39 results on '"Raghava, G.P.S."'
Search Results
2. Computer-aided biotechnology: from immuno-informatics to reverse vaccinology
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Vivona, Sandro, Gardy, Jennifer L., Ramachandran, Srinivasan, Brinkman, Fiona S.L., Raghava, G.P.S., Flower, Darren R., and Filippini, Francesco
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- 2008
- Full Text
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3. Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
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Sieberts, S.K., Schaff, J., Duda, M., Pataki, B.Á., Sun, M., Snyder, P., Daneault, J.F., Parisi, F., Costante, G., Rubin, U., Banda, P., Chae, Y., Chaibub Neto, E., Dorsey, E.R., Aydın, Z., Chen, A., Elo, L.L., Espino, C., Glaab, E., Goan, E., Golabchi, F.N., Görmez, Y., Jaakkola, M.K., Jonnagaddala, J., Klén, R., Li, D., McDaniel, C., Perrin, D., Perumal, T.M., Rad, N.M., Rainaldi, E., Sapienza, S., Schwab, P., Shokhirev, N., Venäläinen, M.S., Vergara-Diaz, G., Zhang, Y., Abrami, A., Adhikary, A., Agurto, C., Bhalla, S., Bilgin, H., Caggiano, V., Cheng, J., Deng, E., Gan, Q., Girsa, R., Han, Z., Heisig, S., Huang, K., Jahandideh, S., Kopp, W., Kurz, C.F., Lichtner, G., Norel, R., Raghava, G.P.S., Sethi, T., Shawen, N., Tripathi, V., Tsai, M., Wang, T., Wu, Y., Zhang, J., Zhang, X., Wang, Y., Guan, Y., Brunner, D., Bonato, P., Mangravite, L.M., Omberg, L., AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü, Aydin, Zafer, Fonds National de la Recherche - FnR [sponsor], and Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center]
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Movement disorders ,Parkinson's disease ,Biotechnologie [F06] [Sciences du vivant] ,Neurology [D14] [Human health sciences] ,Medicine (miscellaneous) ,Disease ,Multidisciplinaire, généralités & autres [F99] [Sciences du vivant] ,0302 clinical medicine ,Health Information Management ,Evaluation methods ,Biotechnology [F06] [Life sciences] ,Multidisciplinary, general & others [D99] [Human health sciences] ,0303 health sciences ,Outcome measures ,Computer Science Applications ,machine learning ,smart sensors ,bradykinesia ,Biomarker (medicine) ,Technology Platforms ,medicine.symptom ,medicine.medical_specialty ,Multidisciplinaire, généralités & autres [D99] [Sciences de la santé humaine] ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Health Informatics ,Multidisciplinary, general & others [F99] [Life sciences] ,Digital Biomarker ,Crowdsourcing ,Article ,VALIDATION ,Parkinson’s Disease ,03 medical and health sciences ,Physical medicine and rehabilitation ,Machine learning ,medicine ,030304 developmental biology ,mobile phone ,GENDER-DIFFERENCES ,Neurologie [D14] [Sciences de la santé humaine] ,business.industry ,biomarkers ,medicine.disease ,tremor ,Digital health ,nervous system diseases ,Clinical trial ,dyskinesia ,Dyskinesia ,Cardiovascular and Metabolic Diseases ,HYPOTHESIS TESTS ,business ,Biomarkers ,030217 neurology & neurosurgery - Abstract
Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)., npj Digital Medicine, 4 (1), ISSN:2398-6352
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- 2021
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4. Oxypred: Prediction and Classification of Oxygen-Binding Proteins
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Muthukrishnan, S., Garg, Aarti, and Raghava, G.P.S.
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- 2007
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5. VGIchan: Prediction and Classification of Voltage-Gated Ion Channels
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Saha, Sudipto, Zack, Jyoti, Singh, Balvinder, and Raghava, G.P.S.
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- 2006
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6. VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition
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Saha, Sudipto and Raghava, G.P.S.
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- 2006
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7. Computational methods in genome research
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Bhasin, Manoj and Raghava, G.P.S.
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- 2006
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8. CCDB: a curated database of genes involved in cervix cancer
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Agarwal, Subhash M., Raghav, Dhwani, Singh, Harinder, and Raghava, G.P.S.
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- 2011
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9. Prognostic Biomarkers for Predicting Papillary Thyroid Carcinoma Patients at High Risk Using Nine Genes of Apoptotic Pathway
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Arora, Chakit, primary, Kaur, Dilraj, additional, and Raghava, G.P.S, additional
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- 2020
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10. Pattern Recognition Receptor based Prognostic Biomarkers for predicting Survival of Uterine Corpus Endometrial Cancer Patients
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Kaur, Dilraj, primary, Arora, Chakit, additional, and Raghava, G.P.S, additional
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- 2020
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11. TAPPred Prediction of TAP-Binding Peptides in Antigens
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Bhasin, Manoj, primary, Lata, Sneh, additional, and Raghava, G.P.S., additional
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- 2007
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12. Contributors
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Abbott, Catherine A., primary, Baillie, David L., additional, Bhasin, Manoj, additional, Butler, Gregory, additional, Choi, Sangdun, additional, Cohen, Jacques, additional, Fayz, Brian, additional, Green, Michelle, additional, Grigoriev, Igor V., additional, Hsiang, Tom, additional, Karp, Peter D., additional, Kevorkov, Dmytro, additional, Krawetz, Stephen A., additional, Krummenacker, Markus, additional, Legendre, Pierre, additional, Lennie, Robert, additional, Martinez, Diego A., additional, Makarenkov, Vladimir, additional, Menz, R. Ian, additional, Meškauskas, Audrius, additional, Min, Xiang Jia, additional, Nevalainen, Helena, additional, Paley, Suzanne M., additional, Ranganathan, Shobha, additional, Pitman, Melissa R., additional, Platts, Adrian E., additional, Raghava, G.P.S., additional, Salamov, Asaf A., additional, Santat, Leah A., additional, Singh, Gautam B., additional, Storms, Reginald, additional, O'Toole, Nicholas, additional, Tjaden, Brian, additional, Tsang, Adrian, additional, Tsykin, Anna, additional, Wang, Dawei, additional, Wilkinson, Christopher R., additional, Wilson, Claire H., additional, Wongsai, Sangdao, additional, Yao, Bin, additional, and Yu, Yeisoo, additional
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- 2006
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13. CytoPred: a server for prediction and classification of cytokines
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Lata, Sneh and Raghava, G.P.S.
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- 2008
14. HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies
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Singh, Mahender Kumar, Srivastava, Shilpy, Raghava, G.P.S., and Varshney, Grish C.
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- 2006
15. Prediction of CTL epitopes using QM, SVM and ANN techniques
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Bhasin, Manoj and Raghava, G.P.S.
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- 2004
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16. Role of evolutionary information in prediction of aromatic-backbone NH interactions in proteins
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Kaur, Harpreet and Raghava, G.P.S.
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- 2004
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17. Large expert-curated database for benchmarking document similarity detection in biomedical literature search
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Brown, P. Zhou, Y. Tan, A.-C. El-Esawi, M.A. Liehr, T. Blanck, O. Gladue, D.P. Almeida, G.M.F. Cernava, T. Sorzano, C.O. Yeung, A.W.K. Engel, M.S. Chandrasekaran, A.R. Muth, T. Staege, M.S. Daulatabad, S.V. Widera, D. Zhang, J. Meule, A. Honjo, K. Pourret, O. Yin, C.-C. Zhang, Z. Cascella, M. Flegel, W.A. Goodyear, C.S. van Raaij, M.J. Bukowy-Bieryllo, Z. Campana, L.G. Kurniawan, N.A. Lalaouna, D. Hüttner, F.J. Ammerman, B.A. Ehret, F. Cobine, P.A. Tan, E.-C. Han, H. Xia, W. McCrum, C. Dings, R.P.M. Marinello, F. Nilsson, H. Nixon, B. Voskarides, K. Yang, L. Costa, V.D. Bengtsson-Palme, J. Bradshaw, W. Grimm, D.G. Kumar, N. Martis, E. Prieto, D. Sabnis, S.C. Amer, S.E.D.R. Liew, A.W.C. Perco, P. Rahimi, F. Riva, G. Zhang, C. Devkota, H.P. Ogami, K. Basharat, Z. Fierz, W. Siebers, R. Tan, K.H. Boehme, K.A. Brenneisen, P. Brown, J.A.L. Dalrymple, B.P. Harvey, D.J. Ng, G. Werten, S. Bleackley, M. Dai, Z. Dhariwal, R. Gelfer, Y. Hartmann, M.D. Miotla, P. Tamaian, R. Govender, P. Gurney-Champion, O.J. Kauppila, J.H. Zhang, X. Echeverría, N. Subhash, S. Sallmon, H. Tofani, M. Bae, T. Bosch, O. Cuív, P.O. Danchin, A. Diouf, B. Eerola, T. Evangelou, E. Filipp, F. Klump, H. Kurgan, L. Smith, S.S. Terrier, O. Tuttle, N. Ascher, D.B. Janga, S.C. Schulte, L.N. Becker, D. Browngardt, C. Bush, S.J. Gaullier, G. Ide, K. Meseko, C. Werner, G.D.A. Zaucha, J. Al-Farha, A.A. Greenwald, N.F. Popoola, S.I. Rahman, S. Xu, J. Yang, S.Y. Hiroi, N. Alper, O.M. Baker, C.I. Bitzer, M. Chacko, G. Debrabant, B. Dixon, R. Forano, E. Gilliham, M. Kelly, S. Klempnauer, K.-H. Lidbury, B.A. Lin, M.Z. Lynch, I. Ma, W. Maibach, E.W. Mather, D.E. Nandakumar, K.S. Ohgami, R.S. Parchi, P. Tressoldi, P. Xue, Y. Armitage, C. Barraud, P. Chatzitheochari, S. Coelho, L.P. Diao, J. Doxey, A.C. Gobet, A. Hu, P. Kaiser, S. Mitchell, K.M. Salama, M.F. Shabalin, I.G. Song, H. Stevanovic, D. Yadollahpour, A. Zeng, E. Zinke, K. Alimba, C.G. Beyene, T.J. Cao, Z. Chan, S.S. Gatchell, M. Kleppe, A. Piotrowski, M. Torga, G. Woldesemayat, A.A. Cosacak, M.I. Haston, S. Ross, S.A. Williams, R. Wong, A. Abramowitz, M.K. Effiong, A. Lee, S. Abid, M.B. Agarabi, C. Alaux, C. Albrecht, D.R. Atkins, G.J. Beck, C.R. Bonvin, A.M.J.J. Bourke, E. Brand, T. Braun, R.J. Bull, J.A. Cardoso, P. Carter, D. Delahay, R.M. Ducommun, B. Duijf, P.H.G. Epp, T. Eskelinen, E.-L. Fallah, M. Farber, D.B. Fernandez-Triana, J. Feyerabend, F. Florio, T. Friebe, M. Furuta, S. Gabrielsen, M. Gruber, J. Grybos, M. Han, Q. Heinrich, M. Helanterä, H. Huber, M. Jeltsch, A. Jiang, F. Josse, C. Jurman, G. Kamiya, H. de Keersmaecker, K. Kristiansson, E. de Leeuw, F.-E. Li, J. Liang, S. Lopez-Escamez, J.A. Lopez-Ruiz, F.J. Marchbank, K.J. Marschalek, R. Martín, C.S. Miele, A.E. Montagutelli, X. Morcillo, E. Nicoletti, R. Niehof, M. O'Toole, R. Ohtomo, T. Oster, H. Palma, J.-A. Paterson, R. Peifer, M. Portilla, M. Portillo, M.C. Pritchard, A.L. Pusch, S. Raghava, G.P.S. Roberts, N.J. Ross, K. Schuele, B. Sergeant, K. Shen, J. Stella, A. Sukocheva, O. Uversky, V.N. Vanneste, S. Villet, M.H. Viveiros, M. Vorholt, J.A. Weinstock, C. Yamato, M. Zabetakis, I. Zhao, X. Ziegler, A. Aizat, W.M. Atlas, L. Bridges, K.M. Chakraborty, S. Deschodt, M. Domingues, H.S. Esfahlani, S.S. Falk, S. Guisado, J.L. Kane, N.C. Kueberuwa, G. Lau, C.L. Liang, D. Liu, E. Luu, A.M. Ma, C. Ma, L. Moyer, R. Norris, A.D. Panthee, S. Parsons, J.R. Peng, Y. Pinto, I.M. Reschke, C.R. Sillanpää, E. Stewart, C.J. Uhle, F. Yang, H. Zhou, K. Zhu, S. Ashry, M. Bergsland, N. Berthold, M. Chen, C.-E. Colella, V. Cuypers, M. Eskew, E.A. Fan, X. Gajda, M. Gonzálezlez-Prendes, R. Goodin, A. Graham, E.B. Groen, E.J.N. Gutiérrez-Sacristán, A. Habes, M. Heffler, E. Higginbottom, D.B. Janzen, T. Jayaraman, J. Jibb, L.A. Jongen, S. Kinyanjui, T. Koleva-Kolarova, R.G. Li, Z. Liu, Y.-P. Lund, B.A. Lussier, A.A. Ma, L. Mier, P. Moore, M.D. Nagler, K. Orme, M.W. Pearson, J.A. Prajapati, A.S. Saito, Y. Tröder, S.E. Uchendu, F. Verloh, N. Voutchkova, D.D. Abu-Zaid, A. Bakkach, J. Baumert, P. Dono, M. Hanson, J. Herbelet, S. Hobbs, E. Kulkarni, A. Kumar, N. Liu, S. Loft, N.D. Reddan, T. Senghore, T. Vindin, H. Xu, H. Bannon, R. Chen, B. Cheung, J.T.K. Cooper, J. Esnakula, A.K. Feghali, K.A. Ghelardi, E. Gnasso, A. Horbar, J. Lai, H.M. Li, J. Ma, L. Ma, R. Pan, Z. Peres, M.A. Pranata, R. Seow, E. Sydes, M. Testoni, I. Westermair, A.L. Yang, Y. Afnan, M. Albiol, J. Albuquerque, L.G. Amir, S. Amiya, E. Amorim, R.M. An, Q. Andersen, S.U. Aplin, J.D. Argyropoulos, C. Asmann, Y.W. Assaeed, A.M. Atanasov, A.G. Atchison, D.A. Avery, S.V. Avillach, P. Baade, P.D. Backman, L. Badie, C. Baldi, A. Ball, E. Bardot, O. Barnett, A.G. Basner, M. Batra, J. Bazanova, O.M. Beale, A. Beddoe, T. Bell, M.L. Berezikov, E. Berners-Price, S. Bernhardt, P. Berry, E. Bessa, T.B. Billington, C. Birch, J. Blakely, R.D. Blaskovich, M.A.T. Blum, R. Boelaert, M. Bogdanos, D. Bosch, C. Bourgoin, T. Bouvard, D. Boykin, L.M. Bradley, G. Braun, D. Brownlie, J. Brühl, A. Burt, A. Butler, L.M. Byrareddy, S.N. Byrne, H.J. Cabantous, S. Calatayud, S. Candal, E. Carlson, K. Casillas, S. Castelvetro, V. Caswell, P.T. Cavalli, G. Cerovsky, V. Chagoyen, M. Chen, C.-S. Chen, D.F. Chen, H. Chen, H. Chen, J.-T. Chen, Y. Cheng, C. Cheng, J. Chinapaw, M. Chinopoulos, C. Cho, W.C.S. Chong, L. Chowdhury, D. Chwalibog, A. Ciresi, A. Cockcroft, S. Conesa, A. Cook, P.A. Cooper, D.N. Coqueret, O. Corea, E.M. Costa, A. Costa, E. Coupland, C. Crawford, S.Y. Cruz, A.D. Cui, H. Cui, Q. Culver, D.C. D'Angiulli, A. Dahms, T.E.S. Daigle, F. Dalgleish, R. Danielsen, H.E. Darras, S. Davidson, S.M. Day, D.A. Degirmenci, V. Demaison, L. Devriendt, K. Ding, J. Dogan, Y. Dong, X.C. Donner, C.F. Dressick, W. Drevon, C.A. Duan, H. Ducho, C. Dumaz, N. Dwarakanath, B.S. Ebell, M.H. Eisenhardt, S. Elkum, N. Engel, N. Erickson, T.B. Fairhead, M. Faville, M.J. Fejzo, M.S. Festa, F. Feteira, A. Flood-Page, P. Forsayeth, J. Fox, S.A. Franks, S.J. Frentiu, F.D. Frilander, M.J. Fu, X. Fujita, S. Galea, I. Galluzzi, L. Gani, F. Ganpule, A.P. García-Alix, A. Gedye, K. Giordano, M. Giunta, C. Gleeson, P.A. Goarant, C. Gong, H. Gora, D. Gough, M.J. Goyal, R. Graham, K.E. Grande-Pérez, A. Graves, P.M. Greidanus, H. Grice, D. Grunau, C. Gumulya, Y. Guo, Y. Gurevich, V.V. Gusev, O. Hacker, E. Hage, S.R. Hagen, G. Hahn, S. Haller, D.M. Hammerschmidt, S. Han, J. Han, R. Handfield, M. Hapuarachchi, H.C. Harder, T. Hardingham, J.E. Heck, M. Heers, M. Hew, K.F. Higuchi, Y. Hilaire, C.St. Hilton, R. Hodzic, E. Hone, A. Hongoh, Y. Hu, G. Huber, H.P. Hueso, L.E. Huirne, J. Hurt, L. Idborg, H. Ikeo, K. Ingley, E. Jakeman, P.M. Jensen, A. Jia, H. Jia, H. Jia, S. Jiang, J. Jiang, X. Jin, Y. Jo, D. Johnson, A.M. Johnston, M. Jonscher, K.R. Jorens, P.G. Jorgensen, J.O.L. Joubert, J.W. Jung, S.-H. Junior, A.M. Kahan, T. Kamboj, S.K. Kang, Y.-K. Karamanos, Y. Karp, N.A. Kelly, R. Kenna, R. Kennedy, J. Kersten, B. Khalaf, R.A. Khalid, J.M. Khatlani, T. Khider, T. Kijanka, G.S. King, S.R.B. Kluz, T. Knox, P. Kobayashi, T. Koch, K.-W. Kohonen-Corish, M.R.J. Kong, X. Konkle-Parker, D. Korpela, K.M. Kostrikis, L.G. Kraiczy, P. Kratz, H. Krause, G. Krebsbach, P.H. Kristensen, S.R. Kumari, P. Kunimatsu, A. Kurdak, H. Kwon, Y.D. Lachat, C. Lagisz, M. Laky, B. Lammerding, J. Lange, M. Larrosa, M. Laslett, A.L. Laverman, G.D. Leclair, E.E. Lee, K.-W. Lee, M.-Y. Lee, M.-S. Li, G. Li, J. Lieb, K. Lim, Y.Y. Lindsey, M.L. Line, P.-D. Liu, D. Liu, F. Liu, H. Liu, H. Lloyd, V.K. Lo, T.-W. Locci, E. Loidl, J. Lorenzen, J. Lorkowski, S. Lovell, N.H. Lu, H. Lu, W. Lu, Z. Luengo, G.S. Lundh, L.-G. Lysy, P.A. Mabb, A. Mack, H.G. Mackey, D.A. Mahdavi, S.R. Maher, P. Maher, T. Maity, S.N. Malgrange, B. Mamoulakis, C. Mangoni, A.A. Manke, T. Manstead, A.S.R. Mantalaris, A. Marsal, J. Marschall, H.-U. Martin, F.L. Martinez-Raga, J. Martinez-Salas, E. Mathieu, D. Matsui, Y. Maza, E. McCutcheon, J.E. McKay, G.J. McMillan, B. McMillan, N. Meads, C. Medina, L. Merrick, B.A. Metzger, D.W. Meunier, F.A. Michaelis, M. Micheau, O. Mihara, H. Mintz, E.M. Mizukami, T. Moalic, Y. Mohapatra, D.P. Monteiro, A. Montes, M. Moran, J.V. Morozov, S.Y. Mort, M. Murai, N. Murphy, D.J. Murphy, S.K. Murray, S.A. Naganawa, S. Nammi, S. Nasios, G. Natoli, R.M. Nguyen, F. Nicol, C. van Nieuwerburgh, F. Nilsen, E.B. Nobile, C.J. O'Mahony, M. Ohlsson, S. Olatunbosun, O. Olofsson, P. Ortiz, A. Ostrikov, K. Otto, S. Outeiro, T.F. Ouyang, S. Paganoni, S. Page, A. Palm, C. Paradies, Y. Parsons, M.H. Parsons, N. Pascal, P. Paul, E. Peckham, M. Pedemonte, N. Pellizzon, M.A. Petrelli, M. Pichugin, A. Pinto, C.J.C. Plevris, J.N. Pollesello, P. Polz, M. Ponti, G. Porcelli, P. Prince, M. Quinn, G.P. Quinn, T.J. Ramula, S. Rappsilber, J. Rehfeldt, F. Reiling, J.H. Remacle, C. Rezaei, M. Riddick, E.W. Ritter, U. Roach, N.W. Roberts, D.D. Robles, G. Rodrigues, T. Rodriguez, C. Roislien, J. Roobol, M.J. Rowe, A. Ruepp, A. van Ruitenbeek, J. Rust, P. Saad, S. Sack, G.H. Santos, M. Saudemont, A. Sava, G. Schrading, S. Schramm, A. Schreiber, M. Schuler, S. Schymkowitz, J. Sczyrba, A. Seib, K.L. Shi, H.-P. Shimada, T. Shin, J.-S. Shortt, C. Silveyra, P. Skinner, D. Small, I. Smeets, P.A.M. So, P.-W. Solano, F. Sonenshine, D.E. Song, J. Southall, T. Speakman, J.R. Srinivasan, M.V. Stabile, L.P. Stasiak, A. Steadman, K.J. Stein, N. Stephens, A.W. Stewart, D.I. Stine, K. Storlazzi, C. Stoynova, N.V. Strzalka, W. Suarez, O.M. Sultana, T. Sumant, A.V. Summers, M.J. Sun, G. Tacon, P. Tanaka, K. Tang, H. Tanino, Y. Targett-Adams, P. Tayebi, M. Tayyem, R. Tebbe, C.C. Telfer, E.E. Tempel, W. Teodorczyk-Injeyan, J.A. Thijs, G. Thorne, S. Thrift, A.G. Tiffon, C. Tinnefeld, P. Tjahjono, D.H. Tolle, F. Toth, E. Del Tredici, A.L. Tsapas, A. Tsirigotis, K. Turak, A. Tzotzos, G. Udo, E.E. Utsumi, T. Vaidyanathan, S. Vaillant, M. Valsesia, A. Vandenbroucke, R.E. Veiga, F.H. Vendrell, M. Vesk, P.A. Vickers, P. Victor, V.M. Villemur, R. Vohl, M.-C. Voolstra, C.R. Vuillemin, A. Wakelin, S. Waldron, L. Walsh, L.J. Wang, A.Y. Wang, F. Wang, Y. Watanabe, Y. Weigert, A. Wen, J.-C. Wham, C. White, E.P. Wiener, J. Wilharm, G. Wilkinson, S. Willmann, R. Wilson, C. Wirth, B. Wojan, T.R. Wolff, M. Wong, B.M. Wu, T.-W. Wuerbel, H. Xiao, X. Xu, D. Xu, J.W. Xu, J. Xue, B. Yalcin, S. Yan, H. Yang, E.-C. Yang, S. Yang, W. Ye, Y. Ye, Z.-Q. Yli-Kauhaluoma, J. Yoneyama, H. Yu, Y. Yuan, G.-C. Yuh, C.-H. Zaccolo, M. Zeng, C. Zevnik, B. Zhang, C. Zhang, L. Zhang, Y. Zhang, Y. Zhang, Z. Zhang, Z.-Y. Zhao, Y. Zhou, M. Zuberbier, T. Aanei, C.M. Ahmad, R. Al-Lawama, M. Alanio, A. Allardyce, J. Alonso-Caneiro, D. Atack, J.M. Baier, D. Bansal, A. Benezeth, Y. Berbesque, C. Berrevoet, F. Biedermann, P.H.W. Bijleveld, E. Bittner, F. Blombach, F. van den Bos, W. Boudreau, S.A. Bramoweth, A.D. Braubach, O. Cai, Y. Campbell, M. Cao, Z. Catry, T. Chen, X. Cheng, S. Chung, H.-J. Chávez-Fumagalli, M.A. Conway, A. Costa, B.M. Cyr, N. Dean, L.T. Denzel, M.S. Dlamini, S.V. Dudley, K.J. Dufies, M. Ecke, T. Eckweiler, D. Eixarch, E. El-Adawy, H. Emmrich, J.V. Eustace, A.J. Falter-Wagner, C.M. Farhoudi, R. Fuss, J. Gao, J. Gill, M.R. Gloyn, L. Goggs, R. Govinden, U. Greene, G. Greiff, V. Grundle, D.S. Grüneberg, P. Gumede, N. Haore, G. Harrison, P. Hoenner, X. Hojsgaard, D. Hori, H. Ikonomopoulou, M.P. Jeurissen, P. Johnson, D.M. Kabra, D. Kamagata, K. Karmakar, C. Kasian, O. Kaye, L.K. Khan, M.M. Kim, Y.-M. Kish, J.K. Kobold, S. Kohanbash, G. Kohls, G. Kugler, J.-M. Kumar, G. Lacy-Colson, J. Latif, A. Lauschke, V.M. Li, B. Lim, C.J. Liu, F. Liu, X. Lu, J.-J. Lu, Q. Mahavadi, P. Marzocchi, U. McGarrigle, C.A. van Meerten, T. Min, R. Moal, I. Molari, M. Molleman, L. Mondal, S.R. van de Mortel, T. Moss, W.N. Moultos, O.A. Mukherjee, M. Nakayama, K. Narayan, E. Navaratnarajah Neumann, P.-A. Nie, J. Nie, Y. Niemeyer, F. Nolan, F. Nwaiwu, O. Oldenmenger, W.H. Olumayede, E. Ou, J. Pallebage-Gamarallage, M. Pearce, S.P. Pelkonen, T. Pelleri, M.C. Pereira, J.L. Pheko, M. Pinto, K.A. Piovesan, A. Pluess, M. Podolsky, I.M. Prescott, J. Qi, D. Qi, X. Raikou, V.D. Ranft, A. Rhodes, J. Rotge, J.-Y. Rowe, A.D. Saggar, M. Schuon, R.A. Shahid, S. Shalchyan, V. Shirvalkar, P. Shiryayev, O. Singh, J. Smout, M.J. Soares, A. Song, C. Srivastava, K. Srivastava, R.K. Sun, J. Szabo, A. Szymanski, W. Tai, C.N.P. Takeuchi, H. Tanadini-Lang, S. Tang, F. Tao, W. Theron, G. Tian, C.F. Tian, Y.-S. Tuttle, L.M. Valenti, A. Verlot, P. Walker, M. Wang, J. Welter, D. Winslade, M. Wu, D. Wu, Y.-R. Xiao, H. Xu, B. Xu, J. Xu, Z. Yang, D. Yang, M. Yankilevich, P. You, Y. Yu, C. Zhan, J. Zhang, G. Zhang, K. Zhang, T. Zhang, Y. Zhao, G. Zhao, J. Zhou, X. Zhu, Z. Ajani, P.A. Anazodo, U.C. Bagloee, S.A. Bail, K. Bar, I. Bathelt, J. Benkeser, D. Bernier, M.L. Blanchard, A.M. Boakye, D.W. Bonatsos, V. Boon, M.H. Bouboulis, G. Bromfield, E. Brown, J. Bul, K.C.M. Burton, K.J. Butkowski, E.G. Carroll, G. Chao, F. Charrier, E.E. Chen, X. Chen, Y.-C. Chenguang Choi, J.R. Christoffersen, T. Comel, J.C. Cosse, C. Cui, Y. van Dessel, P. Dhaval Diodato, D. Duffey, M. Dutt, A. Egea, L.G. El-Said, M. Faye, M. Fernandez-Fernandez, B. Foley, K.G. Founou, L.L. Fu, F. Gadelkareem, R.A. Galimov, E. Garip, G. Gemmill, A. Gouil, Q. Grey, J. Gridneva, Z. Grothe, M.J. Grébert, T. Guerrero, F. Guignard, L. Haenssgen, M.J. Hasler, D. Holgate, J.Y. Huang, A. Hulse-Kemp, A.M. Jean-Quartier, C. Jeon, S.-M. Jia, Y. Jutzeler, C. Kalatzis, P. Karim, M. Karsay, K. Keitel, A. Kempe, A. Keown, J.R. Khoo, C.M. Khwaja, N. Kievit, R.A. Kosanic, S. Koutoukidis, D.A. Kramer, P. Kumar, D. Kiraǧ, N. Lanza, G. Le, T.D. Leem, J.W. Leightley, D. Leite, A. Lercher, L. Li, Y. Lim, R. Lima, L.R.A. Lin, L. Ling, T. Liu, Y. Liu, Z. Lu, Y. Lum, F.M. Luo, H. Machhi, J. Macleod, A. Macwan, I. Madala, H.R. Madani, N. de Maio, N. Makowiecki, K. Mallinson, D.J. Margelyte, R. Maria, C. Markonis, Y. Marsili, L. Mavoa, S. McWilliams, L. Megersa, M. Souto-Maior, C. Menichetti, J. Mercieca-Bebber, R. Miller, J.J. Minde, D.-P.M. Minges, A. Mishra, E. Mishra, V.R. Moores, C. Morrice, N. Moskalensky, A.E. Navarin, N. Negera, E. Nolet, P. Nordberg, A. Nordén, R. Nowicki, J.P. Olova, N. Olszewski, P. Onzima, R. Pan, C.-L. Park, C. Park, D.I. Park, S. Patil, C.D. Pedro, S.A. Perry, S.R. Peter, J. Peterson, B.M. Pezzuolo, A. Pozdnyakov, I. Qian, S. Qin, L. Rafe, A. Raote, I. Raza, A. Rebl, H. Refai, O. Regan, T. Richa, T. Richardson, M.F. Robinson, K.R. Rossoni, L. Rouet, R. Safaei, S. Schneeberger, P.H.H. Schwotzer, D. Sebastian, A. Selinski, J. Seltmann, S. Sha, F. Shalev, N. Shang, J.-L. Singer, J. Singh, M. Smith, T. Solomon-Moore, E. Song, L. Soraggi, S. Stanley, R. Steckhan, N. Strobl, F. Subissi, L. Supriyanto, I. Surve, C.R. Suzuki, T. Syme, C. Sörelius, K. Tang, Y. Tantawy, M. Tennakoon, S. Teseo, S. Toelzer, C. Tomov, N. Tovar, M. Tran, L. Tripathi, S. Tuladhar, A.M. Ukubuiwe, A.C. Ung, C.O.L. Valgepea, K. Vatanparast, H. Vidal, A. Wang, F. Wang, Q. Watari, R. Webster, R. Webster, R. Wei, J. Wibowo, D. Wingenbach, T.S.H. Xavier, R.M. Xiao, S. Xiong, P. Xu, S. Xu, S. Yao, R. Yao, W. Yin, Q. Yu, Y. Zaitsu, M. Zeineb, Z. Zhan, X.-Y. Zhang, J. Zhang, R. Zhang, W. Zhang, X. Zheng, S. Zhou, B. Zhou, X. Ahmad, H. Akinwumi, S.A. Albery, G.F. Alhowimel, A. Ali, J. Alshehri, M. Alsuhaibani, M. Anikin, A. Azubuike, S.O. Bach-Mortensen, A. Baltiansky, L. Bartas, M. Belachew, K.Y. Bhardwaj, V. Binder, K. Bland, N.S. Boah, M. Bullen, B. Calabrò, G.E. Callahan, T.J. Cao, B. Chalmers, K. Chang, W. Che, Z. Chen, A.T.Y. Chen, H. Chen, H. Chen, Y. Chen, Z. Choi, Y. Chowdhury, M.A.K. Christensen, M.R. Cooke, R.S.C. Cottini, M. Covington, N.V. Cunningham, C. Delarocque, J. Devos, L. Dhar, A.R. Ding, K.-F. Dong, K. Dong, Z. Dreyer, N. Ekstrand, C. Fardet, T. Feleke, B.E. Feurer, T. Freitas, A. Gao, T. Gebremedhin Giganti, F. Grabowski, P. Guerra-Mora, J.R. Guo, C. Guo, X. Gupta, H. He, S. Heijne, M. Heinemann, S. Hogrebe, A. Huang, Z. Iskander-Rizk, S. Iyer, L.M. Jahan, Y. James, A.S. Joel, E. Joffroy, B. Jégousse, C. Kambondo, G. Karnati, P. Kaya, C. Ke, A. Kelly, D. Kickert, R. Kidibule, P.E. Kieselmann, J.P. Kim, H.J. Kitazawa, T. Lamberts, A. Li, Y. Liang, H. Linn, S.N. Litfin, T. Liusuo, W. Lygirou, V. Mahato, A.K. Mai, Z.-M. Major, R.W. Mali, S. Mallis, P. Mao, W. Marvin-Dowle, K. Mason, L.D. Merideth, B. Merino-Plaza, M.J. Merlaen, B. Messina, R. Mishra, A.K. Muhammad, J. Musinguzi, C. Nanou, A. Naqash, A. Nguyen, J.T. Nguyen, T.T.H. Ni, D. Nida Notcovich, S. Ohst, B. Ollivier, Q.R. Osses, D.F. Peng, X. Plantinga, A. Pulia, M. Rafiq, M. Raman, A. Raucher-Chéné Rawski, R. Ray, A. Razak, L.A. Rudolf, K. Rusch, P. Sadoine, M.L. Schmidt, A. Schurr, R. Searles, S. Sharma, S. Sheehan, B. Shi, C. Shohayeb, B. Sommerlad, A. Strehlow, J. Sun, X. Sundar, R. Taherzadeh, G. Tahir, N.D.M. Tang, J. Testa, J. Tian, Z. Tingting, Q. Verheijen, G.P. Vickstrom, C. Wang, T. Wang, X. Wang, Z. Wei, P. Wilson, A. Wyart Yassine, A.-A. Yousefzadeh, A. Zare, A. Zeng, Z. Zhang, C. Zhang, H. Zhang, L. Zhang, T. Zhang, W. Zhang, Z. Zhou, J. Zhu, D. Adamo, V. Adeyemo, A.A. Aggelidou, M. Al-Owaifeer, A.M. Al-Riyami, A.Z. Alzghari, S.K. Andersen, V. Angus, K. Asaduzzaman, M. Asady, H. Ato, D. Bai, X. Baines, R.L. Ballantyne, M. Ban, B. Beck, J. Ben-Nafa, W. Black, E. Blancher, A. Blankstein, R. Bodagh, N. Borges, P. Brooks, A. Brox-Ponce, J. Brunetti, A. Canham, C.D. Carninci, P. Carvajal, R. Chang, S.C. Chao, J. Chatterjee, P. Chen, H. Chen, L. Chen, Y.-C. Chhatriwalla, A.K. Chikowe, I. Chuang, T.-J. Collevatti, R.G. Cornejo, D.A.V. Cuenda, A. Dao, M. Dauga, D. Deng, Z. Devkota, K. Doan, L.V. Elewa, Y.H.A. Fan, D. Faruk, M. Feifei, S. Ferguson, T.S. Fleres, F. Foster, E.J. Foster, S. Furer, T. Gao, Y. Garcia-Rivera, E.J. Gazdar, A. George, R.B. Ghosh, S. Gianchecchi, E. Gleason, J.M. Hackshaw, A. Hall, A. Hall, R. Harper, P. Hogg, W.E. Huang, G. Hunter, K.E. Ijzerman, A.P. Jesus, C. Jian, G. Lewis, J.S., Jr. Kanj, S.S. Kaur, H. Kelly, S. Kheir, F. Kichatova, V.S. Kiyani, M. Klein, R. Kovesi, T. Kraschnewski, J.L. Kumar, A.P. Labutin, D. Lazo-Langner, A. Leclercq, G. Li, M. Li, Q. Li, T. Li, Y. Liao, W.-T. Liao, Z.-Y. Lin, J. Lizer, J. Lobreglio, G. Lowies, C. Lu, C. Majeed, H. Martin, A. Martinez-Sobrido, L. Meresh, E. Middelveen, M. Mohebbi, A. Mota, J. Mozaheb, Z. Muyaya, L. Nandhakumar, A. Ng, S.H.X. Obeidat, M. Oh, D.-H. Owais, M. Pace-Asciak, P. Panwar, A. Park, C. Patterson, C. Penagos-Tabaree, F. Pianosi, P.T. Pinzi, V. Pridans, C. Psaroulaki, A. Pujala, R.K. Pulido-Arjona, L. Qi, P.-F. Rahman, P. Rai, N.K. Rassaf, T. Refardt, J. Ricciardi, W. Riess, O. Rovas, A. Sacks, F.M. Saleh, S. Sampson, C. Schmutz, A. Sepanski, R. Sharma, N. Singh, M. Spearman, P. Subramaniapillai, M. Swali, R. Tan, C.M. Tellechea, J.I. Thomas, L.-M. Tong, X. Vavvas, D.G. Veys, R. Vitriol, V. Wang, H.-D. Wang, J. Wang, J. Waugh, J. Webb, S.A. Williams, B.A. Workman, A.D. Xiang, T. Xie, L.-X. Xu, J. Xu, T. Yang, C. Yoon, J.G. Yuan, C.M. Zaritsky, A. Zhang, Y. Zhao, H. Zuckerman, H. Lyu, R. Pullan, W. RELISH Consortium
- Abstract
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press.
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- 2019
18. Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics
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Dhall, Anjali, primary, Patiyal, Sumeet, additional, Kaur, Harpreet, additional, Bhalla, Sherry, additional, Arora, Chakit, additional, and Raghava, G.P.S., additional
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- 2019
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19. Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides
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Singh, Sandeep, primary, Srivastava, Hemant Kumar, additional, Kishor, Gaurav, additional, Singh, Harinder, additional, Agrawal, Piyush, additional, and Raghava, G.P.S., additional
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- 2017
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20. Tumor Homing Peptides as Molecular Probes for Cancer Therapeutics, Diagnostics and Theranostics
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Gautam, A., primary, Kapoor, P., additional, Chaudhary, K., additional, Kumar, R., additional, Drug Discovery Consortium, Open, additional, and Raghava, G.P.S., additional
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- 2014
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21. Machine learning competition in immunology – Prediction of HLA class I binding peptides
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Zhang, Guang Lan, Ansari, Hifzur Rahman, Bradley, Phil, Cawley, Gavin C., Hertz, Tomer, Hu, Xihao, Jojic, Nebojsa, Kim, Yohan, Kohlbacher, Oliver, Lund, Ole, Lundegaard, Claus, Magaret, Craig A., Nielsen, Morten, Papadopoulos, Harris, Raghava, G.P.S., Tal, Vider-Shalit, Xue, Li C., Yanover, Chen, Zhu, Shanfeng, Rock, Michael T., Crowe, James E., Panayiotou, Christos, Polycarpou, Marios M., Duch, Włodzisław, Brusic, Vladimir, Zhang, Guang Lan, Ansari, Hifzur Rahman, Bradley, Phil, Cawley, Gavin C., Hertz, Tomer, Hu, Xihao, Jojic, Nebojsa, Kim, Yohan, Kohlbacher, Oliver, Lund, Ole, Lundegaard, Claus, Magaret, Craig A., Nielsen, Morten, Papadopoulos, Harris, Raghava, G.P.S., Tal, Vider-Shalit, Xue, Li C., Yanover, Chen, Zhu, Shanfeng, Rock, Michael T., Crowe, James E., Panayiotou, Christos, Polycarpou, Marios M., Duch, Włodzisław, and Brusic, Vladimir
- Abstract
Experimental studies of immune system and related applications such as characterization of immune responses against pathogens, vaccine design, or optimization of therapies are combinatorially complex, time-consuming and expensive. The main methods for large-scale identification of T-cell epitopes from pathogens or cancer proteomes involve either reverse immunology or high-throughput mass spectrometry (HTMS). Reverse immunology approaches involve pre-screening of proteomes by computational algorithms, followed by experimental validation of selected targets ( [Mora et al., 2006], [De Groot et al., 2008] and [Larsen et al., 2010]). HTMS involves HLA typing, immunoaffinity chromatography of HLA molecules, HLA extraction, and chromatography combined with tandem mass spectrometry, followed by the application of computational algorithms for peptide characterization (Bassani-Sternberg et al., 2010). Hundreds of naturally processed HLA class I associated peptides have been identified in individual studies using HTMS in normal (Escobar et al., 2008), cancer ( [Antwi et al., 2009] and [Bassani-Sternberg et al., 2010]), autoimmunity-related (Ben Dror et al., 2010), and infected samples (Wahl et al, 2010). Computational algorithms are essential steps in high-throughput identification of T-cell epitope candidates using both reverse immunology and HTMS approaches. Peptide binding to MHC molecules is the single most selective step in defining T cell epitope and the accuracy of computational algorithms for prediction of peptide binding, therefore, determines the accuracy of the overall method. Computational predictions of peptide binding to HLA, both class I and class II, use a variety of algorithms ranging from binding motifs to advanced machine learning techniques ( [Brusic et al., 2004] and [Lafuente and Reche, 2009]) and standards for their assessments have been developed. The assessments of computational servers that predict peptide binding to several common HLA class I alleles
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- 2011
22. AntigenDB:an immunoinformatics database of pathogen antigens
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Ansari, Hifzur R., Flower, Darren R, Raghava, G.P.S., Ansari, Hifzur R., Flower, Darren R, and Raghava, G.P.S.
- Abstract
The continuing threat of infectious disease and future pandemics, coupled to the continuous increase of drug-resistant pathogens, makes the discovery of new and better vaccines imperative. For effective vaccine development, antigen discovery and validation is a prerequisite. The compilation of information concerning pathogens, virulence factors and antigenic epitopes has resulted in many useful databases. However, most such immunological databases focus almost exclusively on antigens where epitopes are known and ignore those for which epitope information was unavailable. We have compiled more than 500 antigens into the AntigenDB database, making use of the literature and other immunological resources. These antigens come from 44 important pathogenic species. In AntigenDB, a database entry contains information regarding the sequence, structure, origin, etc. of an antigen with additional information such as B and T-cell epitopes, MHC binding, function, gene-expression and post translational modifications, where available. AntigenDB also provides links to major internal and external databases. We shall update AntigenDB on a rolling basis, regularly adding antigens from other organisms and extra data analysis tools. AntigenDB is available freely at http://www.imtech.res.in/raghava/antigendb and its mirror site http://www.bic.uams.edu/raghava/antigendb.
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- 2010
23. EGFRIndb: Epidermal Growth Factor Receptor Inhibitor Database
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Yadav, Inderjit, primary, Singh, Harinder, additional, Khan, Mohd., additional, Chaudhury, Ashok, additional, Raghava, G.P.S., additional, and Agarwal, Subhash, additional
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- 2014
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24. Machine learning competition in immunology – Prediction of HLA class I binding peptides
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Zhang, Guang Lan, primary, Ansari, Hifzur Rahman, additional, Bradley, Phil, additional, Cawley, Gavin C., additional, Hertz, Tomer, additional, Hu, Xihao, additional, Jojic, Nebojsa, additional, Kim, Yohan, additional, Kohlbacher, Oliver, additional, Lund, Ole, additional, Lundegaard, Claus, additional, Magaret, Craig A., additional, Nielsen, Morten, additional, Papadopoulos, Harris, additional, Raghava, G.P.S., additional, Tal, Vider-Shalit, additional, Xue, Li C., additional, Yanover, Chen, additional, Zhu, Shanfeng, additional, Rock, Michael T., additional, Crowe, James E., additional, Panayiotou, Christos, additional, Polycarpou, Marios M., additional, Duch, Włodzisław, additional, and Brusic, Vladimir, additional
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- 2011
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25. Prediction and classification of chemokines and their receptors
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Lata, S., primary and Raghava, G.P.S., additional
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- 2009
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26. AC2DGel: Analysis and Comparison of 2D Gels
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Kush, Amit, primary and Raghava, G.P.S., additional
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- 2008
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27. Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machine
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Kim, Jong Kyoung, primary, Raghava, G.P.S., additional, Bang, Sung-Yang, additional, and Choi, Seungjin, additional
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- 2006
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28. Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure
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Garg, Aarti, primary, Kaur, Harpreet, additional, and Raghava, G.P.S., additional
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- 2005
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29. Prediction of α-turns in proteins using PSI-BLAST profiles and secondary structure information
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Kaur, Harpreet, primary and Raghava, G.P.S., additional
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- 2004
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30. Prediction of Promiscuous and High-Affinity Mutated MHC Binders
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Bhasin, Manoj, primary and Raghava, G.P.S., additional
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- 2003
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31. ProPred1: prediction of promiscuous MHC Class-I binding sites
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Singh, Harpreet, primary and Raghava, G.P.S., additional
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- 2003
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32. A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment
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Kaur, Harpreet, primary and Raghava, G.P.S., additional
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- 2003
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33. An evaluation of β-turn prediction methods
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Kaur, Harpreet, primary and Raghava, G.P.S., additional
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- 2002
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34. GWFASTA: Server for FASTA Search in Eukaryotic and Microbial Genomes
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Issac, Biju, primary and Raghava, G.P.S., additional
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- 2002
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35. Mapping of the plasminogen binding site of streptokinase with short synthetic peptides
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Nihalani, Deepak, primary, Raghava, G.P.S., additional, and Sahni, Girish, additional
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- 1997
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36. Analysis and prediction of affinity of TAP binding peptides using cascade SVM.
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Bhasin, Manoj and Raghava, G.P.S.
- Abstract
The generation of cytotoxic T lymphocyte (CTL) epitopes from an antigenic sequence involves number of intracellular processes, including production of peptide fragments by proteasome and transport of peptides to endoplasmic reticulum through transporter associated with antigen processing (TAP). In this study, 409 peptides that bind to human TAP transporter with varying affinity were analyzed to explore the selectivity and specificity of TAP transporter. The abundance of each amino acid from P1 to P9 positions in high-, intermediate-, and low-affinity TAP binders were examined. The rules for predicting TAP binding regions in an antigenic sequence were derived from the above analysis. The quantitative matrix was generated on the basis of contribution of each position and residue in binding affinity. The correlation of r = 0.65 was obtained between experimentally determined and predicted binding affinity by using a quantitative matrix. Further a support vector machine (SVM)-based method has been developed to model the TAP binding affinity of peptides. The correlation ( r = 0.80) was obtained between the predicted and experimental measured values by using sequence-based SVM. The reliability of prediction was further improved by cascade SVM that uses features of amino acids along with sequence. An extremely good correlation ( r = 0.88) was obtained between measured and predicted values, when the cascade SVM-based method was evaluated through jackknife testing. A Web service, TAPPred ( or ), has been developed based on this approach. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
- View/download PDF
37. PEPstr: A de novo Method for Tertiary Structure Prediction of Small Bioactive Peptides
- Author
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Kaur, Harpreet, Garg, Aarti, and Raghava, G.P.S.
- Abstract
Among secondary structure elements, -turns are ubiquitous and major feature of bioactive peptides. We analyzed 77 biologically active peptides with length varying from 9 to 20 residues. Out of 77 peptides, 58 peptides were found to contain at least one -turn. Further, at the residue level, 34.9 of total peptide residues were found to be in - turns, higher than the number of helical (32.3) and -sheet residues (6.9). So, we utilized the predicted -turns information to develop an improved method for predicting the three-dimensional (3D) structure of small peptides. In principle, we built four different structural models for each peptide. The first ‘model I’ was built by assigning all the peptide residues an extended conformation ( 180°). Second ‘model II’ was built using the information of regular secondary structures (helices, -strands and coil) predicted from PSIPRED. In third ‘model III’, secondary structure information including -turn types predicted from BetaTurns method was used. The fourth ‘model IV’ had main-chain , angles of model III and side chain angles assigned using standard Dunbrack backbone dependent rotamer library. These models were further refined using AMBER package and the resultant C rmsd values were calculated. It was found that adding the -turns to the regular secondary structures greatly reduces the rmsd values both before and after the energy minimization. Hence, the results indicate that regular and irregular secondary structures, particularly -turns information can provide valuable and vital information in the tertiary structure prediction of small bioactive peptides. Based on the above study, a web server PEPstr (http://www.imtech.res.in/raghava/pepstr/) was developed for predicting the tertiary structure of small bioactive peptides.
- Published
- 2007
38. Support Vector Machine Based Prediction of Glutathione S-Transferase Proteins
- Author
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Mishra, Nitish Kumar, Kumar, Manish, and Raghava, G.P.S.
- Abstract
Glutathione S-transferase (GST) proteins play vital role in living organism that includes detoxification of exogenous and endogenous chemicals, survivability during stress condition. This paper describes a method developed for predicting GST proteins. We have used a dataset of 107 GST and 107 non-GST proteins for training and the performance of the method was evaluated with five-fold cross-validation technique. First a SVM based method has been developed using amino acid and dipeptide composition and achieved the maximum accuracy of 91.59 and 95.79 respectively. In addition we developed a SVM based method using tripeptide composition and achieved maximum accuracy 97.66 which is better than accuracy achieved by HMM based searching (96.26). Based on above study a web-server GSTPred has been developed (http://www.imtech.res.in/raghava/gstpred/).
- Published
- 2007
39. Calculation of antibody and antigen concentrations from ELISA data using a graphical method
- Author
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Raghava, G.P.S., Joshi, A.K., and Agrewala, J.N.
- Published
- 1992
- Full Text
- View/download PDF
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