…
Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK
Carlos Garcia-Fernandez, Luis Felipe, Monique Shotande +6
Large language models (LLMs) show promise in healthcare, but hallucinations remain a major barrier to clinical use. We present CHECK, a continuous-learning framework that integrate…
TheBlueScrubs-v1, a comprehensive curated medical dataset derived from the internet
Luis Felipe, Carlos Garcia, Issam El Naqa +6
The need for robust and diverse data sets to train clinical large language models (cLLMs) is critical given that currently available public repositories often prove too limited in…
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes
Asim Waqas, Aakash Tripathi, Paul Stewart +3
Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneous nature of this data, especial…
Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology
Asim Waqas, Aakash Tripathi, Sabeen Ahmed +6
Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling t…
HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models
Aakash Tripathi, Asim Waqas, Matthew B. Schabath +2
HONeYBEE (Harmonized ONcologY Biomedical Embedding Encoder) is an open-source framework that integrates multimodal biomedical data for oncology applications. It processes clinical…
Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology Datasets
Aakash Tripathi, Asim Waqas, Kavya Venkatesan +2
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopatho…
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review
Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran +2
Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical rec…
Transformers in Time-series Analysis: A Tutorial
Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi +3
Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision. Recently Transformers have been employed in various aspects o…