Biology subjects

Yasrab, R.

Publications and source records attributed to Yasrab, R..

5 recordsLinked to original sources

bioRxiv2025

Decoding Breast Cancer Heterogeneity via Multi-Omics Integration and Language Model-Based Interpretation

We present a novel pipeline combining Multi-Omics Factor Analysis (MOFA) and fine-tuned Large Language Models (LLMs) to predict breast cancer subtypes using proteomics, DNA methylation, and RNA-Seq data. Breast cancer is a heterogeneous disease characterized by diverse molecular alterations across multiple biological layers, necessitating integrative approaches for accurate subtype classification. Our methodology leverages MOFA for dimensionality reduction to identify key latent factors driving het-erogeneity, followed by LLM fine-tuning on these multi-omics signatures to enhance prediction accuracy. MOFA analysis identified five key latent factors capturing distinct biological processes: immune response, cell cycle regulation, metabolic reprogramming, tumor microenvironment interactions, and DNA repair mechanisms. We extracted the top features per omics layer for each factor and performed Gene Set Enrichment Analysis (GSEA) to characterize their biological significance. Our LLM, trained on curated multi-omics signatures and clinical metadata encoded as structured text prompts, significantly outperformed conventional statistical models in subtype classification, achieving AUC=0.93 and accuracy=0.89, compared to Random Forest (AUC=0.87, accuracy=0.82) and SVM (AUC=0.85, accuracy=0.80). The superior performance of our approach is attributed to the LLMs ability to capture complex, non-linear relationships and hierarchical feature interactions across omics layers. This integrative pipeline provides both improved predictive performance and interpretable biological insights, offering potential for enhanced clinical decision-making in breast cancer management.

cancer biology↗

bioRxiv2021

CNN based Heuristic Function for A* Pathfinding Algorithm: Using Spatial Vector Data to Reconstruct Smooth and Natural Looking Plant Roots

In this work we propose an extension to recent methods for the reconstruction of root architectures in 2-dimensions. Recent methods for the automatic root analysis have proposed deep learned segmentation of root images followed by path finding such as Dijkstras algorithm to reconstruct root topology. These approaches assume that roots are separate, and that a shortest path within the image foreground represents a reliable reconstruction of the underlying root structure. This approach is prone to error where roots grow in close proximity, with path finding algorithms prone to taking "short cuts" and overlapping much of the root material. Here we extend these methods to also consider root angle, allowing a more informed shortest path search that disambiguates roots growing close together. We adapt a CNN architecture to also predict the angle of root material at each foreground position, and utilise this additional information within shortest path searchers to improve root reconstruction. Our results show an improved ability to separate clustered roots.

plant biology↗

bioRxiv2020

PhenomNet: Bridging Phenotype-Genotype Gap: A CNN-LSTM Based Automatic Plant Root Anatomization System.

This research will explore the phenotype-genotype gap by bringing two very diverse technologies together to predict plant characteristics. Currently, there are several studies and tools available for plant phenotype and genotype analysis. However, there is no existing single system that offers both capabilities in one package. Usually, Convolution Neural Networks used for plant phenotyping analysis and Recurrent Neural Networks used for genotype analysis. Both of these machine leanring methods require different input data for feature extraction, analysis and learning. Building a machine learning system for plant data that can make use of both graphic (for phenotype) and time-series (for genotype) is critical and challenging, especially when the system has to predict sensitive information regarding plant growth, accession and types. In this study, the proposed system will solve these problems by bringing two very different technologies, analysis methods and datasets. The proposed research aims to bridge the phenotype-genotype gap using CNN-LSTMs to process graphic and temporal data of plant roots. The proposed system "PhenomNet" offers segmentation of plant roots along with the classification of the given dataset into different accessions. The experiment results have shown that proposed CNN-LSTM architecture provides very high accuracy in comparison to manual or semi-automated approaches.

plant biology↗

bioRxiv2020

RootNet: A Convolutional Neural Networks for Complex Plant Root Phenotyping from High-Definition Datasets

Plant phenotyping using machine learning and computer vision approaches is a challenging task. Deep learning-based systems for plant phenotyping is more efficient for measuring different plant traits for diverse genetic discoveries compared to the traditional image-based phenotyping approaches. Plant biologists have recently demanded more reliable and accurate image-based phenotyping systems for assessing various features of plants and crops. The core of these image-based phenotyping systems is structural classification and features segmentation. Deep learning-based systems, however, have shown outstanding results in extracting very complicated features and structures of above-ground plants. Nevertheless, the below-ground part of the plant is usually more complicated to analyze due to its complex arrangement and distorted appearance. We proposed a deep convolutional neural networks (CNN) model named "RootNet" that detects and pixel-wise segments plant roots features. The feature of the proposed method is detection and segmentation of very thin (1-3 pixels wide roots). The proposed approach segment high definition images without significantly sacrificing pixel density, it leads to more accurate root type detection and segmentation results. It is hard to train CNNs with high definition images due to GPU memory limitations. The proposed patch-based CNN training setup makes use of the entire image (with maximum pixel desisity) to recognize and segment give root system efficiently. We have used wheat (Triticum aestivum L.) seedlings dataset, which consists of wheat roots grown in visible pouches. The proposed system segments are given root systems and save it to the Root System Markup Language (RSML) for future analysis. RootNet trained on the dataset mentioned above along with popular semantic segmentation architectures, and it achieved a benchmark accuracy.

plant biology↗

bioRxiv2019

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures

We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and first and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a final time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools.

bioinformatics↗