Raport de cercetare - Lorentz JÄNTSCHI

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[25]. In this situation, three alternative linear regression methods may be considered, these are a) Ridge Regression(RR), b) Principal Component Regression(PCR) and c) Partial Least Squares(PLS). These three methods are also very useful even when the independent variables are highly correlated. In the ridge regression method, descriptors are transformed into principal components (PCs). All of the principal components are used in the regression, but they are first shrunk differentially according to their eigenvalues and a ridging constant. In the principal components regression, the descriptors are transformed into principal components after which a subset of the PCs is used in an ordinary least square regression. Partial least squares also uses a set of linear combinations of the descriptors but, in this approach, the dependent variable is also considered in this step. Each of these methods makes use of the entire available pool of independent variables as opposed to selecting a subset, which introduces bias and may result in the elimination of important parameters from the study. Formal comparisons have consistently shown subsetting to be less effective than alternative methods, such as these, that retain all of the independent variables and use other approaches to deal with the rank deficiency [26]. Statistical theory suggests that RR is the best of the three methods and this has been generally borne out in multiple comparative studies [26–28]. As such, the RR models developed in the current study are analyzed in more detail than the PCR and PLS models. The RR vector of regression coefficients, b, is given by b = (X T X+kI) - 1 X T Y, where X is the matrix of descriptors, Y is the vector of observed activities, I is an identity matrix, and k is a nonnegative constant known as the “ridge” constant. ÷ Lucrare: Multivariate analysis of experimental and computational descriptors of molecular lipophilicity ÷ Autori: Raimund Mannholda, Gabriele Crucianib, Karl Drossc, Roelof Rekkerd ÷ Sursa: Journal of Computer-Aided Molecular Design, Volume 12, Number 6, 1998, p. 573- 581(9). ÷ Rezumat: Two experimental (log P, RMw) and 17 calculation descriptors for molecular lipophilicity (fragmental, atom-based for based on molecular properties) were investigated by multivariate analysis for a database of 159 compounds including both simple structures as well as more complex drug molecules. Principal component analysis (PCA) of the entire database exhibits a clustering of chemical groups; preciseness of clustering corresponds to chemical similarity. Thus, diversity searching in databases might effectively be performed by PCA on the basis of calculatedlog P. The comparative validity check of experimental and computational procedures by regression analysis and PCA was performed with a chemically balanced, reduced data set (n D 55) representing 11 chemical groups with 5 members each. Regression of experimental descriptors (log Poct versus RMw) proves that chromatographic data, obtained under well-defined experimental conditions, can be used as valid substitutes for log P. Regression of calculated versus experimental lipophilicity data shows a superiority of fragmental over atom-based methods and approaches based on molecular properties, as indicated by correlation coefficients, slopes and intercepts. Inaddition, PCA revealed that fragmental methods (Rekker-type, KOWWIN, KLOGP) sense the compound ranking in log P data to almost the same extent as experimental approaches. For atom-based procedures and CLOGP, both the comparability of absolute values and the sensing of the compound ranking in the database are slightly less. This trend is more pronounced for the methods based on molecular properties, with the exception of BLOGP. ÷ Detalii de interes: Validity check of calculation methods by regression analysis and PCA. Regression analysis of calculated versus experimental data shows that in general fragmental methods are superior to atom-based and 3D-related approaches. These results are in accord with our earlier analysis with a smaller dataset [35]. A limited applicability is often attributed to fragmental methods due to missing fragment values. This is true for CLOGP and Rekker-type methods, but not for KOWWIN. Information obtained by PCA on the same dataset, in general parallels the regression data, but unravels more precisely the comparability in absolute values and the reflection of 38
compound ranking in the database. Accordingly, the ranking of calculation methods observed in regression analysis is confirmed, exceptional behaviour of CLOGP and BLOGP, however, is only detected by PCA. 2007A4. Participări la manifestări ştiinţifice (diseminare - conferinţe, congrese, workshop-uri) şi dobândirea de competenţe complementare (stagii de documentare/cercetare în străinătate) În a doua fază a dobândirii de competenţe complementare prin participări la manifestări ştiinţifice / stagii de documentare-cercetare s-a planificat pentru perioada 6-14 decembrie participarea la următoarele: ÷ University of Oxford, Computational Biology Reseach Group, cursul de instruire intitulat "Introduction to Bioinformatics at CBRG", 7 decembrie 2007 ÷ Dublin Molecular Medicine Centre, cursul de instruire intitulat "DMMC Course: Techniques and Strategies in Molecular Medicine", 10-13 decembrie 2007 ÷ Trinity College Dublin, Centre for Synthesis & Chemical Biology, simpozionul intitulat "Recent Advances in Synthesis and Chemical Biology VI", 14 decembrie 2007 Programul evenimentelor planificate este redat în tabelul de mai jos: "Introduction to Bioinformatics at CBRG", 7 decembrie 2007 This course is intended for new users and/or anyone not familiar with using their molbiol account for bioinformatics analysis. It will be held in the Medical Sciences Teaching Centre (behind the Dunn School of Pathology on South Parks Road). The day-long course is designed to be run in two sessions: The first session will introduce you to the many bioinformatics analysis tools that are now available on this web site. The course will also introduce you to EMBOSS Explorer - a suite of bioinformatics software intended largely as a replacement for GCG. You will be shown how to carry out some basic bioinformatics analyses - for example: access the databases on our servers to retrieve sequence files examine sequence file formats run restriction analysis software carry out sequence alignments and produce publishable images of the aligned sequences search databases using BLAST It will also introduce you to other more specialised tools - BASE and MASCOT - that will allow you to analyse your microarray and proteomics data respectively. Second session: Some of the bioinformatics tools are only available on our Unix servers and the second session will introduce the Unix computing facilities available via your molbiol account. We will show you how to connect to the CBRG Unix machines and introduce you to the Unix environment in general. You will be introduced to some of the features of the programs from the morning session that are not available via the web. There will be a brief introduction to the Staden package of software used for the analysis of dna sequencing chromatograms. The course is not intended to be a comprehensive guide to all the bioinformatics packages available at the CBRG. Instead it is designed to show you the kind of analysis software that is available to you via your account. It should ensure that you also know where to look to find similar software on the CBRG web site and on orac and to find help with any of the software on the system. "DMMC Course: Techniques and Strategies in Molecular Medicine", 10-13 decembrie 2007 This course, running over four mornings, is designed to give bioscientists and clinicians a broad overview of research techniques and their application. Basic molecular biology laboratory experience is assumed, but you should not need prior knowledge of the techniques covered in the course. This course, running over four mornings, is designed to give bioscientists and clinicians a broad 39
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 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: ÷ Lucrare: Quantitative structure-
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
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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
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Post-processing: Visual Inspection
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PCB013 146.55 -2.7348 0.3315 0.3241
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PCB113 136.08 -3.2367 0.5862 0.5861
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Regression 1 6.730776811 6.730777 7
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Metoda Formula intervalului de înc
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1.2 1 0.8 0.6 0.4 0.2 Metoda ArcS 0
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Metoda AvADA(0) AvADA(1) AvADS(0) A
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12 11.01 9.99 10 8 6 4 2 0 6 5 4 3
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Planificarea activităţilor experi
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o Generalized functions; o Operator
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approaches of QSAR modeling o Sessi
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Bookhaven pe care o transferă prog
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1.21739507388622E+000 y= 7.38149868
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20 020_3613389 7.783 -1.627 5.661 0
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} function atom_info($atom,&$cnv_to
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}else{ } } $this->tmp_dist=array();
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} } return "({".implode(",",$this->
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if($this->p12_2) $this->f[57] =$thi
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$fr_f_t=pow(pow($fr_f_x,2)+pow($fr_
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} } $this->f[$a]=$ret; } Interacţi
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÷ 2 tipuri de selecţii ale valori
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} $ret[$j][$k]=array(0.0,0.0,0.0,0.
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if(!$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
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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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