THOMAS OLSON ATC CODE SOFTWARE
THOMAS OLSON ATC CODE CODE
THOMAS OLSON ATC CODE PROFESSIONAL

Professor Hashash is a Fellow of the American Society of Civil Engineers (ASCE), a past president of the Geo-institute of ASCE and has received a number of teaching, university and professional awards including the Presidential Early Career Award for Scientists and Engineers and the ASCE 2014 Peck medal. He is the geotechnical co-leader of the NIST (National Institute of Standards and Technology) led investigation into the Champlain Towers South Collapse in Surfside, Florida.

THOMAS OLSON ATC CODE SOFTWARE

His research group developed the software program DEEPSOIL that is used worldwide for evaluation of soil response to earthquake shaking. His work on seismic design of underground structures is extensively used in engineering practice. He has published numerous journal articles and is co-inventor on four patents. He also works on geotechnical engineering applications of deep learning, artificial intelligence, visualization, augmented reality, imaging and drone technologies. His research focus includes deep excavations and tunneling in urban areas, earthquake engineering, continuum and discrete element modeling and soil-structure interaction as well as resiliency and sustainability of the built infrastructure. He teaches courses in Geotechnical Engineering, Numerical Modeling in Geomechanics, Geotechnical Earthquake Engineering, Tunneling in Soil and Rock, and Excavation and Support Systems. Professor Hashash joined the faculty of the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign in 1998. and Canada including the Boston Central Artery/Tunnel project. In 1994 he joined Parsons Brinckerhoff in San Francisco and worked on a number of underground construction projects in the U.S. He began his career with the PB/MK TEAM in Dallas on the Superconducting Super Collider Project. (1992) in civil engineering from the Massachusetts Institute of Technology.

Sustainable and Resilient Infrastructure Systems.

A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Kuhn K, Szklarczyk D, Pletscher-Frankild S, Blicher TH, Von Mering C, Jensen L, Bork P. Predicting Anatomical Therapeutic Chemical (ATC) Classification of drugs by integrating chemical-chemical interactions and similarities. Drug repositioning: identifying and developing new uses for existing drugs. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Lipinski C, Lombardo F, Dominy B, Feeney P. This simplifies the prediction and allows for improved accuracy.

THOMAS OLSON ATC CODE CODE

Tiered learning utilizes this observation to constrain the learning space for ATC codes at a particular level based on the ATC code at higher levels. Thus, there exists a characteristic distribution of the ATC codes, which can be leveraged to limit the search-space of possible codes that can be ascribed at a particular level once the codes at the preceding levels are known. The basis of our approach lies in the observation that anatomical-therapeutic biological activity of certain types typically precludes activities of many other types.

Additionally, the experiments demonstrated the generalizability of the tiered learning architecture, in that its use was found to improve prediction rates for a majority of machine learning algorithms when compared to their stand-alone application. The prediction accuracy obtained with tiered learning was found to be either comparable or better than that of established methods.

The validation experiments compared chemical descriptors, initialization methods and classification algorithms.

The proposed approach was validated using a number of compounds in both cross-validation and test setting. We propose a machine learning architecture called tiered learning for prediction of ATC codes that relies on the prediction results of the higher levels of the ATC code to simplify the predictions of the lower levels. The ability to predict ATC codes of compounds can assist in creation of high-quality chemical libraries for drug screening and in applications such as drug repositioning. The Anatomical Therapeutic Chemical (ATC) Code System is a World Health Organization (WHO) proposed classification that assigns multi-level codes to compounds based on their therapeutic, pharmacological and chemical characteristics as well as the in-vivo sites(s) of activity. The low success rate and high cost of drug discovery requires the development of new paradigms to identify molecules of therapeutic value.

Thomas olson atc code