Raport de cercetare - Lorentz JÄNTSCHI

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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
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Raport de cercetare - lucrare în e
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2. Scop şi obiective Scopul proiec
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abordare integrată în căutarea r
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• cu acces parţial restricţiona
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2007A1. Planificarea activităţilo
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2 Y = 0.852 -1 =a+bX -1 (Y -1 -a-bX
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PCB128 136.38 -3.4116 0.7761 PCB129
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ISDmsHt lADrtHg Y Equation Residue
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Page 19 and 20:
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Tabelul următor prezintă sintetic
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d_RSD INF 2.6057 d_Volum Matricea p
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30 1.02 0.63 1.75 2.67 160 31 1.14
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Matricea valorilor coeficientului d
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evaluare sistematică pentru identi
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introductivă în subiectul prezent
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Studio (al companiei Accelrys) a pe
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Setting the weights equal to 1 give
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÷ Lucrare: Quantitative structure-
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compound ranking in the database. A
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vectors based on the cloning strate
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"Recent Advances in Synthesis and C
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will be highly inactive, moderately
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Publication Frequency: 8 issues per
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of strand breaks and when they do o
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ibonucleotide reductase Journal of
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scenarios are to be compared, e.g.
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hydrazones • ligand-based drug de
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modalitatea de culegere, colectare
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÷ Transversală: studierea unui e
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9 10 12 8 1 4 3 2 2 15 8 5 4 3 4 8
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2 1 1 2 3 2 0 1 4 3 2 2 5 4 2 0 6 5
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10 1 0 1 0 1 1 1 1 0 1 0 11 0 0 0 1
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12 0 0 0 1 0 0 L12 Factori (nivele)
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3 1 ×2 4 A(3) B(2) C(2) D(2) E(2)
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9 8 0 2 2 2 1 1 10 9 1 2 1 1 0 0 11
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7 1 4 1 1 2 4 8 2 2 3 4 1 1 9 3 2 1
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1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0
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11 10 9 9 4 11 12 11 8 10 3 13 13 1
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÷ Endorelaţie binară (`2-e-r`):
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muchiei e); ponderile pentru vârfu
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ale grafului; între numărul de fe
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Detur d5,1=3; d5,2=3; d5,3=2; d5,4=
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3 1 024220000 1145 4 0 022100222 11
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o ΣA(·) 2 : 231; o Matricea: De 1
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(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($query." :ERROR!\r\
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} unset($a); $query="INSERT INTO `"
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$ok=$this->check_finite();if(!$ok)r
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$q=mysql_query($query_prop."'".$mdf
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2005-RIMC; Jäntschi & Bolboacă, 2
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0.918 0.9 5 14 23110_ 69 332064 837
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0.9665 0.9525 4 30 33504 73 r = 0.9
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20 41521_ lNMrEQg*0.336843860556055
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54 DevMTOp25_ y=1.893045064859439+
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82 19654_ lIDRFMg*-1.31755354516111
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y=3.463929176330566+ ABDmtQg*-13.01
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iBMmwHg*1195.781250000000000+ iFPME
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161 15aacidsHyd_ lSDmwMt*6.37250438
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Relaţii semicantitative structură
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MDFV (Jäntschi şi Bolboacă, 2008
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SAPF (©2009) CF:3 DO:2 AP:5 DP:6 P
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N0) fenotipurilor între generaţii
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adevăr (T/F) pentru fiecare operat
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S5 String[255] Şir de maxim 255 ca
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end; with(MDF)do repeat CF_Rs;SA_Fr
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sfs_FITNESS_strategy= proportional/
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parametrul statistic descriptiv mx
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d_n:S9; Variabilă folosită la pen
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(s:S9):B0T; configurare a execuţie
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; ÷ mută părinţi cu probabilita
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procedure SL_QSr (i,j:I0T); procedu
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XX_min = XX_max = XX_avg = XX (XX =
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$p_max[$i]=1.0*$m-1.0*$m*$tdist->_g
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$d[$i][$k++]=$c[$i][$j]; for($i=0;$
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for($i=0;$i
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mat_means($n,$m,$y,$x,$ma,$mb); red
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} if(strlen($s)>0) $t[]=$s; return
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este necesar ca să se asume ipotez
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6(( b − a + 1) Asimetria; excesul
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Minim; Maxim 0; ∞ Funcţia de pro
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Varianţa Var γ1 (nX-1)σ 2 /nX 2
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Σ1≤i≤m(Ŷi-Yi) 2 → min. (3)
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Tabelul 24. Valorile optimizate ale
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eroare m(n-1) 2 m ⎛ n ⎛ n ⎞
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http://l.academicdirect.ro/Chemistr
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73 la convergenţă folosind un alg
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Cl(n) Cl (n) PCBs: Seria bifenililo
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PCB027 Cl Cl Cl 5.447 PCB028 Cl Cl
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PCB071 Cl Cl Cl Cl 5.987 PCB072 Cl
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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):
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Environment, Applied Medical Inform
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AcademicDirect, ISSN 1583-1078, www
Page 313 and 314:
Observable: Maximum Likelihood Esti
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and Veterinary Medicine Cluj-Napoca
Page 317 and 318:
Relative Response Factor using Mole
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