Search Results for author: Aakash Tripathi

Found 5 papers, 3 papers with code

HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models

1 code implementation13 May 2024 Aakash Tripathi, Asim Waqas, Yasin Yilmaz, Ghulam Rasool

To address this challenge, we introduce HoneyBee, a scalable modular framework for building multimodal oncology datasets that leverages foundational models to generate representative embeddings.

SeNMo: A Self-Normalizing Deep Learning Model for Enhanced Multi-Omics Data Analysis in Oncology

1 code implementation13 May 2024 Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Hamza Farooq, Matthew B. Schabath, Paul Stewart, Mia Naeini, Ghulam Rasool

SeNMo proved to be a mini-foundation model for multi-omics oncology data because it demonstrated robust performance, and adaptability not only across molecular data types but also on the classification task of predicting the primary cancer type of patients.

Predicting Patient Outcomes

Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology Datasets

1 code implementation30 Sep 2023 Aakash Tripathi, Asim Waqas, Kavya Venkatesan, Yasin Yilmaz, Ghulam Rasool

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data.

Data Integration

Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review

no code implementations11 Mar 2023 Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran, Paul Stewart, Ghulam Rasool

Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning.

Data Integration

Transformers in Time-series Analysis: A Tutorial

no code implementations28 Apr 2022 Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ghulam Rasool, Ravi P. Ramachandran

Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision.

Decoder Time Series +1

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