Ali Cakmak, Ph.D.
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My primary research interests focus on mining, computational analysis, and management of data in different fields with a special focus on biological and health data. Hence, my research is mostly interdisciplinary spanning diverse fields including algorithm development, systems biology, medical informatics, and databases.

* Word cloud of my publications

Research Interests:

  • Bioinformatics -- Metabolomics, Biological Networks, Biochemical Pathways, Metabolism
  • Data Mining -- Graph and Text Mining, Ontology-Associated Data Mining, Web Data Mining
  • Systems Biology -- Managing, Querying, and Visualizing Genome-Scale Metabolic Models
  • Information Extraction -- Knowledge Discovery in Text Databases, Social Networks
  • Data Management -- Data Integration, Metabolic Databases, Labeling Schemes for Ontologies
  • Databases -- Query Optimization


Research Groups:

Ongoing Active Research:

·  Biomarker and microRNA-based Therapeutic Target Discovery for Alzheimer’s Disease: There is an urgent need for a better understanding of mechanisms underlying Alzheimer’s disease (AD). Early detection is a key factor in modifying disease course. To date, definitive diagnosis often requires post-mortem examination of brain tissue. While a number of biomarkers are available (e.g. PET, MRI, CSF markers), their utility is limited by costs or poor scalability. Circulating and EV-enriched miRs hold a promising potential as diagnostic and prognostic non-invasive biomarkers of AD and neurodegenerative conditions. Specific proteins which accumulate during AD, have been characterized, however their targeting presents significant challenges due to the need for their tight regulation. Lysosomal dysregulation is strongly associated with AD and our preliminary data suggests that changes in V-ATPase function, which affect lysosomal function and metabolism, may be an early and targetable pathogenic mechanisms in AD to restore lysosomal function and metabolic homeostasis. Other mechanisms which may underlie neuronal vulnerability and initiate adverse cellular processes several years before symptoms, is oxidative damage to DNA, proteins and lipids. In this project, our goal is to discover novel non-invasive biomarkers and therapeutic targets for Alzheimer’s disease using omics and data science approaches. An in-silico model of neurodegeneration will be built in this project using omics and data science utilizing clinical datasets.



·  Multi-Omics-supported Personalized Treatment Recommendation for Patients: Increasing availability of high throughput omics data at decreasing costs has led to rapid progress in understanding the mechanisms of diseases. Such developments offer many opportunities for personalized and precision medicine practices. This project focuses on the development of tools and methods for the exploitation of the computed metabolism changes for personalized treatment selection (e.g., individualized drug recommendations for a patient). To this end, we will intensively use the machine learning models that will be built on the metabolic analysis results. In more detail, this research involves the following parts:


o Integrating transcriptome, proteome, and metabolome for more informed metabolic profile prediction: Metabolic networks are controlled by enzymes of the underlying processes and the regulators (i.e., activator, inhibitors, etc.) of those enzymes. Enzymes being mostly proteins (and, rarely, rRNAs), transcriptome and proteome of an organism are indirectly related to the metabolome of an organism. Hence, investigating effective integration strategies among different “ome”s (i.e., proteome, metabolome, and transcriptome) is vital to deriving more accurate metabolic profile predictions. We will extend our own Metabolitics algorithm to utilize multiple types of omics datasets to compute the metabolic profiles of patients.





o Developing machine learning-based classification models for diseases: Through the metabolic changes calculated based on the metabolic analysis of omics data, machine learning models will be built to classify individuals as healthy or having a disease of interests. As datasets, databases from public omic data sources such as Metabolomics Workbench, TCGA, GEO, ProteomeXchange may be used.

o Designing and creating a classification model database: Once the machine learning models are created, they will be used repeatedly at many points. Since the training and optimization of these models require time and resources, the reuse of the created models is important for the efficient use of resources. In this context, a classification model database may be designed and created to enable the reuse of the models as well as the efficient management and storage of the classification models created for diseases.





o Developing an individualized drug recommendation system: In this step, a clinical decision support system will be developed to compute the most suitable treatment for a patient by blocking the target of each drug in the metabolic network of the patient and re-computing the metabolic profile of the patient under this constraint. The resulting profile will be fed into the classification model once again to check if the predicted class of the patient turns into “healthy”.

·  Multi-Dataset-based Omics Data Imputation: For a proper biological interpretation of omics datasets and powerful data analysis, preprocessing is essential to ensure high data quality. In various studies containing biological entity measurements, missing values in the data may affect the performance of the analysis results significantly. In recent years, the application of deep learning-based generative models for the accurate imputation of missing values has gained popularity. Unsupervised generative models like variational autoencoders (VAE) can impute missing values to perform more powerful data analysis. This project aims to develop effective models that can accurately predict missing biological entity (metabolites, proteins, transcripts, etc.) values in omics datasets.





·  Drug Repositioning: The main aim of this project is to develop a metabolism-based pipeline that will allow a computational investigation of whether the currently approved drugs for any disease are suitable for use in treating diseases other than their intended target. In summary, this project involves the following parts for each disease of interest.

o Simulating the effect of each drug on patient metabolism: Each targeted enzyme by a drug would be inactivated, and the effect of these inactivations will be computationally simulated to obtain new metabolic profiles for patients.

o Employing machine learning models to assess the new profiles: Subsequently, the classification model previously created and recorded for this disease in the classification model database will be applied to these new metabolic profiles calculated in the above step. If the proportion of patients who are classified as “healthy” with the new metabolic profiles is above a certain threshold, then the drug may be recommended for repositioning for this disease. The threshold value may be determined according to the average effectiveness rate of the approved drugs.

o Exploring Combination Therapies: For some diseases, combinations of drugs are used as the main therapy. We will also computationally explore combination therapy options by unifying the targets of combined drugs into a single set, and then employ a similar approach as outlined above.




·  Computational Discovery of New Drug Targets: The main purpose of this research is to develop a metabolism-based pipeline that will allow computational exploration of new possible drug targets for diseases. The above-outlined approach may be adapted here with small changes. More specifically, for each reaction which is not targeted by any drug in the database, we will compute new metabolic profiles for patients after inactivating that reaction. Subsequently, the machine learning model previously created and recorded for this disease in the classification model database is applied to these new metabolic profiles. If the proportion of patients who are classified as “healthy” with their new metabolic profiles is above a certain threshold value, the proteins catalyzing the reaction and the genes encoding these proteins may be recommended as possible new drug targets.

·  Readmission Risk-based Discharge and Planned Admission Decision Support System: Readmission of patients after a short period of time from their discharge costs billions of dollars to governments every year. It has been shown that effective follow-up and improved coordinated care of the patient may prevent re-hospitalization after discharge. In this research, we will develop an integrated readmission risk monitoring system that will continuously monitor patients, and act as an auxiliary decision support system to provide clinicians with the risk of readmission during the entire period of a patient’s stay, as well as after the discharge through the periodic remote measurements taken by the patient at home. The part of the project include (i) developing statistical machine learning models to predict the risk of readmission for most costly select disease groups (e.g., myocardial infarction, pneumonia, diabetes mellitus, Alzheimer’s disease, etc.), (ii) identifying unplanned readmission cases in a personalized and adaptive way, (iii) locating and eliminating data entry errors, (iv) developing a patient discharge decision support system based on readmission risk, and (v) developing a remote follow-up program integrated with readmission risk prediction models that will recommend health care providers a planned admission or doctor visit in a proactive manner for patients with high readmission risk. We will also develop a cost-aware optimization model to balance the risk of readmission with the financial cost of admission on the healthcare system. Furthermore, this research includes the development of mobile and web apps to enable patients to remotely enter their self-measured data as well as to present various reports to both patients and healthcare providers.

 

 

· Personalized Metabolic Analysis of Diseases: The metabolic wiring of cells is altered drastically in many diseases. Understanding the nature of such changes may pave the way for new therapeutic opportunities, and the development of personalized treatment strategies. In this research, we developed an algorithm, Metabolitics (Cakmak and Celik, 2020), which allows systems-level analysis of changes in the biochemical network of cells in disease states. We demonstrate the use of Metabolitics on three distinct diseases, namely, breast cancer, Crohn’s disease, and colorectal cancer. Our results show that the constructed machine learning models successfully diagnose patients by over 90% accuracy on average. Moreover, we filed an international patent (Cakmak and Celik, 2018) that describes the use of Metabolitics as part of a medical decision support system to help clinicians diagnose diseases in a low-cost and non-invasive manner. We also developed a web-based tool, MetaboliticsDB (Celik et al., 2020), that makes Metabolitics available to researchers from all around the world. It allows users to analyze their Metabolomics data and apply the pre-built machine learning models to identify potential associated diseases with advanced visualization support.





· Predicting Future Covid-19 Mutations : Covid-19 continues to spread over the world steadily as opposed to many earlier estimations that it would disappear in less than two years on its emergence. Even though Covid-19 vaccines have reduced the speed of the infection significantly, they could not fully stop it. On the contrary, the World Health Organization has recently published cautionary statements that infection counts are on the rise, and a huge wave is expected in winter 2022. Vaccines mostly target specific regions of the virus. The high mutation rate of Covid-19 is one essential tool that the virus exploits to escape from the available vaccines. Therefore, researchers have been working on designing next-generation vaccines against the new variants of the virus. Nevertheless, Covid-19 acquires new mutations faster than we can adapt our vaccines due to long clinical trial periods. Hence, there is a need for computational tools that can predict future Covid-19 mutations before they even emerge. In this project, we develop several deep-learning-based methods to estimate the possible future mutations in Covid-19 genome. We design and evaluate various ensemble and bagging architectures enriched with a large set of genomic, biochemical, and phylogenetic features. We evaluate our models on the GISAID data.



· Colorectal Cancer Risk Screening: This research explores the machine learning-based assessment of predisposition to colorectal cancer based on single nucleotide polymorphisms (SNP). Such a computational approach may be used as a risk indicator and an auxiliary diagnosis method that complements the traditional methods such as biopsy and CT scan. Moreover, it may be used to develop a low-cost screening test for the early detection of colorectal cancers to improve public health. We employ several supervised classification algorithms. We study SNPs in particular colorectal cancer-associated genomic loci that are located within DNA regions of 11 selected genes obtained from 115 individuals. We make the following observations: (i) random forest-based classifier using one-hot encoding and K-nearest neighbor (KNN)-based imputation performs the best among the studied classifiers with an F1 score of 89% and area under the curve (AUC) score of 0.96. (ii) One-hot encoding together with K-nearest neighbor-based data imputation increases the F1 scores by around 26% in comparison to the baseline approach which does not employ them. (iii) The proposed model outperforms a commonly employed state-of-the-art approach, ColonFlag, under all evaluated settings by up to 24% in terms of the AUC score. Based on the high accuracy of the constructed predictive models, the studied 11 genes may be considered a gene panel candidate for colon cancer risk screening (Cakmak et al. 2022).

 

Previous Research:


· Scalable Taxonomy Classification: Various fields of applied biology (e.g., agriculture) depend on the classification of living creatures. However, many popular and highly accurate machine learning methods (e.g., Support Vector Machines) are not scalable to taxonomy settings where there is a large number of labels/classes. In this research, we developed a multi-level hierarchical classifier framework to automatically assign taxonomy labels to DNA sequences (Sohsah et al., 2020). We utilize an alignment-free approach called spectrum kernel method for feature extraction. We demonstrate that the proposed framework provides higher accuracy (i.e., 95%) than regular classifiers, and is scalable to taxonomy classification settings. Furthermore, we show that the proposed framework is more robust to mutations and noise in sequence data than the non-hierarchical classifiers.

· Query Selectivity Estimation: Query optimizers of database management systems (DBMS) employ the expected size of a query’s result set while automatically generating the most efficient execution plan for the query. Therefore, accurately estimating the size of the query result set (i.e., selectivity) is critical to the performance of DBMSs. In this research, we first developed a new sequence pattern-based histogram structure and an algorithm (i.e., SPH) (Aytimur and Cakmak, 2018) that employs the patterns stored in this histogram to estimate the selectivity of fuzzy text queries. SPH dramatically outperforms the state-of-the-art approaches for queries with generic text patterns in terms of the estimation accuracy. Moreover, SPH requires two orders of magnitude less space both in memory and on disk. Besides, the selectivity estimation time of SPH is almost an order of magnitude less in comparison to the state of the art. Next, in another work (Aytimur and Cakmak, 2021), we developed novel positional sequence patterns. We demonstrated that employing positional sequence patterns instead of regular sequence patterns for fuzzy string selectivity estimation decreases the estimation error by around 20%. Finally, we filed an international patent (Cakmak, 2018) that describes the integration of the above-described techniques into a commercial database management system.

· Digital Libraries: In today's academia, publish or perish policy results in an enormous body of publications. Hence, given the limited time, researchers have to be selective while putting a paper into their reading list, as well as prioritizing the articles in that list. Ideally, many would be more interested in reading papers that are likely to have a high impact on their fields. However, it is almost impossible to decide whether a paper would really make a high impact ahead of time before reading it (and often even after reading it). In this research (Davletov et al., 2014), we developed an impact prediction framework for academic papers. Our model uses a time-series approach to predict the number of citations. In particular, our model makes use of citation behavior, i.e., the pattern of increase in the number of citations. In the training phase, the papers are clustered according to citation behaviors. Then, when a new paper is published, it is assigned to a cluster and the prediction is performed accordingly.

· Machine Learning on Big Data: Most of the popular Big Data analytics tools evolved to adapt their working environment to extract valuable information from a vast amount of unstructured data. The ability of data mining techniques to filter this helpful information from Big Data led to the term ‘Big Data Mining’. Shifting the scope of data from small-size, structured, and stable data to huge volume, unstructured, and quickly changing data brings many data management challenges. Different tools cope with these challenges in their own way due to their architectural limitations. There are numerous parameters to take into consideration when choosing the right data management framework based on the task at hand. In this paper, we present a comprehensive benchmark for two widely used Big Data analytics tools, namely Apache Spark and Hadoop MapReduce, on a common data mining task, i.e., classification. We employ several evaluation metrics to compare the performance of the benchmarked frameworks, such as execution time, accuracy, and scalability. These metrics are specialized to measure the performance for classification task. To the best of our knowledge, there is no previous study in the literature that employs all these metrics while taking into consideration task-specific concerns. We show that Spark is 5 times faster than MapReduce on training the model. Nevertheless, the performance of Spark degrades when the input workload gets larger. Scaling the environment by additional clusters significantly improves the performance of Spark. However, similar enhancement is not observed in Hadoop. Machine learning utility of MapReduce tend to have better accuracy scores than that of Spark, like around 2%-3%, even in small-size data sets. (Tekdogan and Cakmak, 2021).



 

· Mining for Unknown Pathways: Many essential biological pathways still remain unknown or incomplete for newly sequenced organisms. Moreover, experimental validation of enormous numbers of possible pathway candidates in a wet-lab environment is time and effort-extensive. In this research, we developed comparative genomics tools and algorithms (Cakmak and Ozsoyoglu, 2007a; Cakmak et al., 2007; Ratprasartporn et al., 2006; Cakmak and Ozsoyoglu, 2008a) that help scientists predict pathways in an organism’s metabolic network (with 86% precision and 74% recall).

· Automatic Inference of Gene/Protein Annotations from Literature through Text Mining: Genes and proteins are frequently annotated with the Gene Ontology (GO) concepts. The most reliable GO annotations of genes and gene products are created by biologists manually reading related papers and determining the proper GO concepts to be assigned to the corresponding genes. Nevertheless, locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. In this research, we developed automated text mining tools (Cakmak and Ozsoyoglu, 2008b; Cakmak and Ozsoyoglu, 2007b; Ratprasartporn et al., 2009) to annotate genes and gene products with the Gene Ontology concepts via capturing the related knowledge embedded in textual data to expedite and automate the annotation of genomic entities by GO concepts. The proposed algorithm has reached 78% precision and 61% recall.

· Metabolism Query Language: In another research, we designed a “metabolism query language” (MQL) (Cakmak et al., 2010; Cakmak et al., 2012; Cicek et al. 2014) that computationally captures the metabolism data and allows to query it at a detailed level. MQL accommodates metabolic network knowledge in a manner faithful to the underlying biochemistry. We also solve and present query processing techniques for MQL queries.

· Data & Visualization Models for Pathways: Signaling pathways are chains of interacting proteins, through which the cell converts a (usually) extracellular signal into a biological response. The number of known signaling pathways in the biological literature and on the web has been increasing at a very high rate, thus demanding a need for efficient ways of storing, visualizing, querying, and mining signaling pathways. In this work, first we briefly compare the data modeling and visualization capabilities of existing signaling pathways systems. Then, we present a signaling pathway data model and its visualization that subsumes the existing models. Our model visualizes a signaling pathway (a) as a nested graph, (b) with explicit location information (e.g., cell, tissue, organelle, nucleus, etc.), and (c) in four abstraction levels, namely, the levels of molecule-tomolecule signaling steps, collapsed sub-pathways, molecule-to-pathway connections, and pathway-to-pathway connections. We model (1) the effects of specific signaling steps, (2) state changes of signaling molecules, (3) various (extensible) structural/physical changes of signaling molecules such as complex formation, dissociation, assembly, oligomerization, di-/trimerization, cleavage and degradation, (4) condensation/hydrolysis signaling steps, and (5) exchanges and translocations as signaling steps. The visualization model gracefully models incomplete information and hierarchical levels of signaling molecules. Finally, we introduce a completely new visualization dimension for pathways, namely, Gene Ontology (GO)- based functional visualizations of pathways. We believe that functional visualizations of pathways provides new opportunities in understanding, defining and comparing existing pathways, and in helping discover new ones.

· Biological Web Data Source Integration: Biological web data sources have now become essential information sources for researchers. However, their use is tedious, labor-intensive, repetitive, and possibly involve the integration of data from multiple web data sources. In this paper, as a first step towards the full integration of web data sources, we propose a framework that allows an integrated use of biological sources in a task-oriented manner. We define and experimentally evaluate a toolkit-based framework for semi-automatically constructing an integrated (software) system that automates and optimizes the execution of a biology-related computational task at hand. To test and refine the principles of the framework, we build and evaluate “Pathway-Infer” as a benchmark integrated system.

· Taxonomy-superimposed Graph Mining: New graph structures where node labels are members of hierarchically organized ontologies or taxonomies have become commonplace in different domains, e.g., life sciences. It is a challenging task to mine for frequent patterns in this new graph model which we call taxonomy-superimposed graphs, as there may be many patterns that are implied by the generalization/specialization hierarchy of the associated node label taxonomy. Hence, standard graph mining techniques are not directly applicable. In this work, we develop Taxogram, a taxonomy-superimposed graph mining algorithm that can efficiently discover frequent graph structures in a database of taxonomy-superimposed graphs. Taxogram has two advantages: (i) It performs a subgraph isomorphism test once per class of patterns which are structurally isomorphic, but have different labels, and (ii) it reconciles standard graph mining methods with taxonomy-based graph mining and takes advantage of well-studied methods in the literature. Taxogram has three stages: (a) relabeling nodes in the input database, (b) mining pattern classes/families and constructing associated occurrence indices, and (c) computing patterns and eliminating useless (i.e., over-generalized) patterns by post-processing occurrence indices. Experimental results show that Taxogram is significantly more efficient and more scalable compared to other alternative approaches.

· Systems Biology Work: In this research, we designed and developed a Systems Biology platform (Cakmak et al., 2011; Coskun et al., 2012) that brings together, under a single database environment, metabolic pathways data and systems biology models. Besides, it provides expanded browsing, querying, visualization, and simulation capabilities to help with systems biology modeling and analysis, all brought about due to the integrated environment. This platform is built upon a pathways database system that we developed earlier (Elliott, 2008).

 

· Advanced Querying for Biological Networks: Querying biochemical networks in flexible ways over the web is important to facilitate ongoing biological research. In this work, we present a querying interface for biological networks, more specifically, metabolic networks. The interface allows for the specification of a large class of containment, path, and neighborhood queries with ease from a web browser. The query specification process is user-friendly, employs hierarchically arranged relationships among biological entities, and uses autocomplete features. The interface is provided as part of PathCase, a system to store, query, visualize and analyze metabolic pathways at different levels of detail.

· Mining Twitter for Company Mentions: Twitter is an online social networking website where people can post short messages on any subject, and these messages become visible to other users. Users intentionally express their opinions about companies or products via microblogging texts. Analyzing such messages might help explore what customers think about company products, or what the broad feelings of customers are. Identifying tweets referring to products and companies is becoming an important tool recently. However, company names are often vague. Hence, the first step is to locate the messages that are relevant to a company. In this paper, we present a number of supervised learning techniques to decide whether a given tweet is about a company, e.g., whether a message containing the term ‘amazon’is related to the company Amazon Inc. or not. Solving this task is challenging in comparison to the classical classification process. The main difficulty with this problem is that tweets and company names include limited information. To make this task tractable, external resources are used to get richer data about a company. More specifically, we generate several profiles for each organization, which contain richer information. Then we perform feature extraction to obtain both numerical and categorical features and we do feature selection to identify the most relevant attributes with our task. Finally, we train several supervised classifiers. Our constructed classifiers and carefully selected features provide high accuracy on the WePS-3 dataset.

· Sports Analytics: In recent years, computerized tracking systems that can collect spatiotemporal data from soccer games have become commonplace in major leagues and international competitions. In this research, we developed an interactive visual analytics tool (Delibas et al., 2019a) that can be used to perform an exploratory analysis of soccer data. The visualizations are enriched by utilizing various data mining and machine learning algorithms in the backend. The tool also provides advanced analytics options such as pass success, ball ownership, optimal shooting point, and pass effectiveness prediction (Cakmak et al., 2018). We also filed an international patent (Delibas et al., 2019b) that describes an intelligent system to help players make the most optimal decision during training sessions by taking advantage of the aid of a wearable device similar to the Google Glass with instant feedback.

 

· Query Optimization Work: During my 4 years tenure at Oracle, Inc. (San Francisco, CA), I developed two novel histogram structures, namely, Hybrid Histograms and Frequency-based Histograms, which provide more accurate predicate selectivity estimations during query optimization. Furthermore, I also developed a framework that gathers database statistics for multiple objects in a highly scalable manner that improved statistics gathering time up to 10x. These developments are included in two international patent filings (Chakkappen et al., 2016; Chakkappen et al., 2017). Furthermore, these features are released to the users in Oracle’s market leader Database Management System flagship product in version 12c (Belknap et al., 2013).

- Bibliography:

 

Aytimur, M., & Cakmak, A. (2018). Estimating the selectivity of LIKE queries using pattern-based histograms. Turkish Journal of Electrical Engineering & Computer Sciences, 26(6), 3319-3334.

 

Aytimur, M., Cakmak, A. (2021). Using positional sequence patterns to estimate the selectivity of SQL LIKE queries. Expert Systems with Applications, 165, 113762.

 

Belknap, P., Cakmak, A., Chakkappen, S., Chan, I., Chatterjee, D., Das, D., ... & Lee, A. (2013). Oracle Database SQL Tuning Guide, 12c Release 1 (12.1) E15858-15.

 

Cakmak, A. LIKE Selectivity Estimation. (2018) International Patent App. PCT/TR2018/050366.

 

Cakmak, A., Qi, X., Coskun, S. A., Das, M., Cheng, E., Cicek, A. E., Lai, N., Ozsoyoglu, G. & Ozsoyoglu, Z. M. (2011). PathCase-SB architecture and database design. BMC systems biology, 5(1), 188.

 

Cakmak, A., Qi, X., Cicek, A. E., Bederman, I., Henderson, L., Drumm, M., & Ozsoyoglu, G. (2012). A new metabolomics analysis technique: steady-state metabolic network dynamics analysis. Journal of bioinformatics and computational biology, 10(01), 1240003.

                                  

Cakmak, A., Celik, MH. (2018). A System for Diagnosing Diseases. International Patent App. PCT/TR2019/051101.

 

Cakmak, A., Kirac, M., Reynolds, M. R., Ozsoyoglu, Z. M., & Ozsoyoglu, G. (2007, July). Gene ontology-based annotation analysis and categorization of metabolic pathways. In 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007) (pp. 33-33). IEEE.

 

Cakmak, A., & Ozsoyoglu, G. (2007a). Mining biological networks for unknown pathways. Bioinformatics, 23(20), 2775-2783.

 

Cakmak, A., & Özsoyoglu, G. (2007b). Annotating genes by mining PubMed. In Pacific Symposium on Biocomputing.

 

Cakmak, A., & Ozsoyoglu, G. (2008a). Taxonomy-superimposed graph mining. In Proceedings of the 11th international conference on Extending database technology: Advances in database technology (pp. 217-228).

 

Cakmak, A., & Ozsoyoglu, G. (2008b). Discovering gene annotations in biomedical text databases. BMC bioinformatics, 9(1), 143.

 

Cakmak, A., Ozsoyoglu, G., & Hanson, R. W. (2010). Querying metabolism under different physiological constraints. Journal of bioinformatics and computational biology, 8(02), 247-293.

 

Cakmak, A., Uzun, A., & Delibas, E. (2018). Computational modeling of pass effectiveness in soccer. Advances in Complex Systems, 21(03n04), 1850010.

 

Cakmak, A., & Celik, M. H. (2020). Personalized Metabolic Analysis of Diseases. IEEE/ACM Transactions on Computational Biology and Bioinformatics (in press).

 

Celik, M. H., Saleh, T., Dokay, A., Cakmak, A. (2020). MetaboliticsDB: a database of Metabolomics analyses. (Under major revision for IEEE/ACM Transactions on Computational Biology and Bioinformatics). The web tool is available online at http://metabolitics.biodb.sehir.edu.tr/ .

 

Cicek, A.E., Qi, X., Cakmak, A., Johnson, S.R., Han, X., Alshalwi, S., Ozsoyoglu, Z.M. and Ozsoyoglu, G., 2014. An online system for metabolic network analysis. Database (Oxford Univ. press).

 

Chakkappen, S. P., Zait, M., Lee, A. W., & Cakmak, A. (2016). U.S. Patent No. 9,471,631. Washington, DC: U.S. Patent and Trademark Office.

 

Chakkappen, S. P., Zait, M., Lee, A. W., & Cakmak, A. (2017). U.S. Patent Application No. 15/295,539.

 

Coskun, S.A., Qi, X., Cakmak, A., Cheng, E., Cicek, A.E., Yang, L., Jadeja, R., Dash, R.K., Lai, N., Ozsoyoglu, G. and Ozsoyoglu, Z.M. (2012). PathCase-SB: integrating data sources and providing tools for systems biology research. BMC systems biology, 6(1), p.67.

 

Davletov, F., Aydin, A. S., & Cakmak, A. (2014, November). High impact academic paper prediction using temporal and topological features. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (pp. 491-498).

 

Delibas, E., Uzun, A., Inan, M. F., Guzey, O., & Cakmak, A. (2019a). Interactive exploratory soccer data analytics. INFOR: Information Systems and Operational Research, 57(2), 141-164.

 

Delibas, E., Uzun, A., Inan, M. F., Guzey, O., & Cakmak, A. (2019b). U.S. Patent Application No. 16/331,744.

 

Elliott, B., Kirac, M., Cakmak, A., Yavas, G., Mayes, S., Cheng, E., ... & Meral Ozsoyoglu, Z. (2008). PathCase: pathways database system. Bioinformatics, 24(21), 2526-2533.

 

Ratprasartporn, N., Cakmak, A., & Ozsoyoglu, G. (2006, July). On data and visualization models for signaling pathways. In 18th International Conference on Scientific and Statistical Database Management (SSDBM'06) (pp. 133-142). IEEE.

 

Ratprasartporn, N., Po, J., Cakmak, A., Bani-Ahmad, S., & Ozsoyoglu, G. (2009). Context-based literature digital collection search. The VLDB Journal, 18(1), 277-301.

 

Sohsah, G. N., Ibrahimzada, A. R., Ayaz, H., Cakmak, A. (2020). Scalable classification of organisms into a taxonomy using hierarchical supervised learners. Journal of Bioinformatics and Computational Biology, 18(05), 2050026.