Arzucan Özgür

Background: Identifying traits of narcissistic personality disorder (NPD) is clinically challenging, yet early detection can significantly improve outcomes. Online forums have become a major source of self-expression, offering new opportunities to understand mental health. However, analyzing this complex language requires new tools. Objective: This study aims to determine whether a machine learning model could be trained to reliably detect language patterns associated with NPD traits in Reddit posts. Specifically, we sought to identify both authors who exhibit these traits and posts discussing individuals with these traits. Methods: We analyzed 75,985 posts from 4 Reddit communities (r/Narcissism, r/DeepThoughts, r/Showerthoughts, and r/ImposterSyndrome). A subset of 966 posts was annotated by 2 psychiatrists to create a reliable dataset. Using a range of machine learning techniques, from traditional text analysis to modern transformer-based embedding models, we trained the system to distinguish posts containing NPD-trait markers from general reflective posts. This secondary analysis of deidentified public Reddit data was exempted by the Boğaziçi University FMINAREK (Fen Bilimleri ve Mühendislik Alanları İnsan Araştırmaları Etik Kurulu) Institutional Review Board (exemption/application number 2025-13). Results: Our models demonstrated high accuracy. The modern embedding-based models were particularly effective, achieving a mean F1-score of 0.90, indicating a strong balance between recall and precision by correctly identifying posts with NPD-trait markers while minimizing false positives. The models remained effective even when common keywords such as “narcissism” were removed, with a mean relative F1-score decrease of 3.6% for the frequency-based baseline classifier and less than 2% (1.9% and 0.8%) for the embedding models. Conclusions: This study demonstrates that automated analysis of online posts is a promising approach for understanding and identifying NPD traits. Although this technology has potential for future clinical applications, it is currently a research tool and should be used only with strict ethical oversight, not for public-facing diagnosis.

Motivation: Accurate prediction of protein function is essential for elucidating molecular mechanisms and advancing biological and therapeutic discovery. Yet experimental annotation lags far behind the rapid growth of protein sequence data. Computational approaches address this gap by associating proteins with Gene Ontology (GO) terms, which encode functional knowledge through hierarchical relations and textual definitions. However, existing models often emphasize one modality over the other, limiting their ability to generalize, particularly to unseen or newly introduced GO terms that frequently arise as the ontology evolves, and making the previously trained models outdated. Results: We present STAR-GO, a Transformer-based framework that jointly models the semantic and structural characteristics of GO terms to enhance zero-shot protein function prediction. STAR-GO integrates textual definitions with ontology graph structure to learn unified GO representations, which are processed in hierarchical order to propagate information from general to specific terms. These representations are then aligned with protein sequence embeddings to capture sequence–function relationships. STAR-GO achieves state-of-the-art performance and superior zero-shot generalization, demonstrating the utility of integrating semantics and structure for robust and adaptable protein function prediction. Availability: Code and pre-trained models are available at https://github.com/boun-tabi-lifelu/stargo, https://doi.org/10.5281/zenodo.18643082

Background: When genes are translated into proteins, mutations in the gene sequence can lead to changes in protein structure and function as well as in the interactions between proteins. These changes can disrupt cell function and contribute to the development of tumors. In this study, we introduce a novel approach based on graph neural networks that jointly considers genetic mutations and protein interactions for cancer prediction. We use DNA mutations in whole exome sequencing data and construct a heterogeneous graph in which patients and proteins are represented as nodes and protein-protein interactions as edges. Furthermore, patient nodes are connected to protein nodes based on mutations in the patient’s DNA. Each patient node is represented by a feature vector derived from the mutations in specific genes. The feature values are calculated using a weighting scheme inspired by information retrieval, where whole genomes are treated as documents and mutations as words within these documents. The weighting of each gene, determined by its mutations, reflects its contribution to disease development. The patient nodes are updated by both mutations and protein interactions within our noval heterogeneous graph structure. Since the effects of each mutation on disease development are different, we processed the input graph with attention-based graph neural networks. Results: We compiled a dataset from the UKBiobank consisting of patients with a cancer diagnosis as the case group and those without a cancer diagnosis as the control group. We evaluated our approach for the four most common cancer types, which are breast, prostate, lung and colon cancer, and showed that the proposed framework effectively discriminates between case and control groups. Conclusions: The results indicate that our proposed graph structure and node updating strategy improve cancer classification performance. Additionally, we extended our system with an explainer that identifies a list of causal genes which are effective in the model’s cancer diagnosis predictions. Notably, some of these genes have already been studied in cancer research, demonstrating the system’s ability to recognize causal genes for the selected cancer types and make predictions based on them.

BACKGROUND Identifying traits of narcissistic personality disorder (NPD) is clinically challenging, yet early detection can significantly improve outcomes. Online forums have become a major source of self-expression, offering new opportunities to understand mental health. However, analyzing this complex language requires new tools. OBJECTIVE This study aims to determine whether a machine learning model could be trained to reliably detect language patterns associated with NPD traits in Reddit posts. Specifically, we sought to identify both authors who exhibit these traits and posts discussing individuals with these traits. METHODS We analyzed 75,985 posts from 4 Reddit communities (r/Narcissism, r/DeepThoughts, r/Showerthoughts, and r/ImposterSyndrome). A subset of 966 posts was annotated by 2 psychiatrists to create a reliable dataset. Using a range of machine learning techniques, from traditional text analysis to modern transformer-based embedding models, we trained the system to distinguish posts containing NPD-trait markers from general reflective posts. This secondary analysis of deidentified public Reddit data was exempted by the Boğaziçi University FMINAREK (Fen Bilimleri ve Mühendislik Alanları İnsan Araştırmaları Etik Kurulu) Institutional Review Board (exemption/application number 2025-13). RESULTS Our models demonstrated high accuracy. The modern embedding-based models were particularly effective, achieving a mean F1-score of 0.90, indicating a strong balance between recall and precision by correctly identifying posts with NPD-trait markers while minimizing false positives. The models remained effective even when common keywords such as “narcissism” were removed, with a mean relative F1-score decrease of 3.6% for the frequency-based baseline classifier and less than 2% (1.9% and 0.8%) for the embedding models. CONCLUSIONS This study demonstrates that automated analysis of online posts is a promising approach for understanding and identifying NPD traits. Although this technology has potential for future clinical applications, it is currently a research tool and should be used only with strict ethical oversight, not for public-facing diagnosis.

Protein-protein interactions (PPIs) play a crucial role in various biological processes, and understanding these interactions is essential for advancing biomedical research. Automated extraction and analysis of PPI information from the rapidly growing scientific literature remains an important challenge. We present a novel ontology-based approach to analyze protein-protein interactions using Large Language Models (LLMs). We applied different learning strategies, namely in-context learning and parameter-efficient instruction fine-tuning for the Llama-2 chat models, to identify keywords in the text that indicate an interaction between a pair of proteins. Our results show that parameter-efficient fine-tuning leads to a performance gain even when the domain is new. The smaller fine-tuned models outperformed the zero-shot performance of much larger models. The keywords identified by the Llama-2 models were mapped to the ontology terms in the Interaction Network Ontology (INO). Our study suggests that a pipeline of an LLM and an ontology is an effective strategy for explaining relations between biomedical entities. This work demonstrates the potential of leveraging ontologies and advanced language models to advance automated PPI analysis from the scientific literature.

Hasan Kerem Şeker, Gökçe Uludoğan, Pelin Önal, Arzucan Özgür. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.

Tokenization is a crucial step in processing protein sequences for machine learning models, as proteins are complex sequences of amino acids that require meaningful segmentation to capture their functional and structural properties. However, existing subword tokenization methods, developed primarily for human language, may be inadequate for protein sequences, which have unique patterns and constraints. This study evaluates three prominent tokenization approaches, Byte-Pair Encoding (BPE), WordPiece, and SentencePiece, across varying vocabulary sizes (400-6400), analyzing their effectiveness in protein sequence representation, domain boundary preservation, and adherence to established linguistic laws. Our comprehensive analysis reveals distinct behavioral patterns among these tokenizers, with vocabulary size significantly influencing their performance. BPE demonstrates better contextual specialization and marginally better domain boundary preservation at smaller vocabularies, while SentencePiece achieves better encoding efficiency, leading to lower fertility scores. WordPiece offers a balanced compromise between these characteristics. However, all tokenizers show limitations in maintaining protein domain integrity, particularly as vocabulary size increases. Analysis of linguistic law adherence shows partial compliance with Zipf's and Brevity laws but notable deviations from Menzerath's law, suggesting that protein sequences may follow distinct organizational principles from natural languages. These findings highlight the limitations of applying traditional NLP tokenization methods to protein sequences and emphasize the need for developing specialized tokenization strategies that better account for the unique characteristics of proteins.

Language models, especially transformer-based ones, have achieved colossal success in natural language processing. To be precise, studies like BERT for natural language understanding and works like GPT-3 for natural language generation are very important. If we consider DNA sequences as a text written with an alphabet of four letters representing the nucleotides, they are similar in structure to natural languages. This similarity has led to the development of discriminative language models such as DNABERT in the field of DNA-related bioinformatics. To our knowledge, however, the generative side of the coin is still largely unexplored. Therefore, we have focused on the development of an autoregressive generative language model such as GPT-3 for DNA sequences. Since working with whole DNA sequences is challenging without extensive computational resources, we decided to conduct our study on a smaller scale and focus on nucleotide sequences of human genes, i.e. unique parts of DNA with specific functions, rather than the whole DNA. This decision has not significantly changed the structure of the problem, as both DNA and genes can be considered as 1D sequences consisting of four different nucleotides without losing much information and without oversimplification. First of all, we systematically studied an almost entirely unexplored problem and observed that recurrent neural networks (RNNs) perform best, while simple techniques such as N-grams are also promising. Another beneficial point was learning how to work with generative models on languages we do not understand, unlike natural languages. The importance of using real-world tasks beyond classical metrics such as perplexity was noted. In addition, we examined whether the data-hungry nature of these models can be altered by selecting a language with minimal vocabulary size, four due to four different types of nucleotides. The reason for reviewing this was that choosing such a language might make the problem easier. However, in this study, we found that this did not change the amount of data required very much.

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence‐based models often segment molecular sequences into pieces called chemical words, analogous to the words that make up sentences in human languages, and then apply advanced natural language processing techniques for tasks such as de novo drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. To address this gap, we employ data‐driven SMILES tokenization techniques such as Byte Pair Encoding, WordPiece, and Unigram to identify chemical words and compare the resulting vocabularies. To understand the chemical significance of these words, we build a language‐inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf–idf weighting. The experiments on multiple protein–ligand affinity datasets show that despite differences in words, lengths, and validity among the vocabularies generated by different subword tokenization algorithms, the identified key chemical words exhibit similarity. Further, we conduct case studies on a number of target to analyze the impact of key chemical words on binding. We find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our approach elucidates chemical properties of the words identified by machine learning models and can be used in drug discovery studies to determine significant chemical moieties.

Motivation: Detecting protein–protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. As biomedical literature continues to grow rapidly, there is an increasing need for automated and accurate extraction of these interactions to facilitate scientific discovery. Pretrained language models, such as generative pretrained transformers and bidirectional encoder representations from transformers, have shown promising results in natural language processing tasks. Results: We evaluated the performance of PPI identification using multiple transformer-based models across three manually curated gold-standard corpora: Learning Language in Logic with 164 interactions in 77 sentences, Human Protein Reference Database with 163 interactions in 145 sentences, and Interaction Extraction Performance Assessment with 335 interactions in 486 sentences. Models based on bidirectional encoder representations achieved the best overall performance, with BioBERT achieving the highest recall of 91.95% and F1 score of 86.84% on the Learning Language in Logic dataset. Despite not being explicitly trained for biomedical texts, GPT-4 showed commendable performance, comparable to the bidirectional encoder models. Specifically, GPT-4 achieved the highest precision of 88.37%, a recall of 85.14%, and an F1 score of 86.49% on the same dataset. These results suggest that GPT-4 can effectively detect protein interactions from text, offering valuable applications in mining biomedical literature. Availability and implementation: The source code and datasets used in this study are available at https://github.com/hurlab/PPI-GPT-BERT.