Index

Note: Page numbers followed by f indicate figures and t indicate tables.

A

AAC  See Amino acid composition (AAC)
ABAQUS V6.13 156–158
Acquired immunodeficiency syndrome (AIDS) 69
Active site water molecules 490–491
Adaptive directional microphone (ADM) strategy 320
Adaptive immunetworks 125–127, 126–127f, 128t
Akaike information criterion (AIC) 591
Alpha carbon 99
Amino acid composition (AAC) 107–108, 110t
ANN model  See Artificial neural networks (ANN) model
ANOVA 340
Answer set programming (ASP) formalisms 415, 424–425
Anteroalteral stress distribution 160–161, 161–162f, 164
Apolipoprotein B 400
Area under the curve (AUC) 102, 508
Artificial mind 
definition 378
life and information 386
Artificial neural networks (ANN) model 
application 4–5
immune cell differentiation 
back-propagation 9–11, 12f
data analysis with noise 13–15, 14t
vs. LR model 11–15, 13t
prediction errors 9–11, 11t
R named neuralnet 9–11
vs. SVM model 12–13, 13t, 15
multilayer perceptron (MLP) structure 4–5, 4f
Augmented Dickey-Fuller (ADF) test 591, 592t
Auto-correlation function (ACF) 592, 592f, 592t
Auto covariance (AC) 106, 110t
Auto Regressive Integrated Moving Average (ARIMA) model 
clinical laboratory test volume estimation 
ACF 592, 592f, 592t
AIC 591–592, 593t
assumption testing 591
autoregressive model 590
BIC 591–592, 593t
moving average model 590
performance comparison 594, 594t
R statistical package 591
vs. HWM 594, 594t
provincial test volume time series 594, 596f

B

BAM conversion 526
Bayesian belief network (BBN) 476–478, 477f
Bayesian estimation 461
Bayesian information criterion (BIC) 591
BBN  See Bayesian belief network (BBN)
BCI  See Binaural cochlear implant (BCI) coding
Beamformer (BF) algorithm 
KEMAR 310
noise reduction 308
schematic representation 310, 310f
BET  See Bonding evolution theory (BET)
BFAST 525
Biliary atresia control network 
attractors 134–137, 136t
attractors calculation 134, 137f
gene expression 133–134, 133f, 135f
negative circuits 134, 135f
Binary tree (BT), NCA 76–79, 77–78f, 80f, 84, 86f
Binaural cochlear implant (BCI) coding 308
algorithm influence 320
Beamformer 308
Doerbecker’s processing 308
full recognition 317
ILD 308
ITD 308
listeners 316
localization task 315–317
noise angle 321
normal hearing listeners 321
phoneme recognition session 
phonetic material 313–315
recognition test 315
training session 315
reinjection factor 321
signal processing 
beamformer 310
Doerbecker’s processing 311–313
synoptic representation 309, 309f
Vocoder 313
SNR influence 320–321
source localization 317–320
specific recognition 317
spectral subtraction 308
speech intelligibility 308
Biochemical reaction network 457–458
BNC models 473–474, 474t, 474f
Bonding evolution theory (BET) 173–174
Bowtie2 524–525
Burrows-Wheeler Aligner (BWA) 524–525
Business intelligence (BI) framework 
administration services 583
ARIMA  See Auto Regressive Integrated Moving Average (ARIMA) model
clinical laboratory facility 577–578
clinical laboratory test usage patterns visualization 
average usage pattern 588, 588f
CH7 test 587, 587f
data source and methods 585–586
limitations 588
data source 579
data warehouse management service 583
definition 577
disadvantages 578
driving motivation 578–579
emerging framework 
clinical Big Data analytics 580
clinical laboratory setting 580–582, 581–582f
clinical section managers 580–581
EH&S managers 580–581
finance managers 580–581
human resources managers 580–581
IT service administration managers 581
planning and new business managers 580–581
user-centered framework architecture 581–582, 582f
Hadoop distributed processing platform 580
HEALTH cluster 583
Holt-Winters  See Holt-Winters model
lab management application interface 583
laboratory management system components 582
MapReduce framework 579–580
operational management services 583
service infrastructure-hadoop platform 583
test procedure services 583
typical framework usage 584
Ubuntu Juju 584

C

CD4+ T cell differentiation model  See also Immune cell differentiation modeling
kinetic equations 2–3
ODE model 1–2
SBML model 1, 6, 7f
sigmoidal Hill equations 2–3
Chaotic resonance (CR) 356–358
chaotic neural network 355–356
concept 357f
dependence on parameter d 368–370
dependence on signal strength A 371
Izhikevich neuron model, periodic signal 367–368
spiking neural systems 356–358
Charm Bundles 584
Chemical seven test (CH7) test 587, 587f
Classic watchmaker analogy 
folk psychology 383
mental life and information 383–385
CLAST  See CLustering Any Sequence Tool (CLAST)
Clinical Big Data 577
Clustering 
definition 51–52
DNA microarray 51–52
hierarchical algorithm 52
agglomerative approach 53
definition 53
divisive approach 53
height differences 57, 58f, 59
KRAS-positive data set 59–60, 59f
normal tissue data set 57, 58f
pseudonode 53–55, 55f
hierarchical K-means algorithm 56, 56f
K-means algorithm 52–53, 54f
CLustering Any Sequence Tool (CLAST) 
edge-weight-based approach 204–205
equivalence-order methods 204
hashing methods 204–206
matching 206–210
MGS 25 213–216
PALM2-AKAP2 gene cluster 217f
SGS data sets 217–219
Smith-Waterman algorithm 204
steady-state solution 213–216
transcript sequences 211–213
CM  See Covariance model (CM)
CNAs  See Copy number alterations (CNAs)
Coevolutionary divergence (CD) method 105, 105t
Composed functional similarity (CFSim) 226
Computational workflow 
adjustment module 507, 507f
alignment 510–511, 511f
baseline subtraction 507–508, 508f
binning module 506, 506f
KD3 505
normalization effects 508, 509f
peak detection 509
preprocessing and evaluation steps 505, 505f
quality assessment 509–510
recalibration process 511
spectra format 511
spectra generation 511
Consciousness 
access consciousness 
androids 380
blind sight 379
consciousness of human beings 381
IDA and ACT-R 380
mental state 379
Necker flip 380–381
reasoning and action rational control 378
representation 378
SOAR 380
phenomenal consciousness 
consciousness of human beings 381
definition 378
representational component 378
self-awareness 379
sensations of pain 379
Consensus docking 493–494
Continuous wavelet transform (CWT) 543, 544f
COordinate Rotation DIgital Computer (CORDIC) acceleration technique 
configurations for evaluating functions 26–27, 27t
hardware submodules 30–31, 30f
iteration equations 26–27
prescaling identities 27, 28t
Copy number alterations (CNAs) 
atherosclerosis 
angiotensin II receptor, type 1 400
apolipoprotein B 400
lipoprotein (A) 400
NSAIDs 400
zinc fingers and homeoboxes 2 gene 400
cancer treatment 
EGFR oncogene 399
glutathione-S-transferase 399
thioredoxin reductase 399
transforming growth factor β receptor 1 gene 399–400
combination therapies 394–395
drive proliferation 391–392
drug repositioning/repurposing 390
FDA-approved drug 390–391
GBM brain tumor 393–394
genetic survival networks 
in glioma cells 392
ovarian cancer cells 392
in radiation hybrid cells 393
molecular networks 391
pan-cancer CNA 392
synergistic strategy 401
thalidomide 391
Cornea  See Keratoconus disease
Covariance model (CM) 429
sensitivity and specificity 432t
sequence-structure alignment 431
states and transition rules 430
stochastic context-free grammar 430
updated CM 430–431
Critical Assessment of (protein) Structure Prediction (CASP) 440
Cumulative distribution function (CDF) 462–464, 463f

D

Database of Useful Decoys–Enhanced (DUD-E) 492
Data-mining 
bioinformatic data-mining 267
three-base windows  See Open Reading Frame (ORF)
triplets 267
DCC  See Domain cohesion and coupling (DCC)
Detection threshold 509
DFT computational methods 174
Diels-Alder (DA) reaction 
activation enthalpy 175
activation free energy 175
BET 173–174
computational methods 174
cyclization reaction 172–173, 173s
ELF analysis 
attractor positions and atom numbering 186–187, 188f
disynaptic basins 186–189
electron density 186
monosynaptic basins 186–189
MPWB1K/6-31G 189
pseudoradical centers 186, 189
valence basin populations 186–187, 188t
ELF bonding 179, 180t
Houk’s theozyme 172, 173s
macrocyclic lactone 176, 177–178f, 178–179
MMFF conformation 183–184, 184f
into tricyclic compound 184–185, 185–186f, 185t
Paraherquamide A 171–172, 172s
potential energy surface 174–175, 175t
Spinosyn A 
biosynthesis 170, 170s
charge transfer (CT) 171
genes 170–171
transition state models 171, 171f
Dindel model 526
Discrete wavelet transform (DWT) 542
DNA double-strand breaks (DSBs) 
causality logic 415
Bayesian approaches/probabilistic logics 414
biological systems 414
inhibition 414
modal logics 414
protein B 414
cell cycle 410
cellular dynamics 410
classical logic 414–415
default logic 423, 424f
ASP formalisms 415, 424–425
definition 416
extensions 416–417
normal defaults 416
transforms 416
knowledge discovery 423, 424f
logic of hypothesis 423
nonmonotonic logics 415
Pommier model 423, 424f
Prolog program 422–423
propositional logic 413–414
p53 tumor suppressor protein 423
signaling pathway 418–419
abduction 412f, 417–418, 419f
cancer, ultraviolets and genes 412–413, 412f
clauses and Horn clauses 419–420
default rules 421–422
drawback 421
hard rules 421–422
language syntax 420–421
protein interactions 411–412, 411–412f
protein production and activation 422
SOLAR 424
DNA microarrays 51
Doerbecker’s processing 308
Domain cohesion and coupling (DCC) 112, 115t
Double-stranded DNA (dsDNA) sequences 
data-mining 
three-base windows  See Open Reading Frame (ORF)
triplets 267
molecular genetics 266–267, 266f
UGA and UGG, properties 265–266, 266f
DUD-E  See Database of Useful Decoys-Enhanced (DUD-E)

E

Early Traceback Viterbi (ETB-Viterbi) 109, 110t
EEG recording 337
Elderly fall prevention 
assessment decision algorithm 331–332
fall detection technologies 327
human-centric approach 326–327
lanyards 327
local audio alarms 327
pressure cushions 327
risk factors 325–326
senior population, in US 326f
Sparrow system 
algorithm 331
characteristics 328–329
fall tracking and fall reduction outcomes 332–333
imaging sensors 329
medical professional 333
multifacility outcome tracking 330, 330f
multimodal strategy 329
Sparrow prototype P-II 329, 329f
system installation and setup 330
third-generation Sparrow prototype (P-III) 330
Electronic health record (EHR) 292
Electron localization functions (ELF), bonding analysis 174, 179, 180t
attractor positions and atom numbering 186–187, 188f
disynaptic basins 186–189
electron density 186
monosynaptic basins 186–189
MPWB1K/6-31G 189
pseudoradical centers 186, 189
valence basin populations 186–187, 188t
ELF  See Electron localization functions (ELF), bonding analysis
ENISI Visual 3–4, 6–8
Ensemble docking 492–493
Enteric Immunity Simulator (ENISI) 3
Environmental health and safety (EH&S) managers 580–581
Equipartition conjecture (EC) 74
Eroom’s Law 389–390
Extensible Markup Language (XML) 277

F

FAIBG  See Fibular allograft with impaction bone grafting (FAIBG)
Femoral head necrosis (FHN) 
definition 155
FAIBG  See Fibular allograft with impaction bone grafting (FAIBG)
FHN  See Femoral head necrosis (FHN)
Fibular allograft with impaction bone grafting (FAIBG) 
ABAQUS V6.13 156–158
anteroalteral stress distribution 160–161, 161–162f, 164
debridement radius, necrotic bone 159, 159f
definition 155–156
JIC classification 156
load and constraint conditions 157–158, 158f
model validation 162–163, 163f
NURBS models 156–157
peak stress, residual necrotic bone 161–162, 163f
solid models 156–157
stress transfer path 159, 160f, 164
thorough debridement 155–156
Fisher score criterion (FCS) 247
Flexible docking 492
FoG diagnosis 
average accuracy 474–475, 475t
BNC models 473–474, 474t, 474f
causality and methodology 472–473, 473f
data acquisition and preparation 
foot accelerometer 470, 471f, 472t
freeze index (FI) 469–470, 472
goniometer 470, 471f, 472t
multisensor device 469
shin accelerometer 470, 471f, 472t
signals 469–470, 470f
telemeter 470, 471f, 472t
definition 468
FoG-precision 474–475, 475t
noFoG-precision 474–475, 475t
PGM 469
Fourier transform (FT) 537
FreeBayes 526
Free induction decays (FIDs) 539
Freeze index (FI) 469–470, 472

G

Gene expression analysis 
Bayes Network classifier 252
breast cancer SVM 257f
Chor’s incremental algorithm 244–247
computational geometry tools 235–236
data sets 249t
feature selection (FS) 262t
breast cancer 261f
leukemia 258f
lung cancer 260f
SRBCT 259f
fine filtration stage 252
10-fold cross validation 253f
gene selection 
coarse filtration 248
fine filtration 249
Fisher score criterion 247
significance analysis of microarrays 247–248
values and class label 247
incremental approach 
three-dimensional 243–244
two-dimensional 241–243
LP formulation of separability 
d-dimensional Euclidean space 237
Megiddo’s and Dyer’s technique 238
prune and search technique 238
machine learning (ML) tools 252
offline approach 239–240
PLSTs 252
SRBCT BayesNet accuracy vs. feature space 255f
SRBCT SVM 255f
two and three-dimensional linear separability 250, 251t
Unger’s linear programming formulation 244–247
Gene ontology (GO) 51–52
data set 62, 63f
GOEAST 60–62
KRAS-positive tissue data set 60, 62f
normal tissue data set 60, 61f
terms and pathways 62, 64t
Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) 60–62
Genetic algorithm (GA) quantification 
advantage of 538
capabilities of 538
chromosome structure 546f
evolutionary optimization 537–538
fitness calculation 545–546
fittest individuals, survival of 545
genetic methods, terminology in 545
high complex search spaces, noisy conditions 538
Monte Carlo analysis 554–555, 557t
quantification results for 
amplitude parameters 549–551, 549t, 551t
damping factor parameters 551, 552t
relative error (RE) 548–549, 554–555, 556–557f
settings of 548–549, 548t
Genetic regulatory networks 
archetypal sequence AL 147–149, 148–149f
attractors 123–124
biliary atresia control network 
attractors 134–137, 136t
attractors calculation 134, 137f
gene expression 133–134, 133f, 135f
negative circuits 134, 135f
circular Hamming distance 146–147
genetic threshold Boolean regulatory network 138–140
immunetworks 
adaptive immunetworks 125–127, 126–127f, 128t
links with microRNAs 124–125
TLR and ICAM1 expression 124, 125f, 126t
iron control network 127–129, 128–129t, 129f
morphogenetic network 130–132, 130f, 131t
propositions 140–142, 142t
state-dependent updating schedule 145–146
tangent and intersecting circuits 142–145, 142f, 145t
Genomic data privacy model 
bio-bank and DNA transaction model 
Asp After Obfuscation 608, 609t
Asp Before Obfuscation 608, 609t
codon frequency table 608, 609f
framework 606f
obfuscation data processing component 607–608, 608f
original data storage component 607
patient record component 607
codon frequency/codon usage table 603–604
confidential transaction 605
data obfuscation 604
data shuffling 604
DNA cryptographic and steganography algorithms 605
Google Scholar search 604–605, 604f
holistic approach 605
original and obfuscated protein sequence 
after reverse translation process 614, 615f
amino acid frequency 612, 613f
codon frequency 611–612, 612f
dot matrix 613–614, 614–616f, 616
pairwise alignment analysis 613, 614f
synthetic patient records data set 610
patient ID mapping 611, 611f
random number generator 610–611, 610f
shuffled values 610–611, 611f
Glioblastoma multiforme (GBM) brain tumors 393–394
Global immunetwork 125–126, 127f
GO  See Gene ontology (GO)
GOEAST  See Gene Ontology Enrichment Analysis Software Toolkit (GOEAST)
Graphical user interface (GUI) 516
Grid-based Fast SLAM algorithm 
architecture 31–34, 33f
computational bottlenecks identifications 25–26, 25f
execution times 34–35, 34f
partitioning of Gaussian distribution 28
steps based on PF 24–25

H

Hadoop Distributed File System (HDFS) 580
Hadoop enabled automated laboratory transformation hub (HEALTH) cluster 583
Handwriting and speech syndromes (HSS) 
BBN 476–478, 477f
experimental protocol 476, 476f
global methodology 478, 479f
PDP 475
Herschel-Bulkley fluid  See Tapered artery
Hidden Markov model (HMM) 109
Hierarchical algorithm 52
agglomerative approach 53
definition 53
divisive approach 53
height differences 57, 58f, 59
KRAS-positive data set 59–60, 59f
normal tissue data set 57, 58f
pseudonode 53–55, 55f
Hierarchical K-means algorithm 56, 56f
Hierarchical Latent Class (HLC) models 476–477
High-throughput virtual screening (HTVS) 
active site water molecules 490–491
crystal structures and decoys 491–492
databases 487
MM-GBSA 494–495
MM-PBSA 494–495
pharmacophore model 496
protonation states 488–490
Histidine (His) 489
Hodgkin-Huxley (HH) model 355
Holt-Winters model (HWM) 
additive model 
AIC 591, 595–596, 596t
BIC 595–596, 596t
level, trend, and seasonal index 590
limitations 595f, 598, 598t
provincial test volume time series 594, 595f, 596, 597f
residual error histogram 596, 597f
smoothing factors 594, 594t
vs. ARIMA model 594, 594t
assumption testing 591
MSE 590
multiplicative model 
AIC 591, 595, 596t
BIC 595, 596t
level, trend, and seasonal index 589–590
provincial test volume time series 594, 595f
smoothing factors 594, 594t
R statistical package 591
Houk’s theozyme 172, 173s
HSS  See Handwriting and speech syndromes (HSS)
HTVS  See High-throughput virtual screening (HTVS)
Human immunodeficiency virus type 1 (HIV-1) 69 See also N-aryloxazolidinone-5-carboxamides (NCAs)
Human MicroRNA disease database (HMDD) 222

I

ILE  See Intentionally linked entities (ILE)
Immune cell differentiation modeling 
ANN model 
back-propagation 9–11, 12f
data analysis with noise 13–15, 14t
vs. LR model 11–15, 13t
prediction errors 9–11, 11t
R named neuralnet 9–11
vs. SVM model 12–13, 13t, 15
data 9, 10t
functional relationship 9
MSM and model reduction 3–4, 3f
problem definition 9
T-cell differentiation 9
Immunetworks 
adaptive immunetworks 125–127, 126–127f, 128t
links with microRNAs 124–125
TLR and ICAM1 expression 124, 125f, 126t
Information entropy (IE) 70–71, 73–74
Information technology (IT) service administration managers 581
Intentionally linked entities (ILE) 
components 287
drug dosing and administration 276
EHR 292
electronic health record 277
entity set object and entity object 287–289, 288f
epidemiological data modeling 275, 281–282
health care applications 275
E/R model 278, 278f
JMTZ’s relational database 279, 280f
patient-provider relationship 279, 279f
prescription relationship 280f
nonrelational approaches 
table-based NoSQL database 284, 285f, 286
XML approach 283–284, 284f
object-oriented database 276–277
object-oriented programming language 278
patient care and database analysis 275
per-entity relationship attribute 290–291
prescription relationship 287f
relational database system 276
relationship linking roles 291, 291f
relset objects and relationship objects 289–290
role objects 290
XML 277
Interaction Profile Hidden Markov Models (ipHMMs) 109
Inverse continuous wavelet transform (ICWT) 543–544, 544f
Iron control network 127–129, 128–129t, 129f
Izhikevich neuron model 
chaotic properties 
chaotic behaviors 362–366
evaluation indices 361–362
Poincaré section, Lyapu-nov exponent 361
chattering 359, 359f
intrinsically bursting 359, 359f
Lyapunov exponent 356
physiological parameters 358
regular spiking (RS) 358–359, 359f
spike-generation mechanisms 358
stable fixed point 359–360
stochastic resonance 355–356
unstable fixed point 359–360

J

Japanese Investigation Committee (JIC) 156

K

Keratoconus disease 
cropped quad-tree method 
architecture 564, 565f
decision bits 567, 568f
diamond square algorithm 569–570
logistic regression 569
MLP 567–568, 568f
normalization process 570, 570–571f
post-operational images 570, 571–572f
pre-operation images 570, 571–572f
schematic diagram 564–566, 566f
structure 566–567, 566–567f
texturing processes 570, 570–571f
data 564
definition 561
healthy cornea vs. Keratoconus cornea 561
Scheimpflug camera system 563
K-means algorithm 52–53, 54f
Knowledge Discovery in Databases (KDD) process/KD3 513
composition 513, 515f
functional object 515f, 516
workflow 515f, 516
Knowles Electronics Manikin for Acoustical Research (KEMAR) 310

L

Learning procedures (LPs) 74–75
Linear regression (LR) model 
vs. ANN model 14–15
lm function 11–12
predictive errors 13–14, 13–14t
Literals 419
Lotka-Volterra (LV) system 456, 458, 459f
LR model  See Linear regression model
Lung adenocarcinoma 
data set 57, 58t
GO 
data set 62, 63f
GOEAST 60–62
KRAS-positive tissue data set 60, 62f
normal tissue data set 60, 61f
terms and pathways 62, 64t

M

Machine learning (ML) 101
Macrocyclic lactone 176, 177–178f, 178–179
MMFF conformation 183–184, 184f
into tricyclic compound 184–185, 185–186f, 185t
Magnetic resonance spectroscopy imaging (MRSI) 558
Magnetic resonance spectroscopy (MRS) metabolite quantification 
artificial MRS signal 
frequency domain representation 546, 547f
peak parameters 548, 548t
real and imaginary parts of 546, 547f
curve-fitting procedure 538
FIDs 539
frequency-domain 537
Lorentzian line shape 539
MATLAB 546
in medical diagnosis 537
methodology 539, 540f
optimization problem 539, 545
single GA quantification  See Genetic algorithm (GA) quantification
SVD signal separation  See Singular value decomposition (SVD) analysis
time-domain 537, 539
wavelet denoising  See Wavelet denoising
MapReduce framework 579–580
Matched molecular pair analysis (MMPA) 93
Matrix-assisted laser desorption/ionization (MALDI) 504–505
Matthews correlation coefficient (MCC) 102
MEGADOCK 112–113, 115t
Merck molecular force field (MMFF) 174, 183–184, 184f
Metazoan mitochondrial DNA (mtDNA) codes 272
MicroRNA (miRNA) 
CFSim 226, 228–229f
functional similarity 221
gene ontology 221
genetic association database 225
GO 224
HMDD 222, 224
human MicroRNA oncogenic and tumor suppressors 225
MeSH 224
miRBase 221, 224
MIRIA database 225–226, 230–231f, 231
miRNA databases 
MicroCosm targets 222
MicroRNA.org 222
miR2disease 222
MiRNAMap 222
targetscan dataset 222
OMIT 223–224
TarBase 225
web framework 227, 230f
MIR  See Most Interacting Residues (MIR)
miRNA integration and analysis (MIRIA) platform 231–232
GO and MeSH ontologies 226
integrative database 223
Java language 225
portability 227
response time 227
type and add new disease 228–229, 230f
usability 227
user-friendly interface 227
web framework 227
Mirror tree method 103, 105t
MMFF  See Merck molecular force field (MMFF)
MolClas 75
Molecular docking 
active site water molecules 490–491
consensus docking 493–494
crystal structures and decoys 491–492
ensemble docking 492–493
flexible docking 492
MD simulations 493
MM-GBSA 494–495
MM-PBSA 494–495
protonation states 488–490
sampling algorithm 487–488
scoring function 487–488
structure-based drug design 495
Molecular dynamic (MD) simulations 493
Molecular mechanics generalized-Born/surface area (MM-GBSA) 494–495
Molecular mechanics Poisson-Boltzamann/surface area (MM-PBSA) 494–495
Monte Carlo analysis 554–555, 557t
Monte Carlo method 461
Morphogenetic network 130–132, 130f, 131t
Most Interacting Residues (MIR) 
algorithm 439
folding process 437–438
folding steps 439–440, 439f
hydrophobic positions 440
initial models 441–442, 441–442f
2lhc(A) and 2lhd(A) proteins 445, 448t, 448f
limitation 440
NCNs 439–440, 449–451
nucleus 438
protein core vs. nucleus 438, 438f
SMIR 439
dynamic results 444, 446f
graphical representation 443–444, 445f
interface 443–444, 443f
PDB ID 442–444
submission status 443–444, 444f
SSEs 437–438
TEFs 437–438
topohydrophobic positions 437–438
wild types (WTs) proteins 445–446, 447t, 448–449, 449t, 450f
MSM  See Multiscale modeling (MSM)
Multidrug-resistant (MDR) variants 69–70
Multilayer perceptron (MLP) 11–12, 568, 568f
Multiresolution analysis (MRA) 542
Multiscale modeling (MSM) 3–4, 3f

N

N-aryloxazolidinone-5-carboxamides (NCAs) 
binary tree (BT) 76–79, 77–78f, 80f, 84, 86f
classes analysis 79, 79t
EC 74
empirical function 91
grouping algorithm 71–73
IE 70–71, 73–74
LP 74–75
MMPA 93
molecular structure 69–70, 70f
PCA factor 81–82, 82t
definition 79–81
loadings 82, 83t, 84, 85f
profile 84, 84t
scores 84, 85f, 86, 88f
PCCs 75–76
PCD 75–76, 76f
periodic properties 86–91, 89t
principal components (PCs) 79–82
QSAR 93
radial tree (RT) 79, 81f, 84, 87f
similarity index 71
splits graph (SG) 79, 82f, 86, 88f
SplitsTree program 79
SPRs 79–81
stabilized similarity matrix 71–73
variation of property vs, counts 86–87, 90f
variation of property vs, group number 87–91, 90f, 92f
vector of properties 71, 72t
Noncoding RNA (ncRNA) 
covariance model 429–430
dynamic programming algorithm 430
length restrictions 431–432
sensitivity and specificity 432t
sequence-structure alignment 431
software tools 429
updated CM 430–431
Noncovalent neighbors (NCNs) 439–440, 449–451
Non-Newtonian fluid  See Tapered artery
Normal hearing listeners (NHLs) 321
NoSQL approaches 277
NURBS models 156–157

O

Object-oriented database (OODB) 276–277
Occipital and temporal electrical activity 344–349
apparatus and stimuli 335–336
behavioral data 342–344
co-occurring, unconsciousness 350–353
EEG recording 337
ERP data 340–341
experiments 337–339
procedure 336–337
One-dimensional inverse discrete wavelet transform (1D IDWT) 542
Open reading frame (ORF) 
algorithmic generator 267–268
coding sequences 272–273
environmental DNA 273
GenBank 267
genetic codes, alternatives 271–272, 271f
mtDNA codes 272
random fragments 273
recursive and exhaustive algorithms 268–269, 268f
stop codon evolution 272
triplet-based approximations 269–270, 270f

P

Paraherquamide A 171–172, 172s
Parkinson’s disease (PD) 
definition 467–468
FoG diagnosis  See FoG diagnosis
global methodology 
clustering 479–481, 479f, 480t
handwriting and speech patterns 478, 479f
PPN 478
global probabilistic model 481–482, 482f
handwriting and speech diagnosis 
BBN 476–478, 477f
experimental protocol 476, 476f
PDP 475
symptom 467–468
Partial correlation diagram (PCD) 75–76, 76f
Particle filtering (PF) 
Bayesian estimation framework 21–22
computational bottlenecks identifications 25–26, 25f
CORDIC acceleration technique 26–27
degeneracy problem 22–23
hardware/software partitioning 25–26
nonlinear filtering 21
particle degeneracy phenomenon 22
SIR 23
SLAM 23–25
SMC-based approach 22
VHSIC Hardware Description Language (VHDL). 34
Ziggurat acceleration technique 28–30
Particle Markov Chain Monte Carlo (pMCMC) 457
PD  See Parkinson’s disease (PD)
PD patient (PDP) 475–476
Pearson correlation coefficients (PCCs) 75–76
Pedunculopontine nucleus (PPN) 478
Pharmacophore model 496
PIPE  See Protein-protein Interaction Prediction Engine (PIPE)
Population extinction, biological systems 
competing species model 457–458, 459f
first-passage time 455–456
LV system 456
numerical study 
CDFs 462–464, 463f
input data 461–462, 462f
PDF 462–464, 463f
parameters estimation 460–461
PDF 457–460
pMCMC 457
stochasticity 455–456
Position, posture, movement (PPM) algorithm 331
Posterior forward–lean forward (PF-LF) posture 331
Potentially interacting domain (PID) matrix score 111, 115t
Power-law index 42
PPI  See Protein-protein interaction (PPI)
Predicting Protein-Protein Interaction (PrePPI) 111, 115t
PreSPI 112, 115t
Principal components analysis (PCA) 79–82, 82t
definition 79–81
loadings 82, 83t, 84, 85f
profile 84, 84t
scores 84, 85f, 86, 88f
PRISM 110–111, 115t
Probabilistic Graphical Model (PGM) 469
Probability density function (PDF) 457–460, 462–464, 463f
Protein Data Bank (PDB) ID 442–444
Protein-protein interaction (PPI) 
ML sequence-based PPI 
AAC 107–108, 110t
AA triads 108, 110t
AC 106, 110t
ETB-Viterbi 109, 110t
pairwise similarity 106–107, 110t
SVM 106
UNISPPI 109, 110t
ML structure-based approaches 
Random Forest 113–114, 115t
Struct2Net 114, 115t
performance measures 
accuracy (Ac) 101–102
AUC 102
F-measure (F1) 102
precision (P) 101–102
ROC curve 102
sensitivity 101–102
specificity (Sp) 101–102
sequence-based approaches 103
statistical sequence-based PPI 
CD method 105, 105t
mirror tree method 103, 105t
PIPE 104, 105t
statistical structure-based approaches 
DCC 112, 115t
MEGADOCK 112–113, 115t
meta approach 113, 115t
PID matrix score 111, 115t
PreSPI 112, 115t
technical challenges 100–101
template structure-based approaches 110–111
Protein-protein Interaction Prediction Engine (PIPE) 104, 105t
Proteins 
definition 99
primary structure 99
secondary structure 99
Proteomics mass spectrometry (MS) data 
background signals 503
biomarker candidates identification 512–513, 514f
biomarker discovery process 503
components 504
coupled computational workflow 
adjustment module 507, 507f
alignment 510–511, 511f
baseline subtraction 507–508, 508f
binning module 506, 506f
KD3 505
normalization effects 508, 509f
peak detection 509
preprocessing and evaluation steps 505, 505f
quality assessment 509–510
recalibration process 511
spectra format 511
spectra generation 511
features 503–504
KDD process/KD3 
composition 513, 515f
functional object 515f, 516
workflow 515f, 516
MALDI 504–505
Protonation states 488–490
Pseudonode 53–55
Pulsatile flow 
boundary conditions 42
experimental results 45–47, 45–47f
nondimensional variables 41–42
Power-law index 42
radial velocity 41–42
volumetric flow rate 42
wall profile, geometry 41, 41f

Q

QSAR 93
Quadrature mirror filter 542

R

Radial tree (RT), NCA 79, 81f, 84, 87f
Random Forest 113–114, 115t
Reading frames (RFs)  See Open Reading Frame (ORF)
Readmapping and indel calling software 
average precision and recall 529–531, 530f
BWT 522–523
Dindel model 524
F1-score 528–529, 528f
1000 genomes real data 531–532, 533f
hash table maps 522
read length effect 531, 532f
real data 527–528
Sanger data 
accuracy of 531, 532f, 533t
run-time performance 531, 533t
simulated data 526
Smith-Waterman algorithm 523
software workflow 
BAM conversion 526
BFAST 525
Bowtie2 524–525
BWA 524–525
Dindel model 526
FreeBayes 526
SHRiMP 524–525
SNVer 526
SystemG nodes 525
Receiver operating characteristic (ROC) curve 102, 512–513
Region growing 
C4.5 303t, 304
and cluster 298
definition 295
Euclidean distance 296
independent and dependent variables 297–298
leave-one-out (LOO) cross-validation 303t, 304
methodology 
neighborly SOM 299–301, 303, 303t
region-based prediction methodology 301–302
neighborhood concept 296, 297f
non-pictorial data 295
pictorial data 297–298
redundant attributes 303
SAR data set 302
traditional SOM 304t
Relaxed variable kernel density estimator (RVKDE) 108
Ressource Parisienne en Bioinformatique Structurale (RPBS) 442
Root-mean-square deviation (RMSD) 491–492
RVKDE  See Relaxed variable kernel density estimator (RVKDE)

S

Saccharomyces cerevisiae 104
Sampling importance resampling (SIR) 20, 23
Sanger data 
accuracy of 531, 532f, 533t
run-time performance 531, 533t
SBML model  See Systems Biology Markup Language (SBML) model
SCFG  See Stochastic context-free grammar (SCFG)
Secondary-structure elements (SSEs) 437–438
Self-consistent reaction field (SCRF) 174
Sequential importance sampling (SIS) PF 23
Shape ratio 509
SHRiMP 524–525
Signal-to-noise ratio (SNR) 509, 550, 550t
Singular value decomposition (SVD) analysis 538, 541f
continuous wavelet transform 543, 544f
denoised MRS signal, ICWT 543–544, 544f
k-means clustering 544, 544f
Monte Carlo analysis 554–555, 557t
MRS signal and noise component, separation of 543
orthogonal and diagonal matrix 543
quantification errors 554–555, 556–557f
quantification results of 
amplitude parameters 552–554, 553t
damping factor parameters 552–554, 554t
signal representation 543–544
singular values 543–544
SIR  See Sampling importance resampling (SIR)
SMIR 439
dynamic results 444, 446f
graphical representation 443–444, 445f
interface 443–444, 443f
PDB ID 442–444
submission status 443–444, 444f
Smith-Waterman (SW) score 107
SNVer 526
Sparrow system 
algorithm 331
characteristics 328–329
fall tracking and fall reduction outcomes 332–333
imaging sensors 329
medical professional 333
multifacility outcome tracking 330, 330f
multimodal strategy 329
Sparrow prototype P-II 329, 329f
system installation and setup 330
third-generation Sparrow prototype (P-III) 330
Spinosyn A 
biosynthesis 170, 170s
charge transfer (CT) 171
genes 170–171
transition state models 171, 171f
Splits graph (SG), NCA 79, 82f, 86, 88f
SplitsTree program 79
Stimulus-termination asynchrony (STA) 338
Stochastic context-free grammar (SCFG) 430
Stochastic resonance (SR) 
brain activity 356
concept 357f
Stress transfer path 159, 160f, 164
Struct2Net 114, 115t
Structure–property relationships (SPRs) 79–81
Super Video Graphics Array (SVGA) 336
Supervised learning  See Artificial neural networks (ANN) model
Support vector machines (SVMs) 12–13, 13t, 15, 106
SystemG nodes 525
Systems Biology Markup Language (SBML) model 1, 6, 7f

T

Tapered artery 
flow characteristics 40
laminar and pulsatile flow 
boundary conditions 42
nondimensional variables 41–42
Power-law index 42
radial velocity 41–42
volumetric flow rate 42
wall profile, geometry 41, 41f
variables 
experimental results 45–47, 45–47f
flow resistance 44–45
Reynolds number 40–42
steady flow rate 44–45
stream function 43
velocity 43
wall shear stress 43–44
Womersley number 44
Tightened end fragments (TEFs) 437–438
Time-of-flight (TOF) 504–505
Toll-like receptors (TLRs) 123–124, 125f, 126t
Total turing test (TTT) 381
Transactivation domain (TAD) 413

U

Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI) 109, 110t

V

Virtual screening  See High-throughput virtual screening (HTVS)
Vocoder only (VOC) 320
Vocoder only situation (VOC) 317

W

Wavelet denoising 541f
coarsest scale 541–542
detail coefficients, soft thresholding 542
discrete wavelet transform 538
filters 542
MATLAB implementation 542–543
Monte Carlo analysis 554–555, 557t
mother wavelet 541–542
MRS signal 
one-level decomposition 542
reconstruction, 1D IDWT 542
multiresolution analysis 542
quantification errors 554–555, 556–557f
quantification results of 
amplitude parameters 551, 552t
damping factor parameters 551, 553t
removes noisy components 540
scaling and wavelet coefficients 541–542
and SVD signal separation, combination of 
amplitudes and damping factors, values of 554, 555t
Monte Carlo analysis 554–555, 557t
performance of 558
quantification errors 554–555, 556–557f
SNR, denoising method 550, 550t
translation and dilation parameters 541–542
Wheelchair occupant  See Elderly fall prevention
Wild types (WTs) proteins 445–446, 447t, 448–449, 449t, 450f
Within-cluster sum of squares (WCSS) 53

Z

Ziggurat acceleration technique 
hardware submodules 31, 32–33f
partitioning 29f, 28
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