PMID- 30534632
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240405
VI  - 43
DP  - 2018
TI  - Optimal Chemical Grouping and Sorbent Material Design by Data Analysis, Modeling 
      and Dimensionality Reduction Techniques.
PG  - 421-426
LID - 10.1016/B978-0-444-64235-6.50076-0 [doi]
AB  - The ultimate goal of the Texas A&M Superfund program is to develop comprehensive 
      tools and models for addressing exposure to chemical mixtures during 
      environmental emergency-related contamination events. With that goal, we aim to 
      design a framework for optimal grouping of chemical mixtures based on their 
      chemical characteristics and bioactivity properties, and facilitate comparative 
      assessment of their human health impacts through read-across. The optimal 
      clustering of the chemical mixtures guides the selection of sorption material in 
      such a way that the adverse health effects of each group are mitigated. Here, we 
      perform (i) hierarchical clustering of complex substances using chemical and 
      biological data, and (ii) predictive modeling of the sorption activity of 
      broad-acting materials via regression techniques. Dimensionality reduction 
      techniques are also incorporated to further improve the results. We adopt several 
      recent examples of chemical substances of Unknown or Variable composition Complex 
      reaction products and Biological materials (UVCB) as benchmark complex 
      substances, where the grouping of them is optimized by maximizing the 
      Fowlkes-Mallows (FM) index. The effect of clustering method and different 
      visualization techniques are shown to influence the communication of the 
      groupings for read-across.
FAU - Onel, Melis
AU  - Onel M
AD  - Artie McFerrin Department of Chemical Engineering, Texas A&M University, College 
      Station, TX, 77843, USA.
AD  - Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, 
      USA.
FAU - Beykal, Burcu
AU  - Beykal B
AD  - Artie McFerrin Department of Chemical Engineering, Texas A&M University, College 
      Station, TX, 77843, USA.
AD  - Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, 
      USA.
FAU - Wang, Meichen
AU  - Wang M
AD  - Department of Veterinary Integrative Biosciences, Texas A&M University, College 
      Station, TX, 77843, USA.
FAU - Grimm, Fabian A
AU  - Grimm FA
AD  - Department of Veterinary Integrative Biosciences, Texas A&M University, College 
      Station, TX, 77843, USA.
FAU - Zhou, Lan
AU  - Zhou L
AD  - Department of Statistics, Texas A&M University, College Station, TX, 77843, USA.
FAU - Wright, Fred A
AU  - Wright FA
AD  - Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 
      27695-7566, USA.
FAU - Phillips, Timothy D
AU  - Phillips TD
AD  - Department of Veterinary Integrative Biosciences, Texas A&M University, College 
      Station, TX, 77843, USA.
FAU - Rusyn, Ivan
AU  - Rusyn I
AD  - Department of Veterinary Integrative Biosciences, Texas A&M University, College 
      Station, TX, 77843, USA.
FAU - Pistikopoulos, Efstratios N
AU  - Pistikopoulos EN
AD  - Artie McFerrin Department of Chemical Engineering, Texas A&M University, College 
      Station, TX, 77843, USA.
AD  - Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, 
      USA.
LA  - eng
GR  - P42 ES027704/ES/NIEHS NIH HHS/United States
PT  - Journal Article
DEP - 20180704
PL  - England
TA  - ESCAPE
JT  - ESCAPE. European Symposium on Computer Aided Process Engineering
JID - 101734507
PMC - PMC6284807
MID - NIHMS989329
OTO - NOTNLM
OT  - Clustering
OT  - dimensionality reduction
OT  - predictive modeling
OT  - read-across
EDAT- 2018/12/12 06:00
MHDA- 2018/12/12 06:01
PMCR- 2019/07/04
CRDT- 2018/12/12 06:00
PHST- 2018/12/12 06:00 [entrez]
PHST- 2018/12/12 06:00 [pubmed]
PHST- 2018/12/12 06:01 [medline]
PHST- 2019/07/04 00:00 [pmc-release]
AID - 10.1016/B978-0-444-64235-6.50076-0 [doi]
PST - ppublish
SO  - ESCAPE. 2018;43:421-426. doi: 10.1016/B978-0-444-64235-6.50076-0. Epub 2018 Jul 
      4.