Biology subjects

Rifaioglu, A. S.

Publications and source records attributed to Rifaioglu, A. S..

8 recordsLinked to original sources

bioRxiv2026

CROssBARv2: A Unified Computational Framework for Heterogeneous Biomedical Data Representation and LLM-Driven Exploration

Biomedical discovery is hindered by fragmented, modality-specific repositories and uneven metadata, limiting integrative analysis, accessibility, and reproducibility. To address these challenges, we present CROssBARv2, a provenance-rich biomedical data-and-knowledge integration platform that unifies heterogeneous sources into a maintainable, scalable system. By consolidating diverse data types into an extensive knowledge graph enriched with standardised ontologies, rich metadata, and deep learning-based vector embeddings, CROssBARv2 alleviates the need for researchers to navigate multiple siloed databases and can facilitate downstream tasks, including predictive modelling and mechanistic reasoning, enabling applications such as drug repurposing and protein function prediction. The platform offers interactive graph exploration and embedding-based semantic search with CROssBAR-LLM, an intuitive natural language question-answering system that grounds large language model (LLM) outputs in the underlying knowledge graph to mitigate hallucinations. We assess CROssBARv2 through (i) multiple use-case analyses to test biological coherence and relational validity; (ii) knowledge-augmented biomedical question-answering benchmarks comparing CROssBAR-LLM against generalist LLMs; and (iii) a deep learning-based predictive modelling experiment for protein function prediction leveraging the heterogeneous structure of CROssBARv2. Collectively, CROssBARv2 provides a scalable, AI-ready, and user-friendly foundation that facilitates hypothesis generation, knowledge discovery, and translational research.

bioinformatics↗

bioRxiv2025

SpaCEy: Discovery of Functional Spatial Tissue Patterns by Association with Clinical Features Using Explainable Graph Neural Networks

Tissues are complex ecosystems tightly organized in space. This organization influences their function, and its alteration underpins multiple diseases. Spatial omics allows us to profile its molecular basis, but how to leverage these data to link spatial organization and molecular patterns to clinical practice remains a challenge. We present SpaCEy (Spatial Clinical Explainability), an explainable graph neural network that uncovers organizational tissue patterns predictive of clinical outcomes. SpaCEy learns directly from molecular marker expression by modelling tissues as spatial graphs of cells and their interactions, without requiring predefined cell types or anatomical regions. Its embeddings capture intercellular relationships and molecular dependencies that enable accurate prediction of variables such as overall survival and disease progression. SpaCEy integrates a specialized explainer module that reveals recurring spatial patterns of cell organisation and coordinated marker expression that are most relevant to predictions of the models. Applied to a spatially resolved proteomic lung cancer cohort, SpaCEy discovers distinct spatial arrangements of cells together with coordinated expression of protein markers associated with disease progression. Across multiple breast cancer proteomic datasets, it consistently stratifies patients according to overall survival, both across and within established clinical subtypes. SpaCEy also highlights spatial patterns of a small set of key protein markers underlying this patient stratification.

bioinformatics↗

bioRxiv2025

Molecular Contrastive Learning with Graph Attention Network (MoCL-GAT) for Enhanced Molecular Representation

Learning the representation of molecules is crucial for drug discovery but is often hindered by the scarcity of labeled experimental data, which limits the performance of supervised machine learning models. While self-supervised learning (SSL) offers a solution by leveraging vast unlabeled chemical databases, many existing methods focus on learning from either local structural information or global molecular properties, but not both simultaneously. We introduce MoCL-GAT, a novel contrastive and transfer learning-based SSL framework that addresses this gap by simultaneously learning from two complementary objectives. It combines a local contrastive task on molecular subgraphs to capture fine-grained chemical environments with a global predictive task to learn holistic molecular descriptors. This dual-objective approach, powered by a Graph Attention Network, is designed to create more robust, versatile, and transferable molecular representations. Pre-trained on 1.9 million compounds, MoCL-GAT was fine-tuned on diverse benchmarks. It achieved state-of-the-art performance on molecular property prediction tasks, with an AUROC of 0.928 on BBBP and 0.749 on SIDER, and top-ranking RMSEs of 0.570 for ESOL and 1.818 for FreeSolv. Critically, fine-tuned models consistently and significantly outperformed models trained from scratch, confirming the value of pre-training. These results validate that MoCL-GATs dual-objective approach learns highly effective and transferable representations, enabling more accurate and data-efficient predictions for key cheminformatics challenges.

bioinformatics↗

bioRxiv2025

A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow defined cellular trajectories across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cellular trajectory regionalises tumours into distinct tissue niches and manifests in a molecularly conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to map cancer cell states jointly with clones in situ, we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

cancer biology↗

bioRxiv2022

Compartments in medulloblastoma with extensive nodularity are connected through differentiation along the granular precursor lineage

Medulloblastoma with extensive nodularity (MBEN) are cerebellar tumors with two histologically distinct compartments and varying disease course. In some children MBEN progresses, while others show spontaneous differentiation into more benign tumors. However, the mechanisms that control the tug-of-war between proliferation and differentiation are not well understood. Here, we dissected this process with a multi-modal single cell transcriptome analysis. We found that the internodular MBEN compartment comprised proliferating early cerebellar granular neuronal precursors (CGNP)-like tumor cells as well as stromal, vascular, and immune cells. In contrast, the nodular compartment consisted of postmitotic, neuronally differentiated MBEN cells. Both compartments were connected through an intermediate cell stage of actively migrating CGNPs. Furthermore, astrocyte-like tumor cells were identified that had branched off the main CGNP developmental trajectory. Cells with an astroglial phenotype were found in close proximity to migrating, late CGNP-like and postmitotic neuronally differentiated cells. Our study reveals how the spatial tissue organization is linked to the developmental trajectory of proliferating tumor cells through a migrating precursor stage into differentiated tumor cells with a more benign phenotype. We anticipate that our framework for integrating single nucleus RNA-sequencing and spatial transcriptomics will help to uncover intercompartmental interactions also in other cancers with varying histology.

cancer biology↗

bioRxiv2021

Predicting the Specificity-Determining Positions of Paralogous Complexes

Due to its clinical relevance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict partner-selecting, i.e., specificity-determining positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune-related diseases. For this, we used three sequence-based predictors - SDPred, Multi-RELIEF, and Sequence Harmony - and a structure-based approach by utilizing HADDOCK and extensive molecular dynamics simulations. As a result, we show that (i) sequence-based methods overpredict the number of specificity-determining positions for Axl complexes and that (ii) combining sequence-based approaches with HADDOCK provides the most coherent set of predictions. Our work lays out a critical study on the comparative performance specificity-determining position predictors. It also presents a combined sequence-structure-based approach, which can guide the development of therapeutic molecules capable of combatting Axl misregulation in different types of diseases.

bioinformatics↗

bioRxiv2020

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

Systemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.

bioinformatics↗

bioRxiv2018

DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations

The identification of physical interactions between drug candidate chemical substances and target biomolecules is an important step in the process of drug discovery, where the standard procedure is the systematic screening of chemical compounds against pre-selected target proteins. However, experimental screening procedures are expensive and time consuming, therefore, it is not possible to carry out comprehensive tests. Within the last decade, computational approaches have been developed with the objective of aiding experimental studies by predicting novel drug-target interactions (DTI), via the construction and application of statistical models. In this study, we propose a large-scale DTI interaction prediction system, DEEPScreen, for early stage drug discovery, using convolutional deep neural networks. One of the main advantages of DEEPScreen is employing readily available simple 2-D images of compounds at the input level instead of engineered complex feature vectors that displayed limited performance in DTI prediction tasks previously. DEEPScreen learns complex features inherently from the 2-D molecular representations, thus producing highly accurate predictions. DEEPScreen system was trained for 704 target proteins (using ChEMBL curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against shallow classifiers such as the random forest, logistic regression and support vector machines, to indicate the effectiveness of the proposed deep learning approach. Additionally, we compared DEEPScreen with other deep learning based state-of-the-art DTI predictors on widely used benchmark datasets and showed that DEEPScreen produces better or comparable results to the top performers. The method proposed here can be employed to computationally scan a large portion of the recorded drug candidate compound and protein spaces to aid the experimentalists working in the field of drug discovery and repurposing by providing a preselection of interesting novel DTIs.

bioinformatics↗