Raport de cercetare - Lorentz JÄNTSCHI

More documents

Recommendations

Info

parameters that relate to the phenomenon o Exploring novel estrogen receptors and more... MLI in Numbers NM MLSC (3-year summary) U54MH074425 Summary Assays NMMLSC Integrated Discovery Teams HyperCyt Integration of VS & HTS GPR30 - A Novel Estrogen Target for MLI 2D Similarity Shape Similarity - ROCS Electrostatic Similarity - EON In Silico Screening: Bet on all horses… Summary of Estrogen Receptors Results Biomolecular Screening Results Virtual and Biomolecular Screening Workflow Probe Discovery At NMMLSC How Many Drug Targets? WOMBAT-PK Measured Data in WOMBAT-PK Drug Targets & Dis-ease The Fitness Landscape Multi-Target Binding Affinity Cliffs Mono-Target Drugs Current Drugs Classification Unique Drug Targets by Class Urban Legend… Drug Targets Revisited From DrugBank What are the relevant drug metabolizing enzymes? The Problem with unique lists… More realistic? Overview of drug metabolism o Tutorial: Impact of dataset composition on models performance Software • R • Weka Datasets Model building • Descriptors • Data files • Obtaining and validation of the models • Extracting the training and the external related test set from the Cherkasov dataset • Build and analyze a Bayesian model • External validations on unrelated test set Detection of the bias: profiling of data sets o Molecular descriptors: an overview The chemical data Molecular structure Some historical notes Molecular descriptors intro • The role of the molecular descriptors 144
• Representations of a molecular structure • Properties of a molecular descriptor Molecular descriptors detail • Molecular graph • Topological matrices • Local vertex invariants • Distance matrix • Strategies for molecular descriptors o Următoarele 3 figuri • QSAR strategy o models ... regression models (quantitative response) classification models (qualitative response) ranking models (ordered response) 145
Page 1 and 2:
Raport de cercetare - lucrare în e
Page 3 and 4:
2. Scop şi obiective Scopul proiec
Page 5 and 6:
abordare integrată în căutarea r
Page 7 and 8:
• cu acces parţial restricţiona
Page 9 and 10:
2007A1. Planificarea activităţilo
Page 11 and 12:
2 Y = 0.852 -1 =a+bX -1 (Y -1 -a-bX
Page 13 and 14:
PCB128 136.38 -3.4116 0.7761 PCB129
Page 15 and 16:
ISDmsHt lADrtHg Y Equation Residue
Page 17 and 18:
Page 19 and 20:
Page 21 and 22:
Tabelul următor prezintă sintetic
Page 23 and 24:
d_RSD INF 2.6057 d_Volum Matricea p
Page 25 and 26:
30 1.02 0.63 1.75 2.67 160 31 1.14
Page 27 and 28:
Matricea valorilor coeficientului d
Page 29 and 30:
evaluare sistematică pentru identi
Page 31 and 32:
introductivă în subiectul prezent
Page 33 and 34:
Studio (al companiei Accelrys) a pe
Page 35 and 36:
Setting the weights equal to 1 give
Page 37 and 38:
÷ Lucrare: Quantitative structure-
Page 39 and 40:
compound ranking in the database. A
Page 41 and 42:
vectors based on the cloning strate
Page 43 and 44:
"Recent Advances in Synthesis and C
Page 45 and 46:
will be highly inactive, moderately
Page 47 and 48:
Publication Frequency: 8 issues per
Page 49 and 50:
of strand breaks and when they do o
Page 51 and 52:
ibonucleotide reductase Journal of
Page 53 and 54:
scenarios are to be compared, e.g.
Page 55 and 56:
hydrazones • ligand-based drug de
Page 57 and 58:
modalitatea de culegere, colectare
Page 59 and 60:
÷ Transversală: studierea unui e
Page 61 and 62:
9 10 12 8 1 4 3 2 2 15 8 5 4 3 4 8
Page 63 and 64:
2 1 1 2 3 2 0 1 4 3 2 2 5 4 2 0 6 5
Page 65 and 66:
10 1 0 1 0 1 1 1 1 0 1 0 11 0 0 0 1
Page 67 and 68:
12 0 0 0 1 0 0 L12 Factori (nivele)
Page 69 and 70:
3 1 ×2 4 A(3) B(2) C(2) D(2) E(2)
Page 71 and 72:
9 8 0 2 2 2 1 1 10 9 1 2 1 1 0 0 11
Page 73 and 74:
7 1 4 1 1 2 4 8 2 2 3 4 1 1 9 3 2 1
Page 75 and 76:
1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0
Page 77 and 78:
11 10 9 9 4 11 12 11 8 10 3 13 13 1
Page 79 and 80:
÷ Endorelaţie binară (`2-e-r`):
Page 81 and 82:
muchiei e); ponderile pentru vârfu
Page 83 and 84:
ale grafului; între numărul de fe
Page 85 and 86:
Detur d5,1=3; d5,2=3; d5,3=2; d5,4=
Page 87 and 88:
3 1 024220000 1145 4 0 022100222 11
Page 89 and 90:
o ΣA(·) 2 : 231; o Matricea: De 1
Page 91 and 92:
(10, 3) [10, 6, 3] {7, 10, 11} (3,
Page 93 and 94: 11 0.250 0.333 0.333 0.333 0.250 0.
Page 95 and 96: (5, 11) [5, 1, 2, 3, 6, 9, 8, 7, 11
Page 97 and 98: (1, 11) [1, 2, 3, 6, 10, 11] {1, 4,
Page 99 and 100: (9, 11) [9, 8, 7, 11] {1, 2, 3, 4,
Page 101 and 102: (3, 8) [3, 6, 10, 11, 7, 8] {1, 2,
Page 103 and 104: 6 0.167 0.167 0.167 0.167 0.167 0.0
Page 105 and 106: 5 0.500 0.500 0.500 0.500 0.000 0.3
Page 107 and 108: Tabelul 10. Matricea caracteristic
Page 109 and 110: 10 6 7 8 9 10 11 11 6 7 8 9 10 11 6
Page 111 and 112: Astfel, adaptând principiul I al t
Page 113 and 114: Din formula sa de definiţie dS = d
Page 115 and 116: produsul final al întregului lanţ
Page 117 and 118: Tabelul 16. Cât de dulci sunt zaha
Page 119 and 120: ÷ CSLS; ÷ Cyberlipid Center; ÷ D
Page 121 and 122: ÷ STING Millenium Suite; ÷ wwPDB;
Page 123 and 124: pentru care algoritmul performează
Page 125 and 126: scopul maximizării randamentului d
Page 127 and 128: solului, calitatea vremii, manageme
Page 129 and 130: Fragmentation criteria Fragment of
Page 131 and 132: o Se încrucişează Genotip1 cu Ge
Page 133 and 134: Measures of Disease in Health Resea
Page 135 and 136: Generarea întâmplătoare stratifi
Page 137 and 138: Functional Networks Noelia Sánchez
Page 139 and 140: o SVMs application to protein secon
Page 141 and 142: Meta-learning for Algorithm Recomme
Page 143: Similarity Assessment with PDR-FP
Page 147 and 148: Introduction - Non HTS Hit Recognit
Page 149 and 150: Post-processing: Visual Inspection
Page 151 and 152: PCB013 146.55 -2.7348 0.3315 0.3241
Page 153 and 154: PCB113 136.08 -3.2367 0.5862 0.5861
Page 155 and 156: Regression 1 6.730776811 6.730777 7
Page 157 and 158: Metoda Formula intervalului de înc
Page 159 and 160: 1.2 1 0.8 0.6 0.4 0.2 Metoda ArcS 0
Page 161 and 162: Metoda AvADA(0) AvADA(1) AvADS(0) A
Page 163 and 164: 12 11.01 9.99 10 8 6 4 2 0 6 5 4 3
Page 165 and 166: Planificarea activităţilor experi
Page 167 and 168: o Generalized functions; o Operator
Page 169 and 170: approaches of QSAR modeling o Sessi
Page 171 and 172: Bookhaven pe care o transferă prog
Page 173 and 174: 1.21739507388622E+000 y= 7.38149868
Page 175 and 176: 20 020_3613389 7.783 -1.627 5.661 0
Page 177 and 178: } function atom_info($atom,&$cnv_to
Page 179 and 180: }else{ } } $this->tmp_dist=array();
Page 181 and 182: } } return "({".implode(",",$this->
Page 183 and 184: if($this->p12_2) $this->f[57] =$thi
Page 185 and 186: $fr_f_t=pow(pow($fr_f_x,2)+pow($fr_
Page 187 and 188: } } $this->f[$a]=$ret; } Interacţi
Page 189 and 190: ÷ 2 tipuri de selecţii ale valori
Page 191 and 192: } $ret[$j][$k]=array(0.0,0.0,0.0,0.
Page 193 and 194: if(!$q){ } echo($query." :ERROR!\r\
Page 195 and 196:
} unset($a); $query="INSERT INTO `"
Page 197 and 198:
$ok=$this->check_finite();if(!$ok)r
Page 199 and 200:
$q=mysql_query($query_prop."'".$mdf
Page 201 and 202:
2005-RIMC; Jäntschi & Bolboacă, 2
Page 203 and 204:
0.918 0.9 5 14 23110_ 69 332064 837
Page 205 and 206:
0.9665 0.9525 4 30 33504 73 r = 0.9
Page 207 and 208:
20 41521_ lNMrEQg*0.336843860556055
Page 209 and 210:
54 DevMTOp25_ y=1.893045064859439+
Page 211 and 212:
82 19654_ lIDRFMg*-1.31755354516111
Page 213 and 214:
y=3.463929176330566+ ABDmtQg*-13.01
Page 215 and 216:
iBMmwHg*1195.781250000000000+ iFPME
Page 217 and 218:
161 15aacidsHyd_ lSDmwMt*6.37250438
Page 219 and 220:
Relaţii semicantitative structură
Page 221 and 222:
MDFV (Jäntschi şi Bolboacă, 2008
Page 223 and 224:
SAPF (©2009) CF:3 DO:2 AP:5 DP:6 P
Page 225 and 226:
N0) fenotipurilor între generaţii
Page 227 and 228:
adevăr (T/F) pentru fiecare operat
Page 229 and 230:
S5 String[255] Şir de maxim 255 ca
Page 231 and 232:
end; with(MDF)do repeat CF_Rs;SA_Fr
Page 233 and 234:
sfs_FITNESS_strategy= proportional/
Page 235 and 236:
parametrul statistic descriptiv mx
Page 237 and 238:
d_n:S9; Variabilă folosită la pen
Page 239 and 240:
(s:S9):B0T; configurare a execuţie
Page 241 and 242:
; ÷ mută părinţi cu probabilita
Page 243 and 244:
procedure SL_QSr (i,j:I0T); procedu
Page 245 and 246:
XX_min = XX_max = XX_avg = XX (XX =
Page 247 and 248:
$p_max[$i]=1.0*$m-1.0*$m*$tdist->_g
Page 249 and 250:
$d[$i][$k++]=$c[$i][$j]; for($i=0;$
Page 251 and 252:
for($i=0;$i
Page 253 and 254:
mat_means($n,$m,$y,$x,$ma,$mb); red
Page 255 and 256:
} if(strlen($s)>0) $t[]=$s; return
Page 257 and 258:
este necesar ca să se asume ipotez
Page 259 and 260:
6(( b − a + 1) Asimetria; excesul
Page 261 and 262:
Minim; Maxim 0; ∞ Funcţia de pro
Page 263 and 264:
Varianţa Var γ1 (nX-1)σ 2 /nX 2
Page 265 and 266:
Σ1≤i≤m(Ŷi-Yi) 2 → min. (3)
Page 267 and 268:
Tabelul 24. Valorile optimizate ale
Page 269 and 270:
eroare m(n-1) 2 m ⎛ n ⎛ n ⎞
Page 271 and 272:
http://l.academicdirect.ro/Chemistr
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
73 la convergenţă folosind un alg
Page 279 and 280:
Cl(n) Cl (n) PCBs: Seria bifenililo
Page 281 and 282:
PCB027 Cl Cl Cl 5.447 PCB028 Cl Cl
Page 283 and 284:
PCB071 Cl Cl Cl Cl 5.987 PCB072 Cl
Page 285 and 286:
PCB110 Cl Cl Cl Cl Cl 6.532 PCB111
Page 287 and 288:
PCB151 Cl Cl Cl Cl Cl Cl 6.647 PCB1
Page 289 and 290:
PCB191 Cl Cl Cl Cl Cl Cl Cl 7.557 P
Page 291 and 292:
O altă remarcă se poate face cu p
Page 293 and 294:
al eşantionului pe care o induc me
Page 295 and 296:
PmkEt 1 26 26 sDDJEg 1 26 26 MDDKHt
Page 297 and 298:
86 Abateri semnificative statistic
Page 299 and 300:
Generaţii ce produc evoluţii (num
Page 301 and 302:
Rar (D, P) (D, T) Figura 2. Cât de
Page 303 and 304:
2010A5. Construirea bazei de date c
Page 305 and 306:
Portalul "MDF" Portalul "Statistics
Page 307 and 308:
(Bolboacă & Jäntschi, 2007-AAHS):
Page 309 and 310:
Environment, Applied Medical Inform
Page 311 and 312:
AcademicDirect, ISSN 1583-1078, www
Page 313 and 314:
Observable: Maximum Likelihood Esti
Page 315 and 316:
and Veterinary Medicine Cluj-Napoca
Page 317 and 318:
Relative Response Factor using Mole
show all

Raport de cercetare - Lorentz JÄNTSCHI

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?