Raport de cercetare - Lorentz JÄNTSCHI

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2005-RIMC; Jäntschi & Bolboacă, 2006-OCPG) au constituit filtre în colectarea informaţiei medicale de activitate terapeutică. În cadrul activităţii de colectare a informaţiei medicale o serie de aspecte cu privire la confidenţa în observaţiile experimentale (Drugan & others, 2003-BCI1; Bolboacă & Jäntschi, 2005-CILR; Bolboacă & Jäntschi, 2007BCIO) au constituit subiect de studiu având ca rezultat elaborarea unei serii de studii în acest subiect (Bolboacă & Jäntschi, 2008-ABCI; Bolboacă & Jäntschi, 2008-OCIB; Jäntschi & Bolboacă, 2010-EPCI). S-au căutat seturi de molecule cu potentă activitate terapeutică; s-au construit modele pentru structurile moleculelor seturilor folosind aplicaţia comercială HyperChem; s-au colectat valorile activităţilor experimentale observate. Denumirile în limba engleză ale activităţilor terapeutice colectate sunt: LC50/EC50 & NOEC & LOEC - fertilization of sea urchin & embryological development of sea urchin & germination of sea urchin & zoospore germination of green macroalgae & germling length of green macroalgae & germling cell number of green macroalgae & survival and reproductive success of polychaete & redfish larvae survival & juveniles survival of opossum shrimp (LC50 = lethal concentration to 50% of the test organisms; EC50 = effective concentration to 50% of the test organisms; NOEC = no observed effect concentration; LOEC = lowest observed effect concentration), inhibition activity, mutagenicity, antiallergic activity, anti-HIV-1 potencies, antituberculotic activity, growth inhibition activity, insecticidal activity, antioxidant efficacy, inhibition activity on carbonic anhydrase I & II & IV, herbicidal activity. Seria de lucrări selectate este după cum urmează: Nr Set n Total Clear 2var R2_old Ref R2_pred R2_est v Ref 1 2 1 23151_ 16 289206 93362 0.985 (4, n = 13) [ ] 0.997 0.995 3 [ ] 0.741 (4, n = 16) 2 23158 40 324181 99125 0.8 (5) [ ] 0.951 0.945 2 3 36638_ 16 335657 105319 0.967 (?) 4 [ ] 0.994 0.991 5 3 [ ] 4 41521_ 8 350233 86407 0.985 (5) 0.913 (3) 6 [ ] 0.999 0.998 7 2 [ ] 5 IChr10_ 10 324388 103237 0.9 (2) 8 [ ] 0.999 0.999 9 2 [ ] 1 Vijay K. Agrawal, Ravindra Srivastavaa and Padmakar V. Khadikarb, QSAR Studies on Some Antimalarial Sulfonamides, Bioorganic & Medicinal Chemistry, 2001, 9, p. 3287–3293. 2 Lorentz JÄNTSCHI and Sorana BOLBOACA, Molecular Descriptors Family on Structure Activity Relationships 5. Antimalarial Activity of 2,4-Diamino-6-Quinazoline Sulfonamide Derivates, Leonardo Journal of Sciences, 2006, Issue 8, p. 77-88. 3 Agrawala V.K., Khadikarb P.V., QSAR Prediction of Toxicity of Nitrobenzenes, Bioorganic & Medicinal Chemistry, 2001, 9, 3035–3040. 4 Brasquet C., Le Cloirec P., QSAR for Organics Adsorption Onto Activated Carbon In Water: What About The Use Of Neural Networks?, Wat. Res., 1999, 33(17), p. 3603-3608. 5 Jäntschi L., Water Activated Carbon Organics Adsorption Structure - Property Relationships, Leonardo Journal of Sciences, AcademicDirect, 2004, Issue 5, p. 63-73. 6 Hasegawa K., Arakawa M., Funatsu K., 3D-QSAR study of insecticidal neonicotinoid compounds based on 3-way partial least squares model, Chemometrics and Intelligent Laboratory Systems, 1999, 47, p. 33–40. 7 Bolboacă S., Jäntschi ., Molecular Descriptors Family on Structure Activity Relationships 2. Insecticidal Activity of Neonicotinoid Compounds, Leonardo Journal of Sciences, AcademicDirect, 2005, Issue 6, 78-85. 8 Jäntschi L., Muresan S., Diudea M., Modeling Molecular Refraction and Chromatographic Retention by Szeged Indices, Studia Universitatis Babes-Bolyai, Chemia, 2000, XLV(1-2), p. 313- 318.. 9 . Jäntschi L., MDF - A New QSAR/QSPR Molecular Descriptors Family, Leonardo Journal of Sciences, AcademicDirect, 2004, Issue 4, p. 67-84. 201 3
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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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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,
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11 0.250 0.333 0.333 0.333 0.250 0.
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(5, 11) [5, 1, 2, 3, 6, 9, 8, 7, 11
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(1, 11) [1, 2, 3, 6, 10, 11] {1, 4,
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(9, 11) [9, 8, 7, 11] {1, 2, 3, 4,
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(3, 8) [3, 6, 10, 11, 7, 8] {1, 2,
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6 0.167 0.167 0.167 0.167 0.167 0.0
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5 0.500 0.500 0.500 0.500 0.000 0.3
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Tabelul 10. Matricea caracteristic
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10 6 7 8 9 10 11 11 6 7 8 9 10 11 6
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Astfel, adaptând principiul I al t
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Din formula sa de definiţie dS = d
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produsul final al întregului lanţ
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Tabelul 16. Cât de dulci sunt zaha
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÷ CSLS; ÷ Cyberlipid Center; ÷ D
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÷ STING Millenium Suite; ÷ wwPDB;
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pentru care algoritmul performează
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scopul maximizării randamentului d
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solului, calitatea vremii, manageme
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Fragmentation criteria Fragment of
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o Se încrucişează Genotip1 cu Ge
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Measures of Disease in Health Resea
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Generarea întâmplătoare stratifi
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Functional Networks Noelia Sánchez
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o SVMs application to protein secon
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Meta-learning for Algorithm Recomme
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Similarity Assessment with PDR-FP
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• Representations of a molecular
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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: $q=mysql_query($query_prop."'".$mdf
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($i=0;$
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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
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PCB151 Cl Cl Cl Cl Cl Cl 6.647 PCB1
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PCB191 Cl Cl Cl Cl Cl Cl Cl 7.557 P
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O altă remarcă se poate face cu p
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al eşantionului pe care o induc me
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PmkEt 1 26 26 sDDJEg 1 26 26 MDDKHt
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86 Abateri semnificative statistic
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Generaţii ce produc evoluţii (num
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Rar (D, P) (D, T) Figura 2. Cât de
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2010A5. Construirea bazei de date c
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Portalul "MDF" Portalul "Statistics
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(Bolboacă & Jäntschi, 2007-AAHS):
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Environment, Applied Medical Inform
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AcademicDirect, ISSN 1583-1078, www
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Observable: Maximum Likelihood Esti
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and Veterinary Medicine Cluj-Napoca
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Relative Response Factor using Mole
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Raport de cercetare - Lorentz JÄNTSCHI

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