Biology subjects

Haghani, M.

Publications and source records attributed to Haghani, M..

5 recordsLinked to original sources

bioRxiv2025

Harnessing DNA Foundation Models for Cross-Species Transcription Factor Binding Site Prediction in Plant Genomes

Accurate prediction of transcription factor binding sites (TFBSs) is crucial for understanding gene regulation. While experimental methods such as ChIP-seq and DAP-seq are informative, they are labor-intensive and species-specific. Recent advancements in large-scale pretrained DNA foundation models have shown promise in overcoming these limitations. This study evaluates the performance of three such models--DNABERT-2, AgroNT, and HyenaDNA--in predicting TFBSs in plants. Using DAP-seq data from Arabidopsis thaliana and Sisymbrium irio, we benchmark their accuracy against specialized approaches, including a motif-based method and two deep learning models, DeepBind and BERT-TFBS. Our results demonstrate that foundation models, particularly HyenaDNA, offer superior predictive accuracy and computational efficiency, highlighting their potential for scalable, genome-wide TFBS prediction in plants.

bioinformatics↗

bioRxiv2025

HAVEN: Hierarchical Attention for Viral protEin-based host iNference

It is crucial to accurately predict hosts of viruses to understand and anticipate human infectious diseases that originate from animals. There is a lack of versatile models that handle out-of-distribution factors such as unseen hosts and viruses. We develop a machine learning model for predicting the host infected by a virus, given only the sequence of a protein encoded by the genome of that virus. Our approach, HAVEN, is the first to apply to multiple hosts and to generalize to unseen hosts and viruses. HAVEN is a transformer-based architecture coupled with hierarchical self-attention that can accept sequences of highly diverse lengths. We integrate HAVEN with a prototype-based few-shot learning classifier to predict rare classes. We demonstrate the accuracy, robustness, and generalizability of HAVEN through a comprehensive series of experiments. In particular, we show that HAVEN can achieve a median AUPRC of 0.67 while predicting common hosts. Moreover, HAVEN retains this AUPRC value even for rare hosts (median prevalence as low as 0.09%). Our model performs on par with state-of-the-art foundation models, which are 65 to 5, 000 times larger in size, and outperforms them in identifying hosts of SARS-CoV-2 variants of concern.

bioinformatics↗

bioRxiv2025

SynVerse: A Framework for Systematic Evaluation of Deep Learning Based Drug Synergy Prediction Models

Synergistic drug combinations are often used to treat cancer. Experimental exploration of all possibilities is expensive. Deep learning (DL) for predicting the synergy of drug pairs in specific cell lines might provide an alternative. However, current approaches often suffer from data leakage. They also lack systematic ablation studies. To address these gaps, we propose SynVerse, a comprehensive evaluation framework featuring four data-splitting strategies to assess DL model generalizability and three ablation studies: module-based, feature shuffling, and a novel network-based approach to disentangle factors influencing model performance. We evaluated sixteen models incorporating eight drug- and cell line-specific features, five preprocessing techniques, and two widely used encoders. Our analysis revealed several insights. None of the models outperformed a naive baseline using one-hot encoding as features. Biologically meaningful drug or cell line features and drug-drug interactions were not the drivers of predictive performance. All models demonstrated poor generalization to unseen drugs and cell lines. SynVerse emphasizes the need for substantial improvements before computational predictors can reliably support experimental and clinical settings.

pharmacology and toxicology↗

bioRxiv2024

NEFFy: A Versatile Tool for Computing the Number of Effective Sequences

SummaryA Multiple Sequence Alignment (MSA) contains fundamental evolutionary information that is useful in the prediction of structure and function of proteins and nucleic acids. The "Number of Effective Sequences" (NEFF) quantifies the diversity of sequences of an MSA. Several tools can compute the NEFF of an MSA, each offering various options. NEFFy is the first software package to integrate all these options and calculate NEFF across diverse MSA formats for proteins, RNAs, and DNAs. It surpasses existing tools in functionality without compromising computational efficiency and scalability. NEFFy also offers per-residue NEFF calculation and supports NEFF computation for MSAs of multimeric proteins, with the capability to be extended to nucleic acids (DNA and RNA). Availability and ImplementationNEFFy is released as open-source software under the GNU General Public License v3.0. The source code in C++ and a Python wrapper are available on GitHub at https://github.com/Maryam-Haghani/NEFFy. To ensure users can fully leverage these capabilities, comprehensive documentation and examples are provided at https://Maryam-Haghani.github.io/NEFFy

bioinformatics↗

bioRxiv2020

Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCov literature

During the current century, each major coronavirus outbreak has triggered a quick and immediate surge of academic publications on this topic. The spike in research publications following the 2019 Novel Coronavirus (Covid-19) outbreak, however, has been like no other. The global crisis caused by the Covid-19 pandemic has mobilised scientific efforts in an unprecedented way. In less than five months, more than 12,000 research items have been indexed while the number increasing every day. With the crisis affecting all aspects of life, research on Covid-19 seems to have become a focal point of interest across many academic disciplines. Here, scientometric aspects of the Covid-19 literature are analysed and contrasted with those of the two previous major Coronavirus diseases, i.e. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). The focus is on the co-occurrence of key-terms, bibliographic coupling and citation relations of journals and collaborations between countries. Certain recurring patterns across all three literatures were discovered. All three outbreaks have commonly generated three distinct and major cohort of studies: (i) studies linked to the public health response and epidemic control, (ii) studies associated with the chemical constitution of the virus and (iii) studies related to treatment, vaccine and clinical care. While studies affiliated with the category (i) seem to have been the first to emerge, they overall received least numbers of citations compared to those of the two other categories. Covid-19 studies seem to have been distributed across a broader variety of journals and subject areas. Clear links are observed between the geographical origins of each outbreak or the local geographical severity of each outbreak and the magnitude of research originated from regions. Covid-19 studies also display the involvement of authors from a broader variety of countries compared to SARS and MRS.Competing Interest StatementThe authors have declared no competing interest.View Full Text

scientific communication and education↗