Hakime Öztürk

Hakime Öztürk
German Cancer Research Center | DKFZ · Computational Genomics and System Genetics

PhD

About

16
Publications
9,348
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
703
Citations
Introduction
Education
September 2012 - June 2014
Bogazici University
Field of study
  • Computer Engineering

Publications

Publications (16)
Article
Full-text available
Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsource...
Article
Identification of high affinity drug‐target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules and sequence similarity is not always correlated with functional similarity. We propose ChemBoost, a c...
Preprint
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural language processing (NLP) methodologies in the processing of spoken languages accelerated the application of NLP to elucidate hidden knowledge in textual representations of th...
Article
Full-text available
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural language processing (NLP) methodologies in the processing of spoken languages accelerated the application of NLP to elucidate hidden knowledge in textual representations of th...
Preprint
Full-text available
Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity. Results: WideDTA uses four text-based information sources, namely the p...
Preprint
Full-text available
Identification of high affinity drug-target interactions (DTI) is a major research question in drug discovery. In this study, we propose a novel methodology to predict drug-target binding affinity using only ligand SMILES information. We represent proteins using the word-embeddings of the SMILES representations of their strong binding ligands. Each...
Article
Full-text available
The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a co...
Article
Full-text available
Motivation: The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand-based approach can be...
Article
Full-text available
Trastuzumab is a monoclonal antibody frequently used to prevent the progression of HER2+ breast cancers, which constitute approximately 20% of invasive breast cancers. microRNAs (miRNAs) are small, non-coding RNA molecules that are known to be involved in gene regulation. With their emerging roles in cancer, they are recently promoted as potential...
Article
Full-text available
Motivation: The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many...
Thesis
Full-text available
During the last decades, the use of semantic text similarity has been adopted as a major component in many Natural Language Processing tasks, including text retrieval, summarization, and document categorization. Integration of semantic information acts as a powerful tool for a better understanding and structuring of text. Among the many domains tha...
Article
Full-text available
Background Molecular structures can be represented as strings of special characters using SMILES. Since each molecule is represented as a string, the similarity between compounds can be computed using SMILES-based string similarity functions. Most previous studies on drug-target interaction prediction use 2D-based compound similarity kernels such a...
Article
Full-text available
β-lactamase mediated antibiotic resistance is an important health issue and the discovery of new β-lactam type antibiotics or β-lactamase inhibitors is an area of intense research. Today, there are about a thousand β-lactamases due to the evolutionary pressure exerted by these ligands. While β-lactamases hydrolyse the β-lactam ring of antibiotics,...

Network

Cited By