Biology subjects

Prabhakar, V.

Publications and source records attributed to Prabhakar, V..

3 recordsLinked to original sources

bioRxiv2023

Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E images

Spatial transcriptomics allows precise RNA abundance measurement at high spatial resolution, linking cellular morphology with gene expression. We present a novel deep learning algorithm predicting local gene expression from histopathology images. Our approach employs a graph isomorphism neural network capturing cell-to-cell interactions in the tumor microenvironment and a Vision Transformer (CTransPath) for obtaining the tumor morphological features. Using a dataset of 30,612 spatially resolved gene expression profiles matched with histopathology images from 23 breast cancer patients, we identify 250 genes, including established breast cancer biomarkers, at a 100 {micro}m resolution. Additionally, we co-train our algorithm on spatial spot-level transcriptomics from 10x Visium breast cancer data along with another variant of our algorithm on TCGA-BRCA bulk RNA Seq. data, yielding mutual benefits and enhancing predictive accuracy on both these datasets. This work enables image-based screening for molecular biomarkers with spatial variation, promising breakthroughs in cancer research and diagnostics.

pathology↗

bioRxiv2023

VLIB: Unveiling insights through Visual and Linguistic Integration of Biorxiv data relevant to cancer via Multimodal Large Language Model

The field of cancer research has greatly benefited from the wealth of new knowledge provided by research articles and preprints on platforms like Biorxiv. This study investigates the role of scientific figures and their accompanying captions in enhancing our comprehension of cancer. Leveraging the capabilities of Multimodal Large Language Models (MLLMs), we conduct a comprehensive analysis of both visual and linguistic data in biomedical literature. Our work introduces VLIB, a substantial scientific figure-caption dataset generated from cancer biology papers on Biorxiv. After thorough preprocessing, which includes figure-caption pair extraction, sub-figure identification, and text normalization, VLIB comprises over 500,000 figures from more than 70,000 papers, each accompanied by relevant captions. We fine-tune baseline MLLMs using our VLIB dataset for downstream vision-language tasks, such as image captioning and visual question answering (VQA), to assess their performance. Our experimental results underscore the vital role played by scientific figures, including molecular structures, histological images, and data visualizations, in conjunction with their captions, in facilitating knowledge translation through MLLMs. Specifically, we achieved a ROUGE score of 0.66 for VQA and 0.68 for image captioning, as well as a BLEU score of 0.72 for VQA and 0.70 for image captioning. Furthermore, our investigation highlights the potential of MLLMs to bridge the gap between artificial intelligence and domain experts in the field of cancer biology.

cancer biology↗

bioRxiv2023

An Ensemble Learning Approach to perform Link Prediction on Large Scale Biomedical Knowledge Graphs for Drug Repurposing and Discovery

Generating knowledge graph embeddings (KGEs) to represent entities (nodes) and relations (edges) in large scale knowledge graph datasets has been a challenging problem in representation learning. This is primarily because the embeddings / vector representations that are required to encode the full scope of data in a large heterogeneous graph needs to have a high dimensionality. The orientation of a large number of vectors requires a lot of space which is achieved by projecting the embeddings to higher dimensions. This is not a scalable solution especially when we expect the knowledge graph to grow in size in order to incorporate more data. Any efforts to constrain the embeddings to lower number of dimensions could be problematic as insufficient space to spatially orient the large number of embeddings / vector representations within limited number of dimensions could lead to poor inferencing on downstream tasks such as link prediction which leverage these embeddings to predict the likelihood of existence of a link between two or more entities in a knowledge graph. This is especially the case with large biomedical knowledge graphs which relate several diverse entities such as genes, diseases, signaling pathways, biological functions etc. that are clinically relevant for the application of KGs to drug discovery. The size of the biomedical knowledge graphs are therefore much larger compared to typical benchmark knowledge graph datasets. This poses a huge challenge in generating embeddings / vector representations of good quality to represent the latent semantic structure of the graph. Attempts to circumvent this challenge by increasing the dimensionality of the embeddings often render hardware limitations as generating high dimensional embeddings is computationally expensive and often times infeasible. To practically deal with representing the latent structure of such large scale knowledge graphs (KGs), our work proposes an ensemble learning model in which the full knowledge graph is sampled into several smaller subgraphs and KGE models generate embeddings for each individual subgraph. The results of link prediction from the KGE models trained on each subgraph are then aggregated to generate a consolidated set of link predictions across the full knowledge graph. The experimental results demonstrated significant improvement in rank-based evaluation metrics on task specific link predictions as well as general link predictions on four open-sourced biomedical knowledge graph datasets.

bioinformatics↗