Biology subjects

Aldahdooh, J.

Publications and source records attributed to Aldahdooh, J..

10 recordsLinked to original sources

bioRxiv2025

SOORENA: Self-lOOp containing or autoREgulatory Nodes in biological network Analysis

Autoregulatory mechanisms, in which proteins regulate their own activity or expression, are fundamental to biological networks but are challenging to identify systematically from literature. To address this gap, we present SOORENA (https://soorena.it.helsinki.fi/soorena/), a two-stage transformer model that predicts and classifies protein autoregulation in PubMed abstracts. SOORENA was trained on 1,332 experimentally validated abstracts and achieved 96.0 percent accuracy and 97.8 percent precision in stage one, with stage two achieving 95.5 percent accuracy and 96.2 percent macro-F1 across seven mechanistic classes. Applied to 3.34 million abstracts, SOORENA identified 85,145 publications containing autoregulatory mechanisms, yielding 97,657 protein-specific records. Integration with curated databases generated 100,065 comprehensive entries accessible via an interactive Shiny application. By systematically cataloging self-regulatory interactions, which often act as bottlenecks in dynamic network modeling, SOORENA provides a resource that supports mechanistic interpretation, model reduction, and predictive systems-level analyses. These results demonstrate that domain-specific language models can scale the discovery and curation of biologically essential self-regulatory mechanisms, bridging literature mining and systems biology.

bioinformatics↗

bioRxiv2023

Mining drug-target interactions from biomedical literature using chemical and gene descriptions based ensemble transformer model

Drug-target interactions (DTIs) play a pivotal role in drug discovery, as it aims to identify potential drug targets and elucidate their mechanism of action. In recent years, the application of natural language processing (NLP), particularly when combined with pre-trained language models, has gained considerable momentum in the biomedical domain, with the potential to mine vast amounts of texts to facilitate the efficient extraction of DTIs from the literature. In this article, we approach the task of DTIs as an entity-relationship extraction problem, utilizing different pretrained transformer language models, such as BERT, to extract DTIs. Our results indicate that an ensemble approach, by combining gene descriptions from the Entrez Gene database with chemical descriptions from the Comparative Toxicogenomics Database (CTD), is critical for achieving optimal performance. The proposed model achieves an F1 score of 80.6 on the hidden DrugProt test set, which is the top-ranked performance among all the submitted models in the official evaluation. Furthermore, we conduct a comparative analysis to evaluate the effectiveness of various gene textual descriptions sourced from Entrez Gene and UniProt databases to gain insights into their impact on the performance. Our findings highlight the potential of NLP-based text mining using gene and chemical descriptions to improve drug-target extraction tasks. Datasets utilized in this study are accessible at https://dtis.drugtargetcommons.org/.

bioinformatics↗

bioRxiv2022

A gene essentiality signature for studying the mechanism of action of drugs

Cancer drugs often kill cells independent of their putative targets, suggesting the limitation of existing knowledge on the mechanisms of action. In this study, we explored whether the integration of loss-of-function genetic and drug sensitivity screening data can define a gene essentiality signature to better understand the drug target interactions. We showed that our gene essentiality signature can predict drug targets more accurately than chemical fingerprints and drug-perturbated gene expression signatures. We further showed how gene essentiality signature can help identify mechanisms of action of drugs de novo, including the EGFR inhibitor lapatinib, and drugs associated with DNA mismatch repair. Finally, we established gene essentiality signatures for noncancer drugs and used them to predict their anticancer targets. We have successfully validated the target predictions for multiple noncancer drugs, using cell-based drug target deconvolution by the proteome integral solubility alteration assay. Our study provides a novel signature of drugs that may facilitate the rational design of drug repurposing.

systems biology↗

bioRxiv2022

DrugRepo: A novel approach to repurpose a huge collection of compounds based on chemical and genomic features

The drug development process consumes 9-12 years and approximately one billion US dollars in terms of costs. Due to high finances and time costs required by the traditional drug discovery paradigm, repurposing the old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysis of different data types leading to the formulation of repurposing hypotheses. This study presents a novel scoring algorithm based on chemical and genomic data types to repurpose vast collection of compounds for 674 cancer types and other diseases. The data types used to design the scoring algorithm are chemical structures, drug-target interactions (DTI), pathways, and disease-gene associations. The repurpose scoring algorithm is strengthened by integrating the most comprehensive manually curated datasets for each data type. More than 100 of our repurposed compounds can be matched with ongoing studies at clinical trials (https://clinicaltrials.gov/). Our analysis is supported by a web tool available at: http://drugrepo.org/.

bioinformatics↗

bioRxiv2021

Using BERT to identify drug-target interactions from whole PubMed

BackgroundDrug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and they are collected in large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of studies providing this data (~0.1 million) likely constitutes only a fraction of all studies on PubMed that contain experimental DTI data. Finding such studies and extracting the experimental information is a challenging task, and there is a pressing need for machine learning for the extraction and curation of DTIs. To this end, we developed new text mining document classifiers based on the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format. ResultsOur novel method identified and extracted DTIs from 2.1 million studies not previously included in public DTI databases. Using 10-fold cross-validation, we obtained ~99% accuracy for identifying studies containing drug-target pairs. The accuracy for the prediction of assay format is ~90%, which leaves room for improvement in future studies. ConclusionThe BERT model in this study is robust and the proposed pipeline can be used to identify new and previously overlooked studies containing DTIs and automatically extract the DTI data points. The tabular output facilitates validation of the extracted data and assay format information. Overall, our method provides a significant advancement in machine-assisted DTI extraction and curation. We expect it to be a useful addition to drug mechanism discovery and repurposing.

bioinformatics↗

bioRxiv2021

SynergyFinder Plus: towards a better interpretation and annotation of drug combination screening datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. (1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. (2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. (3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. (4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. (5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.

bioinformatics↗

bioRxiv2021

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; 2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; 3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample; and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

pharmacology and toxicology↗

bioRxiv2020

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

bioinformatics↗

bioRxiv2020

RNA Atlas of Human Bacterial Pathogens Uncovers Stress Dynamics Linked to Infection

Despite being genetically diverse, bacterial pathogens can adapt to similar stressful environments in human host, but how this diversity allows them to achive this is yet not fully understood. Knowledge gained through comparative genomics is insufficient as it lacks the level of gene expression reflecting gene usage. To fill this gap, we investigated the transcriptome of 32 diverse bacterial pathogens under 11 host related stress conditions. We revealed that diverse bacterial pathogens have common responses to similar stresses to a certain extent but mostly employ their unique repertoire with intersections between different stress responses. We also identified universal stress responders which shed light on the nature of antimicrobial targets. In addition, we found that known and unknown putative novel ncRNAs comprised a significant proportion of the responses. All the data is collected in PATHOgenex atlas, providing ample opportunities to discover novel players critical for virulence and maintenance of infection.

microbiology↗

bioRxiv2019

DrugComb - an integrative cancer drug combination data portal

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users own drug combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

pharmacology and toxicology↗