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$29.75 USD
PHP
Windows Desktop
Engineering
C# Programming
Software Architecture
+3更多
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经验
Software Engineer
10月 2008 - 5月 2011 (2 年, 7 个月)
Worked as a Java EE developer and was responsible for adding functionality to company products as well as fixing bugs reported internally or by the customer. In addition, other duties included customer support and on-site deployment of software.
Projects:
- Baggage Reconciliation System (BRS)
- OFOQ Talent Management System (OTMS)
Tools/Technologies used:
- Java EE, JSP, JSF, JavaScript, EJB 3.0, JPA
- IBM Websphere, Oracle Application Server, ActiveVOS, Glassfish, Liferay
- IBM DB2, Oracle DB
教育
PhD in Computer Science
(5 年)
MSc in Bioinformatics
(1 年)
B.Sc. in Computer Science
(4 年)
资质
Machine Learning Foundations: A Case Study Approach
Coursera
2015
Introduction to Big Data
Coursera
2015
Intro to using Hadoop and MapReduce.
出版物
Computational Prediction of Drug-Target Interactions via Ensemble Learning
Springer Protocols (Methods in Molecular Biology)
Book chapter on prediction of drug-target interactions via ensemble learning.
Link: https://link.springer.com/protocol/10.1007/978-1-4939-8955-3_14
Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Came up with a new method that improves prediction performance over preceding algorithms for drug-target interaction prediction. In this work, matrix factorization and manifold learning (graph regularization) were used.
Link: https://doi.org/10.1109/TCBB.2016.2530062
Drug-target interaction prediction using ensemble learning and dimensionality reduction
EL-SEVIER (METHODS)
An improvised machine learning method was devised to deal with issues in the data being used to train the prediction model, namely the huge size of the data, the high dimensionality of the data and the severe class imbalance in the data.
Link: https://doi.org/10.1016/j.ymeth.2017.05.016
Computational prediction of drug–target interactions using chemogenomic approaches: empirical survey
Oxford University Press (Briefings in Bioinformatics)
A survey on computational methods for prediction of drug–target interactions (DTIs), an essential task in drug discovery.
Link: https://doi.org/10.1093/bib/bby002
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