My researches address the development of a practical approach and a computational platform for environmental risk assessment of Engineered Nanomaterials (ENMs), which increasing use in modern industrial products and processes has raised public concern regarding their potential release and damage to the environment. Development of such a platform requires the investigation of High Throughput Screening (HTS) data processing methods (e.g., outlier removal, normalization, hit-identification), data mining for ENMs bioactivity data (e.g., clustering analysis for similar ENMs and exploring relationships among ENMs bioactivities), and decision support approach for ENMs environmental risk assessment.
I am also participating in nanoinformatics initiatives and developing tools/resources for integration and management of heterogeneous information, defining ENMs ontology, and modeling based knowledge extraction. In particular, I am interested in the development of (Quantitative)-Structure-Activity Relationships ((Q)SARs)) that can predict ENMs bioactivity from their structural and physicochemical properties (i.e., nano-(Q)SAR)) and assist in the understanding of the mechanisms governing the behavior of ENMs in biological/ecological systems.
In addition to ENMs environmental risk assessment, my research interests also include general machine learning/data mining topics, such as development of supervised and unsupervised feature selection methods (to identify suitable features/descriptors for machine learning/data mining model development) and pattern recognition methods for biological images.
Data Mining/Machine Learning
Environmental Risk Assessment
Data Minining for Environmental Problems
Development of a Practical Approach for ENM Environmental Impact Assessment
Development of Decision Support Tools for ENM Environmental Impact Assessment
Building Data-Driven Models based on HTS Data of ENM Bioactivity
Development of HTS Data Processing Methods for Normalization, Hit-Identification, and Visualization
Nano(Q)-SAR Development for Prediction of ENMs Bioactivity
Identification of Relationships among different Cellular Response induced by ENMs
Software Development for Nano-/HTS-/Bio-Informatics
Development of Feature Selections with scalability to Big-Data