Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide

Author(s): Bowen Li, Min Li, Chunying Lu, Yifei Wu, Heng Chen* and Bifang He*

Volume 31, Issue 26, 2024

Published on: 22 January, 2024

Page: [4079 - 4099] Pages: 21

DOI: 10.2174/0109298673264218231121104407

Price: $65

Abstract

Background: Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2, which gave rise to coronavirus disease 2019 (COVID-19), has considerably impacted global health. The identification of effective anticoronavirus peptides (ACVPs) and the establishment of robust data storage methods are critical in the fight against COVID-19. Traditional wet-lab peptide discovery approaches are timeconsuming and labor-intensive. With advancements in computer technology and bioinformatics, machine learning has gained prominence in the extraction of functional peptides from extensive datasets.

Methods: In this study, we comprehensively review data resources and predictors related to ACVPs published over the past two decades. In addition, we analyze the influence of various factors on model performance.

Results: We have reviewed nine ACVP-containing databases, which integrate detailed information on protein fragments effective against coronaviruses, providing crucial references for the development of antiviral drugs and vaccines. Additionally, we have assessed 15 peptide predictors for antiviral or specifically anticoronavirus activity. These predictors employ computational models to swiftly screen potential antiviral candidates, offering an efficient pathway for drug development.

Conclusion: Our study provides conclusive results and insights into the performance of different computational methods, and sheds light on the future trajectory of bioinformatics tools for ACVPs. This work offers a representative overview of contributions to the field, with an emphasis on the crucial role of ACVPs in combating COVID-19.

Keywords: SARS-CoV-2, COVID-19, anticoronavirus peptide, machine learning, deep learning, bioinformatics tools.

[1]
Chakkour, M.; Salami, A.; Olleik, D.; Kamal, I.; Noureddine, F.Y.; Roz, A.E.; Ghssein, G. Risk markers of COVID-19, a study from South-Lebanon. COVID, 2022, 2(7), 867-876.
[http://dx.doi.org/10.3390/covid2070063]
[2]
Liu, Y.C.; Kuo, R.L.; Shih, S.R. COVID-19: The first documented coronavirus pandemic in history. Biomed. J., 2020, 43(4), 328-333.
[http://dx.doi.org/10.1016/j.bj.2020.04.007] [PMID: 32387617]
[3]
Tay, M.Z.; Poh, C.M.; Rénia, L.; MacAry, P.A.; Ng, L.F.P. The trinity of COVID-19: immunity, inflammation and intervention. Nat. Rev. Immunol., 2020, 20(6), 363-374.
[http://dx.doi.org/10.1038/s41577-020-0311-8] [PMID: 32346093]
[4]
Peiris, J.S.M.; Lai, S.T.; Poon, L.L.M.; Guan, Y.; Yam, L.Y.C.; Lim, W.; Nicholls, J.; Yee, W.K.S.; Yan, W.W.; Cheung, M.T.; Cheng, V.C.C.; Chan, K.H.; Tsang, D.N.C.; Yung, R.W.H.; Ng, T.K.; Yuen, K.Y. Coronavirus as a possible cause of severe acute respiratory syndrome. Lancet, 2003, 361(9366), 1319-1325.
[http://dx.doi.org/10.1016/S0140-6736(03)13077-2] [PMID: 12711465]
[5]
Zumla, A.; Hui, D.S.; Perlman, S. Middle East respiratory syndrome. Lancet, 2015, 386(9997), 995-1007.
[http://dx.doi.org/10.1016/S0140-6736(15)60454-8] [PMID: 26049252]
[6]
Zhou, P.; Yang, X.L.; Wang, X.G.; Hu, B.; Zhang, L.; Zhang, W.; Si, H.R.; Zhu, Y.; Li, B.; Huang, C.L.; Chen, H.D.; Chen, J.; Luo, Y.; Guo, H.; Jiang, R.D.; Liu, M.Q.; Chen, Y.; Shen, X.R.; Wang, X.; Zheng, X.S.; Zhao, K.; Chen, Q.J.; Deng, F.; Liu, L.L.; Yan, B.; Zhan, F.X.; Wang, Y.Y.; Xiao, G.F.; Shi, Z.L. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 2020, 579(7798), 270-273.
[http://dx.doi.org/10.1038/s41586-020-2012-7] [PMID: 32015507]
[7]
Yu, K.; Zhang, Q.; Liu, Z.; Du, Y.; Gao, X.; Zhao, Q.; Cheng, H.; Li, X.; Liu, Z.X. Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation. Brief. Bioinform., 2020, 21(5), 1798-1805.
[http://dx.doi.org/10.1093/bib/bbz107] [PMID: 32978618]
[8]
Noureddine, F.Y.; Chakkour, M.; El Roz, A.; Reda, J.; Al Sahily, R.; Assi, A.; Joma, M.; Salami, H.; Hashem, S.J.; Harb, B.; Salami, A.; Ghssein, G. The Emergence of SARS-CoV-2 variant(s) and its impact on the prevalence of COVID-19 cases in the Nabatieh region, Lebanon. Med. Sci., 2021, 9(2), 40.
[http://dx.doi.org/10.3390/medsci9020040] [PMID: 34199617]
[9]
Shah, M.; Woo, H.G. Molecular perspectives of SARS-CoV-2: Pathology, immune evasion, and therapeutic interventions. Mol. Cells, 2021, 44(6), 408-421.
[http://dx.doi.org/10.14348/molcells.2021.0026] [PMID: 34059561]
[10]
Sinatti, G.; Santini, S.J.; Tarantino, G.; Picchi, G.; Cosimini, B.; Ranfone, F.; Casano, N.; Zingaropoli, M.A.; Iapadre, N.; Bianconi, S.; Armiento, A.; Carducci, P.; Ciardi, M.R.; Mastroianni, C.M.; Grimaldi, A.; Balsano, C. PaO2/FiO2 ratio forecasts COVID-19 patients’ outcome regardless of age: A cross-sectional, monocentric study. Intern. Emerg. Med., 2022, 17(3), 665-673.
[http://dx.doi.org/10.1007/s11739-021-02840-7] [PMID: 34637082]
[11]
Polack, F.P.; Thomas, S.J.; Kitchin, N.; Absalon, J.; Gurtman, A.; Lockhart, S.; Perez, J.L.; Pérez Marc, G.; Moreira, E.D.; Zerbini, C.; Bailey, R.; Swanson, K.A.; Roychoudhury, S.; Koury, K.; Li, P.; Kalina, W.V.; Cooper, D.; Frenck, R.W., Jr; Hammitt, L.L.; Türeci, Ö.; Nell, H.; Schaefer, A.; Ünal, S.; Tresnan, D.B.; Mather, S.; Dormitzer, P.R.; Şahin, U.; Jansen, K.U.; Gruber, W.C.; Group, C.C.T. Safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine. N. Engl. J. Med., 2020, 383(27), 2603-2615.
[http://dx.doi.org/10.1056/NEJMoa2034577] [PMID: 33301246]
[12]
Baden, L.R.; El Sahly, H.M.; Essink, B.; Kotloff, K.; Frey, S.; Novak, R.; Diemert, D.; Spector, S.A.; Rouphael, N.; Creech, C.B.; McGettigan, J.; Khetan, S.; Segall, N.; Solis, J.; Brosz, A.; Fierro, C.; Schwartz, H.; Neuzil, K.; Corey, L.; Gilbert, P.; Janes, H.; Follmann, D.; Marovich, M.; Mascola, J.; Polakowski, L.; Ledgerwood, J.; Graham, B.S.; Bennett, H.; Pajon, R.; Knightly, C.; Leav, B.; Deng, W.; Zhou, H.; Han, S.; Ivarsson, M.; Miller, J.; Zaks, T.; Group, C.S. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N. Engl. J. Med., 2021, 384(5), 403-416.
[http://dx.doi.org/10.1056/NEJMoa2035389] [PMID: 33378609]
[13]
Voysey, M.; Clemens, S.A.C.; Madhi, S.A.; Weckx, L.Y.; Folegatti, P.M.; Aley, P.K.; Angus, B.; Baillie, V.L.; Barnabas, S.L.; Bhorat, Q.E.; Bibi, S.; Briner, C.; Cicconi, P.; Collins, A.M.; Colin-Jones, R.; Cutland, C.L.; Darton, T.C.; Dheda, K.; Duncan, C.J.A.; Emary, K.R.W.; Ewer, K.J.; Fairlie, L.; Faust, S.N.; Feng, S.; Ferreira, D.M.; Finn, A.; Goodman, A.L.; Green, C.M.; Green, C.A.; Heath, P.T.; Hill, C.; Hill, H.; Hirsch, I.; Hodgson, S.H.C.; Izu, A.; Jackson, S.; Jenkin, D.; Joe, C.C.D.; Kerridge, S.; Koen, A.; Kwatra, G.; Lazarus, R.; Lawrie, A.M.; Lelliott, A.; Libri, V.; Lillie, P.J.; Mallory, R.; Mendes, A.V.A.; Milan, E.P.; Minassian, A.M.; McGregor, A.; Morrison, H.; Mujadidi, Y.F.; Nana, A.; O’Reilly, P.J.; Padayachee, S.D.; Pittella, A.; Plested, E.; Pollock, K.M.; Ramasamy, M.N.; Rhead, S.; Schwarzbold, A.V.; Singh, N.; Smith, A.; Song, R.; Snape, M.D.; Sprinz, E.; Sutherland, R.K.; Tarrant, R.; Thomson, E.C.; Török, M.E.; Toshner, M.; Turner, D.P.J.; Vekemans, J.; Villafana, T.L.; Watson, M.E.E.; Williams, C.J.; Douglas, A.D.; Hill, A.V.S.; Lambe, T.; Gilbert, S.C.; Pollard, A.J.; Aban, M.; Abayomi, F.; Abeyskera, K.; Aboagye, J.; Adam, M.; Adams, K.; Adamson, J.; Adelaja, Y.A.; Adewetan, G.; Adlou, S.; Ahmed, K.; Akhalwaya, Y.; Akhalwaya, S.; Alcock, A.; Ali, A.; Allen, E.R.; Allen, L.; Almeida, T.C.D.S.C.; Alves, M.P.S.; Amorim, F.; Andritsou, F.; Anslow, R.; Appleby, M.; Arbe-Barnes, E.H.; Ariaans, M.P.; Arns, B.; Arruda, L.; Azi, P.; Azi, L.; Babbage, G.; Bailey, C.; Baker, K.F.; Baker, M.; Baker, N.; Baker, P.; Baldwin, L.; Baleanu, I.; Bandeira, D.; Bara, A.; Barbosa, M.A.S.; Barker, D.; Barlow, G.D.; Barnes, E.; Barr, A.S.; Barrett, J.R.; Barrett, J.; Bates, L.; Batten, A.; Beadon, K.; Beales, E.; Beckley, R.; Belij-Rammerstorfer, S.; Bell, J.; Bellamy, D.; Bellei, N.; Belton, S.; Berg, A.; Bermejo, L.; Berrie, E.; Berry, L.; Berzenyi, D.; Beveridge, A.; Bewley, K.R.; Bexhell, H.; Bhikha, S.; Bhorat, A.E.; Bhorat, Z.E.; Bijker, E.; Birch, G.; Birch, S.; Bird, A.; Bird, O.; Bisnauthsing, K.; Bittaye, M.; Blackstone, K.; Blackwell, L.; Bletchly, H.; Blundell, C.L.; Blundell, S.R.; Bodalia, P.; Boettger, B.C.; Bolam, E.; Boland, E.; Bormans, D.; Borthwick, N.; Bowring, F.; Boyd, A.; Bradley, P.; Brenner, T.; Brown, P.; Brown, C.; Brown-O’Sullivan, C.; Bruce, S.; Brunt, E.; Buchan, R.; Budd, W.; Bulbulia, Y.A.; Bull, M.; Burbage, J.; Burhan, H.; Burn, A.; Buttigieg, K.R.; Byard, N.; Cabera Puig, I.; Calderon, G.; Calvert, A.; Camara, S.; Cao, M.; Cappuccini, F.; Cardoso, J.R.; Carr, M.; Carroll, M.W.; Carson-Stevens, A.; Carvalho, Y.M.; Carvalho, J.A.M.; Casey, H.R.; Cashen, P.; Castro, T.; Castro, L.C.; Cathie, K.; Cavey, A.; Cerbino-Neto, J.; Chadwick, J.; Chapman, D.; Charlton, S.; Chelysheva, I.; Chester, O.; Chita, S.; Cho, J-S.; Cifuentes, L.; Clark, E.; Clark, M.; Clarke, A.; Clutterbuck, E.A.; Collins, S.L.K.; Conlon, C.P.; Connarty, S.; Coombes, N.; Cooper, C.; Cooper, R.; Cornelissen, L.; Corrah, T.; Cosgrove, C.; Cox, T.; Crocker, W.E.M.; Crosbie, S.; Cullen, L.; Cullen, D.; Cunha, D.R.M.F.; Cunningham, C.; Cuthbertson, F.C.; Da Guarda, S.N.F.; da Silva, L.P.; Damratoski, B.E.; Danos, Z.; Dantas, M.T.D.C.; Darroch, P.; Datoo, M.S.; Datta, C.; Davids, M.; Davies, S.L.; Davies, H.; Davis, E.; Davis, J.; Davis, J.; De Nobrega, M.M.D.; De Oliveira Kalid, L.M.; Dearlove, D.; Demissie, T.; Desai, A.; Di Marco, S.; Di Maso, C.; Dinelli, M.I.S.; Dinesh, T.; Docksey, C.; Dold, C.; Dong, T.; Donnellan, F.R.; Dos Santos, T.; dos Santos, T.G.; Dos Santos, E.P.; Douglas, N.; Downing, C.; Drake, J.; Drake-Brockman, R.; Driver, K.; Drury, R.; Dunachie, S.J.; Durham, B.S.; Dutra, L.; Easom, N.J.W.; van Eck, S.; Edwards, M.; Edwards, N.J.; El Muhanna, O.M.; Elias, S.C.; Elmore, M.; English, M.; Esmail, A.; Essack, Y.M.; Farmer, E.; Farooq, M.; Farrar, M.; Farrugia, L.; Faulkner, B.; Fedosyuk, S.; Felle, S.; Feng, S.; Ferreira Da Silva, C.; Field, S.; Fisher, R.; Flaxman, A.; Fletcher, J.; Fofie, H.; Fok, H.; Ford, K.J.; Fowler, J.; Fraiman, P.H.A.; Francis, E.; Franco, M.M.; Frater, J.; Freire, M.S.M.; Fry, S.H.; Fudge, S.; Furze, J.; Fuskova, M.; Galian-Rubio, P.; Galiza, E.; Garlant, H.; Gavrila, M.; Geddes, A.; Gibbons, K.A.; Gilbride, C.; Gill, H.; Glynn, S.; Godwin, K.; Gokani, K.; Goldoni, U.C.; Goncalves, M.; Gonzalez, I.G.S.; Goodwin, J.; Goondiwala, A.; Gordon-Quayle, K.; Gorini, G.; Grab, J.; Gracie, L.; Greenland, M.; Greenwood, N.; Greffrath, J.; Groenewald, M.M.; Grossi, L.; Gupta, G.; Hackett, M.; Hallis, B.; Hamaluba, M.; Hamilton, E.; Hamlyn, J.; Hammersley, D.; Hanrath, A.T.; Hanumunthadu, B.; Harris, S.A.; Harris, C.; Harris, T.; Harrison, T.D.; Harrison, D.; Hart, T.C.; Hartnell, B.; Hassan, S.; Haughney, J.; Hawkins, S.; Hay, J.; Head, I.; Henry, J.; Hermosin Herrera, M.; Hettle, D.B.; Hill, J.; Hodges, G.; Horne, E.; Hou, M.M.; Houlihan, C.; Howe, E.; Howell, N.; Humphreys, J.; Humphries, H.E.; Hurley, K.; Huson, C.; Hyder-Wright, A.; Hyams, C.; Ikram, S.; Ishwarbhai, A.; Ivan, M.; Iveson, P.; Iyer, V.; Jackson, F.; De Jager, J.; Jaumdally, S.; Jeffers, H.; Jesudason, N.; Jones, B.; Jones, K.; Jones, E.; Jones, C.; Jorge, M.R.; Jose, A.; Joshi, A.; Júnior, E.A.M.S.; Kadziola, J.; Kailath, R.; Kana, F.; Karampatsas, K.; Kasanyinga, M.; Keen, J.; Kelly, E.J.; Kelly, D.M.; Kelly, D.; Kelly, S.; Kerr, D.; Kfouri, R.Á.; Khan, L.; Khozoee, B.; Kidd, S.; Killen, A.; Kinch, J.; Kinch, P.; King, L.D.W.; King, T.B.; Kingham, L.; Klenerman, P.; Knapper, F.; Knight, J.C.; Knott, D.; Koleva, S.; Lang, M.; Lang, G.; Larkworthy, C.W.; Larwood, J.P.J.; Law, R.; Lazarus, E.M.; Leach, A.; Lees, E.A.; Lemm, N-M.; Lessa, A.; Leung, S.; Li, Y.; Lias, A.M.; Liatsikos, K.; Linder, A.; Lipworth, S.; Liu, S.; Liu, X.; Lloyd, A.; Lloyd, S.; Loew, L.; Lopez Ramon, R.; Lora, L.; Lowthorpe, V.; Luz, K.; MacDonald, J.C.; MacGregor, G.; Madhavan, M.; Mainwaring, D.O.; Makambwa, E.; Makinson, R.; Malahleha, M.; Malamatsho, R.; Mallett, G.; Mansatta, K.; Maoko, T.; Mapetla, K.; Marchevsky, N.G.; Marinou, S.; Marlow, E.; Marques, G.N.; Marriott, P.; Marshall, R.P.; Marshall, J.L.; Martins, F.J.; Masenya, M.; Masilela, M.; Masters, S.K.; Mathew, M.; Matlebjane, H.; Matshidiso, K.; Mazur, O.; Mazzella, A.; McCaughan, H.; McEwan, J.; McGlashan, J.; McInroy, L.; McIntyre, Z.; McLenaghan, D.; McRobert, N.; McSwiggan, S.; Megson, C.; Mehdipour, S.; Meijs, W.; Mendonça, R.N.Á.; Mentzer, A.J.; Mirtorabi, N.; Mitton, C.; Mnyakeni, S.; Moghaddas, F.; Molapo, K.; Moloi, M.; Moore, M.; Moraes-Pinto, M.I.; Moran, M.; Morey, E.; Morgans, R.; Morris, S.; Morris, S.; Morris, H.C.; Morselli, F.; Morshead, G.; Morter, R.; Mottal, L.; Moultrie, A.; Moya, N.; Mpelembue, M.; Msomi, S.; Mugodi, Y.; Mukhopadhyay, E.; Muller, J.; Munro, A.; Munro, C.; Murphy, S.; Mweu, P.; Myasaki, C.H.; Naik, G.; Naker, K.; Nastouli, E.; Nazir, A.; Ndlovu, B.; Neffa, F.; Njenga, C.; Noal, H.; Noé, A.; Novaes, G.; Nugent, F.L.; Nunes, G.; O’Brien, K.; O’Connor, D.; Odam, M.; Oelofse, S.; Oguti, B.; Olchawski, V.; Oldfield, N.J.; Oliveira, M.G.; Oliveira, C.; Oosthuizen, A.; O’Reilly, P.; Osborne, P.; Owen, D.R.J.; Owen, L.; Owens, D.; Owino, N.; Pacurar, M.; Paiva, B.V.B.; Palhares, E.M.F.; Palmer, S.; Parkinson, S.; Parracho, H.M.R.T.; Parsons, K.; Patel, D.; Patel, B.; Patel, F.; Patel, K.; Patrick-Smith, M.; Payne, R.O.; Peng, Y.; Penn, E.J.; Pennington, A.; Peralta Alvarez, M.P.; Perring, J.; Perry, N.; Perumal, R.; Petkar, S.; Philip, T.; Phillips, D.J.; Phillips, J.; Phohu, M.K.; Pickup, L.; Pieterse, S.; Piper, J.; Pipini, D.; Plank, M.; Du Plessis, J.; Pollard, S.; Pooley, J.; Pooran, A.; Poulton, I.; Powers, C.; Presa, F.B.; Price, D.A.; Price, V.; Primeira, M.; Proud, P.C.; Provstgaard-Morys, S.; Pueschel, S.; Pulido, D.; Quaid, S.; Rabara, R.; Radford, A.; Radia, K.; Rajapaska, D.; Rajeswaran, T.; Ramos, A.S.F.; Ramos Lopez, F.; Rampling, T.; Rand, J.; Ratcliffe, H.; Rawlinson, T.; Rea, D.; Rees, B.; Reiné, J.; Resuello-Dauti, M.; Reyes Pabon, E.; Ribiero, C.M.; Ricamara, M.; Richter, A.; Ritchie, N.; Ritchie, A.J.; Robbins, A.J.; Roberts, H.; Robinson, R.E.; Robinson, H.; Rocchetti, T.T.; Rocha, B.P.; Roche, S.; Rollier, C.; Rose, L.; Ross Russell, A.L.; Rossouw, L.; Royal, S.; Rudiansyah, I.; Ruiz, S.; Saich, S.; Sala, C.; Sale, J.; Salman, A.M.; Salvador, N.; Salvador, S.; Sampaio, M.; Samson, A.D.; Sanchez-Gonzalez, A.; Sanders, H.; Sanders, K.; Santos, E.; Santos Guerra, M.F.S.; Satti, I.; Saunders, J.E.; Saunders, C.; Sayed, A.; Schim van der Loeff, I.; Schmid, A.B.; Schofield, E.; Screaton, G.; Seddiqi, S.; Segireddy, R.R.; Senger, R.; Serrano, S.; Shah, R.; Shaik, I.; Sharpe, H.E.; Sharrocks, K.; Shaw, R.; Shea, A.; Shepherd, A.; Shepherd, J.G.; Shiham, F.; Sidhom, E.; Silk, S.E.; da Silva Moraes, A.C.; Silva-Junior, G.; Silva-Reyes, L.; Silveira, A.D.; Silveira, M.B.V.; Sinha, J.; Skelly, D.T.; Smith, D.C.; Smith, N.; Smith, H.E.; Smith, D.J.; Smith, C.C.; Soares, A.; Soares, T.; Solórzano, C.; Sorio, G.L.; Sorley, K.; Sosa-Rodriguez, T.; Souza, C.M.C.D.L.; Souza, B.S.D.F.; Souza, A.R.; Spencer, A.J.; Spina, F.; Spoors, L.; Stafford, L.; Stamford, I.; Starinskij, I.; Stein, R.; Steven, J.; Stockdale, L.; Stockwell, L.V.; Strickland, L.H.; Stuart, A.C.; Sturdy, A.; Sutton, N.; Szigeti, A.; Tahiri-Alaoui, A.; Tanner, R.; Taoushanis, C.; Tarr, A.W.; Taylor, K.; Taylor, U.; Taylor, I.J.; Taylor, J.; te Water Naude, R.; Themistocleous, Y.; Themistocleous, A.; Thomas, M.; Thomas, K.; Thomas, T.M.; Thombrayil, A.; Thompson, F.; Thompson, A.; Thompson, K.; Thompson, A.; Thomson, J.; Thornton-Jones, V.; Tighe, P.J.; Tinoco, L.A.; Tiongson, G.; Tladinyane, B.; Tomasicchio, M.; Tomic, A.; Tonks, S.; Towner, J.; Tran, N.; Tree, J.; Trillana, G.; Trinham, C.; Trivett, R.; Truby, A.; Tsheko, B.L.; Turabi, A.; Turner, R.; Turner, C.; Ulaszewska, M.; Underwood, B.R.; Varughese, R.; Verbart, D.; Verheul, M.; Vichos, I.; Vieira, T.; Waddington, C.S.; Walker, L.; Wallis, E.; Wand, M.; Warbick, D.; Wardell, T.; Warimwe, G.; Warren, S.C.; Watkins, B.; Watson, E.; Webb, S.; Webb-Bridges, A.; Webster, A.; Welch, J.; Wells, J.; West, A.; White, C.; White, R.; Williams, P.; Williams, R.L.; Winslow, R.; Woodyer, M.; Worth, A.T.; Wright, D.; Wroblewska, M.; Yao, A.; Zimmer, R.; Zizi, D.; Zuidewind, P. Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: An interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK. Lancet, 2021, 397(10269), 99-111.
[http://dx.doi.org/10.1016/S0140-6736(20)32661-1] [PMID: 33306989]
[14]
Sadoff, J.; Gray, G.; Vandebosch, A.; Cárdenas, V.; Shukarev, G.; Grinsztejn, B.; Goepfert, P.A.; Truyers, C.; Fennema, H.; Spiessens, B.; Offergeld, K.; Scheper, G.; Taylor, K.L.; Robb, M.L.; Treanor, J.; Barouch, D.H.; Stoddard, J.; Ryser, M.F.; Marovich, M.A.; Neuzil, K.M.; Corey, L.; Cauwenberghs, N.; Tanner, T.; Hardt, K.; Ruiz-Guiñazú, J.; Le Gars, M.; Schuitemaker, H.; Van Hoof, J.; Struyf, F.; Douoguih, M.; Group, E.S. Safety and efficacy of single-dose Ad26.COV2.S vaccine against COVID-19. N. Engl. J. Med., 2021, 384(23), 2187-2201.
[http://dx.doi.org/10.1056/NEJMoa2101544] [PMID: 33882225]
[15]
Barouch, D.H.; Stephenson, K.E.; Sadoff, J.; Yu, J.; Chang, A.; Gebre, M.; McMahan, K.; Liu, J.; Chandrashekar, A.; Patel, S.; Le Gars, M.; de Groot, A.M.; Heerwegh, D.; Struyf, F.; Douoguih, M.; van Hoof, J.; Schuitemaker, H. Durable humoral and cellular immune responses 8 months after Ad26.COV2.S vaccination. N. Engl. J. Med., 2021, 385(10), 951-953.
[http://dx.doi.org/10.1056/NEJMc2108829] [PMID: 34260834]
[16]
Tannock, G.A.; Kim, H.; Xue, L. Why are vaccines against many human viral diseases still unavailable; an historic perspective? J. Med. Virol., 2020, 92(2), 129-138.
[http://dx.doi.org/10.1002/jmv.25593] [PMID: 31502669]
[17]
Marqus, S.; Pirogova, E.; Piva, T.J. Evaluation of the use of therapeutic peptides for cancer treatment. J. Biomed. Sci., 2017, 24(1), 21.
[http://dx.doi.org/10.1186/s12929-017-0328-x] [PMID: 28320393]
[18]
Craik, D.J.; Fairlie, D.P.; Liras, S.; Price, D. The future of peptide-based drugs. Chem. Biol. Drug Des., 2013, 81(1), 136-147.
[http://dx.doi.org/10.1111/cbdd.12055] [PMID: 23253135]
[19]
Zhang, Q.; Chen, X.; Li, B.; Lu, C.; Yang, S.; Long, J.; Chen, H.; Huang, J.; He, B. A database of anti-coronavirus peptides. Sci. Data, 2022, 9(1), 294.
[http://dx.doi.org/10.1038/s41597-022-01394-3] [PMID: 35697698]
[20]
Wang, Z.; Wang, G. APD: The antimicrobial peptide database. Nucleic Acids Res., 2004, 32(90001), 590D-592.
[http://dx.doi.org/10.1093/nar/gkh025] [PMID: 14681488]
[21]
Wang, G.; Li, X.; Wang, Z. APD2: the updated antimicrobial peptide database and its application in peptide design. Nucleic Acids Res., 2009, 37(Database issue), D933-D937.
[http://dx.doi.org/10.1093/nar/gkn823] [PMID: 18957441]
[22]
Wang, G.; Li, X.; Wang, Z. APD3: The antimicrobial peptide database as a tool for research and education. Nucleic Acids Res., 2016, 44(D1), D1087-D1093.
[http://dx.doi.org/10.1093/nar/gkv1278] [PMID: 26602694]
[23]
Thomas, S.; Karnik, S.; Barai, R.S.; Jayaraman, V.K.; Idicula-Thomas, S. CAMP: A useful resource for research on antimicrobial peptides. Nucleic Acids Res., 2010, 38(Database issue), D774-D780.
[http://dx.doi.org/10.1093/nar/gkp1021] [PMID: 19923233]
[24]
Waghu, F.H.; Gopi, L.; Barai, R.S.; Ramteke, P.; Nizami, B.; Idicula-Thomas, S. CAMP: Collection of sequences and structures of antimicrobial peptides. Nucleic Acids Res., 2014, 42(D1), D1154-D1158.
[http://dx.doi.org/10.1093/nar/gkt1157] [PMID: 24265220]
[25]
Waghu, F.H.; Barai, R.S.; Gurung, P.; Idicula-Thomas, S. CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res., 2016, 44(D1), D1094-D1097.
[http://dx.doi.org/10.1093/nar/gkv1051] [PMID: 26467475]
[26]
Gawde, U.; Chakraborty, S.; Waghu, F.H.; Barai, R.S.; Khanderkar, A.; Indraguru, R.; Shirsat, T.; Idicula-Thomas, S. CAMPR4: A database of natural and synthetic antimicrobial peptides. Nucleic Acids Res., 2023, 51(D1), D377-D383.
[http://dx.doi.org/10.1093/nar/gkac933] [PMID: 36370097]
[27]
Thakur, N.; Qureshi, A.; Kumar, M. AVPpred: Collection and prediction of highly effective antiviral peptides. Nucleic Acids Res., 2012, 40, W199-W204.
[http://dx.doi.org/10.1093/nar/gks450] [PMID: 22638580]
[28]
Zhao, X.; Wu, H.; Lu, H.; Li, G.; Huang, Q. LAMP: A database linking antimicrobial peptides. PLoS One, 2013, 8(6), e66557.
[http://dx.doi.org/10.1371/journal.pone.0066557] [PMID: 23825543]
[29]
Gogoladze, G.; Grigolava, M.; Vishnepolsky, B.; Chubinidze, M.; Duroux, P.; Lefranc, M.P.; Pirtskhalava, M. DBAASP: Database of antimicrobial activity and structure of peptides. FEMS Microbiol. Lett., 2014, 357(1), 63-68.
[http://dx.doi.org/10.1111/1574-6968.12489] [PMID: 24888447]
[30]
Pirtskhalava, M.; Gabrielian, A.; Cruz, P.; Griggs, H.L.; Squires, R.B.; Hurt, D.E.; Grigolava, M.; Chubinidze, M.; Gogoladze, G.; Vishnepolsky, B.; Alekseev, V.; Rosenthal, A.; Tartakovsky, M. DBAASP v.2: An enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides. Nucleic Acids Res., 2016, 44(D1), D1104-D1112.
[http://dx.doi.org/10.1093/nar/gkv1174] [PMID: 26578581]
[31]
Pirtskhalava, M.; Amstrong, A.A.; Grigolava, M.; Chubinidze, M.; Alimbarashvili, E.; Vishnepolsky, B.; Gabrielian, A.; Rosenthal, A.; Hurt, D.E.; Tartakovsky, M. DBAASP v3: Database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Res., 2021, 49(D1), D288-D297.
[http://dx.doi.org/10.1093/nar/gkaa991] [PMID: 33151284]
[32]
Qureshi, A.; Thakur, N.; Tandon, H.; Kumar, M. AVPdb: A database of experimentally validated antiviral peptides targeting medically important viruses. Nucleic Acids Res., 2014, 42(D1), D1147-D1153.
[http://dx.doi.org/10.1093/nar/gkt1191] [PMID: 24285301]
[33]
Fan, L.; Sun, J.; Zhou, M.; Zhou, J.; Lao, X.; Zheng, H.; Xu, H. DRAMP: A comprehensive data repository of antimicrobial peptides. Sci. Rep., 2016, 6(1), 24482.
[http://dx.doi.org/10.1038/srep24482] [PMID: 27075512]
[34]
Kang, X.; Dong, F.; Shi, C.; Liu, S.; Sun, J.; Chen, J.; Li, H.; Xu, H.; Lao, X.; Zheng, H. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci. Data, 2019, 6(1), 148.
[http://dx.doi.org/10.1038/s41597-019-0154-y] [PMID: 31409791]
[35]
Shi, G.; Kang, X.; Dong, F.; Liu, Y.; Zhu, N.; Hu, Y.; Xu, H.; Lao, X.; Zheng, H. DRAMP 3.0: An enhanced comprehensive data repository of antimicrobial peptides. Nucleic Acids Res., 2022, 50(D1), D488-D496.
[http://dx.doi.org/10.1093/nar/gkab651] [PMID: 34390348]
[36]
Jhong, J.H.; Chi, Y.H.; Li, W.C.; Lin, T.H.; Huang, K.Y.; Lee, T.Y. dbAMP: An integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data. Nucleic Acids Res., 2019, 47(D1), D285-D297.
[http://dx.doi.org/10.1093/nar/gky1030] [PMID: 30380085]
[37]
Jhong, J.H.; Yao, L.; Pang, Y.; Li, Z.; Chung, C.R.; Wang, R.; Li, S.; Li, W.; Luo, M.; Ma, R.; Huang, Y.; Zhu, X.; Zhang, J.; Feng, H.; Cheng, Q.; Wang, C.; Xi, K.; Wu, L.C.; Chang, T.H.; Horng, J.T.; Zhu, L.; Chiang, Y.C.; Wang, Z.; Lee, T.Y. dbAMP 2.0: Updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data. Nucleic Acids Res., 2022, 50(D1), D460-D470.
[http://dx.doi.org/10.1093/nar/gkab1080] [PMID: 34850155]
[38]
Timmons, P.B.; Hewage, C.M. ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides. Brief. Bioinform., 2021, 22(6), bbab258.
[http://dx.doi.org/10.1093/bib/bbab258] [PMID: 34297817]
[39]
Kurata, H.; Tsukiyama, S.; Manavalan, B. iACVP: Markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model. Brief. Bioinform., 2022, 23(4), bbac265.
[http://dx.doi.org/10.1093/bib/bbac265] [PMID: 35772910]
[40]
Chang, K.Y.; Yang, J.R. Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS One, 2013, 8(8), e70166.
[http://dx.doi.org/10.1371/journal.pone.0070166] [PMID: 23940542]
[41]
Beltrán Lissabet, J.F.; Belén, L.H.; Farias, J.G. AntiVPP 1.0: A portable tool for prediction of antiviral peptides. Comput. Biol. Med., 2019, 107, 127-130.
[http://dx.doi.org/10.1016/j.compbiomed.2019.02.011] [PMID: 30802694]
[42]
Schaduangrat, N.; Nantasenamat, C.; Prachayasittikul, V.; Shoombuatong, W. Meta-iAVP: A sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation. Int. J. Mol. Sci., 2019, 20(22), 5743.
[http://dx.doi.org/10.3390/ijms20225743] [PMID: 31731751]
[43]
Wei, L.; Zhou, C.; Su, R.; Zou, Q. PEPred-Suite: Improved and robust prediction of therapeutic peptides using adaptive feature representation learning. Bioinformatics, 2019, 35(21), 4272-4280.
[http://dx.doi.org/10.1093/bioinformatics/btz246] [PMID: 30994882]
[44]
Chowdhury, A.S.; Reehl, S.M.; Kehn-Hall, K.; Bishop, B.; Webb-Robertson, B.J.M. Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance. Sci. Rep., 2020, 10(1), 19260.
[http://dx.doi.org/10.1038/s41598-020-76161-8] [PMID: 33159146]
[45]
Li, J.; Pu, Y.; Tang, J.; Zou, Q.; Guo, F. DeepAVP: A dual-channel deep neural network for identifying variable-length antiviral peptides. IEEE J. Biomed. Health Inform., 2020, 24(10), 3012-3019.
[http://dx.doi.org/10.1109/JBHI.2020.2977091] [PMID: 32142462]
[46]
Zhang, Y.P.; Zou, Q. PPTPP: A novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning. Bioinformatics, 2020, 36(13), 3982-3987.
[http://dx.doi.org/10.1093/bioinformatics/btaa275] [PMID: 32348463]
[47]
Cortes, C.; Vapnik, V.; Vapnik, V.; Llorens, C.; Vapnik, V.N.; Cortes, C. Support-vector networks. Mach. Learn., 1995, 20(3), 273-297.
[http://dx.doi.org/10.1007/BF00994018]
[48]
Breiman, L. Random forests. Mach. Learn., 2001, 45(1), 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[49]
Zare, M.; Mohabatkar, H.; Faramarzi, F.; Beigi, M.M.; Behbahani, M.J.T.O.B.J. Using chou’s pseudo amino acid composition and machine learningmethod to predict the antiviral peptides. Open Bioinform.atics J., 2015, 9, 13-19.
[50]
Freund, Y. A short introduction to boosting. J. Japanese Soci. Artif. Intell., 1999, 14(5), 771-780.
[51]
Graves, A.; Schmidhuber, J. IEEE International Joint Conference on Neural Networks., 2005.
[52]
Lecun, Y.; Bottou, L.J.P.o.t.I. Gradient-based learning applied to document recognition. Proc. IEEE, 1998, 86(11), 2278-2324.
[http://dx.doi.org/10.1109/5.726791]
[53]
Pang, Y.; Wang, Z.; Jhong, J.H.; Lee, T.Y. Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies. Brief. Bioinform., 2021, 22(2), 1085-1095.
[http://dx.doi.org/10.1093/bib/bbaa423] [PMID: 33497434]
[54]
Tyagi, A.; Tuknait, A.; Anand, P.; Gupta, S.; Sharma, M.; Mathur, D.; Joshi, A.; Singh, S.; Gautam, A.; Raghava, G.P.S.; Cancer, P.P.D. CancerPPD: A database of anticancer peptides and proteins. Nucleic Acids Res., 2015, 43(D1), D837-D843.
[http://dx.doi.org/10.1093/nar/gku892] [PMID: 25270878]
[55]
Agrawal, P.; Bhalla, S.; Chaudhary, K.; Kumar, R.; Sharma, M.; Raghava, G.P.S. In silico approach for prediction of antifungal peptides. Front. Microbiol., 2018, 9, 323.
[http://dx.doi.org/10.3389/fmicb.2018.00323] [PMID: 29535692]
[56]
Kaushik, A.C.; Mehmood, A.; Selvaraj, G.; Dai, X.; Pan, Y.; Wei, D.Q. CoronaPep: An anti-coronavirus peptide generation tool. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2021, 18(4), 1299-1304.
[http://dx.doi.org/10.1109/TCBB.2021.3064630] [PMID: 33687847]
[57]
Pang, Y.; Yao, L.; Jhong, J.H.; Wang, Z.; Lee, T.Y. AVPIden: A new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches. Brief. Bioinform., 2021, 22(6), bbab263.
[http://dx.doi.org/10.1093/bib/bbab263] [PMID: 34279599]
[58]
Qureshi, A.; Thakur, N.; Kumar, M. HIPdb: A database of experimentally validated HIV inhibiting peptides. PLoS One, 2013, 8(1), e54908.
[http://dx.doi.org/10.1371/journal.pone.0054908] [PMID: 23359817]
[59]
Mcculloch, W.S.; Pitts, W.H. A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophy., 1942, 5, 115-133.
[PMID: 2185863]
[60]
Manavalan, B.; Basith, S.; Lee, G. Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2. Brief. Bioinform., 2022, 23(1), bbab412.
[http://dx.doi.org/10.1093/bib/bbab412] [PMID: 34595489]
[61]
Sharma, R.; Shrivastava, S.; Singh, S.K.; Kumar, A.; Singh, A.K.; Saxena, S. Deep-AVPpred: Artificial intelligence driven discovery of peptide drugs for viral infections. IEEE J. Biomed. Health Inform., 2022, 26(10), 5067-5074.
[http://dx.doi.org/10.1109/JBHI.2021.3130825] [PMID: 34822333]
[62]
Singh, S.; Chaudhary, K.; Dhanda, S.K.; Bhalla, S.; Usmani, S.S.; Gautam, A.; Tuknait, A.; Agrawal, P.; Mathur, D.; Raghava, G.P.S. SATPdb: A database of structurally annotated therapeutic peptides. Nucleic Acids Res., 2016, 44(D1), D1119-D1126.
[http://dx.doi.org/10.1093/nar/gkv1114] [PMID: 26527728]
[63]
Aguilera-Mendoza, L.; Marrero-Ponce, Y.; Beltran, J.A.; Tellez Ibarra, R.; Guillen-Ramirez, H.A.; Brizuela, C.A. Graph-based data integration from bioactive peptide databases of pharmaceutical interest: Toward an organized collection enabling visual network analysis. Bioinformatics, 2019, 35(22), 4739-4747.
[http://dx.doi.org/10.1093/bioinformatics/btz260] [PMID: 30994884]
[64]
Sharma, R.; Shrivastava, S.; Kumar Singh, S.; Kumar, A.; Saxena, S.; Kumar Singh, R. Deep-ABPpred: Identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec. Brief. Bioinform., 2021, 22(5), bbab065.
[http://dx.doi.org/10.1093/bib/bbab065] [PMID: 33784381]
[65]
Sharma, R.; Shrivastava, S.; Kumar Singh, S.; Kumar, A.; Saxena, S.; Kumar Singh, R. AniAMPpred: Artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom. Brief. Bioinform., 2021, 22(6), bbab242.
[http://dx.doi.org/10.1093/bib/bbab242] [PMID: 34259329]
[66]
Sharma, R.; Shrivastava, S.; Kumar Singh, S.; Kumar, A.; Saxena, S.; Kumar Singh, R. Deep-AFPpred: Identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM. Brief. Bioinform., 2022, 23(1), bbab422.
[http://dx.doi.org/10.1093/bib/bbab422] [PMID: 34670278]
[67]
Singh, V.; Shrivastava, S.; Kumar Singh, S.; Kumar, A.; Saxena, S. StaBle-ABPpred: A stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides. Brief. Bioinform., 2022, 23(1), bbab439.
[http://dx.doi.org/10.1093/bib/bbab439] [PMID: 34750606]
[68]
Mclachlan, G.J. Discriminant Analysis and Statistical Pattern Recognition; Wiley, 2004.
[http://dx.doi.org/10.1002/0471725293]
[69]
Li, W.; Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006, 22(13), 1658-1659.
[http://dx.doi.org/10.1093/bioinformatics/btl158] [PMID: 16731699]
[70]
Fu, L.; Niu, B.; Zhu, Z.; Wu, S.; Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 28(23), 3150-3152.
[http://dx.doi.org/10.1093/bioinformatics/bts565] [PMID: 23060610]
[71]
Huang, Y.; Niu, B.; Gao, Y.; Fu, L.; Li, W.; Suite, C.D-H.I.T. CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics, 2010, 26(5), 680-682.
[http://dx.doi.org/10.1093/bioinformatics/btq003] [PMID: 20053844]
[72]
Lin, S.X.; Lapointe, J. Theoretical and experimental biology in one-A symposium in honour of Professor Kuo-Chen Chou’s 50th anniversary and Professor Richard Giegé’s 40th anniversary of their scientific careers. J. Biomed. Sci. Eng., 2013, 6(4), 435-442.
[http://dx.doi.org/10.4236/jbise.2013.64054]
[73]
Freund, Y.; Schapire, R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Sys. Sci., 1997, 55(1), 119-139.
[74]
Ramesh, V.; Parkavi, P.; Yasodha, P. Performance analysis of data mining techniques for placement chance prediction. Int. J. Sci. Eng. Res., 2011, 2(8), 2229-5518.
[75]
Sakamoto, T.; Uehara, K. Induction of N-level decision trees. Transac. Inform. Proc. Soc. Japan, 1997, 38, 419-428.
[76]
Ali, S.; Smith, K.A. On learning algorithm selection for classification. Appl. Soft Comput., 2006, 6(2), 119-138.
[http://dx.doi.org/10.1016/j.asoc.2004.12.002]
[77]
Altman, N.S. An introduction to kernel and nearest-neighbor nonparametric regression. Am. Strltist., 1992, 46(3), 1-12.
[78]
Xiao, N.; Cao, D.S.; Zhu, M.F.; Xu, Q.S. protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics, 2015, 31(11), 1857-1859.
[http://dx.doi.org/10.1093/bioinformatics/btv042] [PMID: 25619996]
[79]
Friedman, J.H. Greedy function approximation: A gradient boosting machine. Ann. Stat., 2001, 29(5), 29.
[http://dx.doi.org/10.1214/aos/1013203451]
[80]
Ettayapuram Ramaprasad, A.S.; Singh, S.; Gajendra P S, R.; Venkatesan, S. AntiAngioPred: A server for prediction of anti-angiogenic peptides. PLoS One, 2015, 10(9), e0136990.
[http://dx.doi.org/10.1371/journal.pone.0136990] [PMID: 26335203]
[81]
Lata, S.; Sharma, B.K.; Raghava, G.P.S. Analysis and prediction of antibacterial peptides. BMC Bioinformatics, 2007, 8(1), 263.
[http://dx.doi.org/10.1186/1471-2105-8-263] [PMID: 17645800]
[82]
Wei, L.; Zhou, C.; Chen, H.; Song, J.; Su, R. ACPred-FL: A sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics, 2018, 34(23), 4007-4016.
[http://dx.doi.org/10.1093/bioinformatics/bty451] [PMID: 29868903]
[83]
Manavalan, B.; Shin, T.H.; Kim, M.O.; Lee, G. AIPpred: Sequence-based prediction of anti-inflammatory peptides using random forest. Front. Pharmacol., 2018, 9, 276.
[http://dx.doi.org/10.3389/fphar.2018.00276] [PMID: 29636690]
[84]
Wei, L.; Xing, P.; Su, R.; Shi, G.; Ma, Z.S.; Zou, Q. CPPred-RF: A sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. J. Proteome Res., 2017, 16(5), 2044-2053.
[http://dx.doi.org/10.1021/acs.jproteome.7b00019] [PMID: 28436664]
[85]
Rajput, A.; Gupta, A.K.; Kumar, M. Prediction and analysis of quorum sensing peptides based on sequence features. PLoS One, 2015, 10(3), e0120066.
[http://dx.doi.org/10.1371/journal.pone.0120066] [PMID: 25781990]
[86]
Li, N.; Kang, J.; Jiang, L.; He, B.; Lin, H.; Huang, J. PSBinder: A web service for predicting polystyrene surface-binding peptides. BioMed Res. Int., 2017, 2017, 1-5.
[http://dx.doi.org/10.1155/2017/5761517] [PMID: 29445741]
[87]
Hanchuan Peng; Fuhui Long; Ding, C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(8), 1226-1238.
[http://dx.doi.org/10.1109/TPAMI.2005.159] [PMID: 16119262]
[88]
McGraw, R.; Zhang, R. Multivariate analysis of homogeneous nucleation rate measurements. Nucleation in the p-toluic acid/sulfuric acid/water system. J. Chem. Phys., 2008, 128(6), 064508.
[http://dx.doi.org/10.1063/1.2830030] [PMID: 18282057]
[89]
Wei, L.; Xing, P.; Shi, G.; Ji, Z.; Zou, Q. Fast prediction of protein methylation sites using a sequence-based feature selection technique. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2019, 16(4), 1264-1273.
[http://dx.doi.org/10.1109/TCBB.2017.2670558] [PMID: 28222000]
[90]
Rao, B.; Zhou, C.; Zhang, G.; Su, R.; Wei, L. ACPred-Fuse: Fusing multi-view information improves the prediction of anticancer peptides. Brief. Bioinform., 2020, 21(5), 1846-1855.
[http://dx.doi.org/10.1093/bib/bbz088] [PMID: 31729528]
[91]
Zou, Q.; Zeng, J.; Cao, L.; Ji, R. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing, 2016, 173, 346-354.
[http://dx.doi.org/10.1016/j.neucom.2014.12.123]
[92]
Lata, S.; Mishra, N.K.; Raghava, G.P. AntiBP2: Improved version of antibacterial peptide prediction. BMC Bioinforma., 2010, 11(S1), S19.
[http://dx.doi.org/10.1186/1471-2105-11-S1-S19] [PMID: 20122190]
[93]
Thakur, N.; Qureshi, A.; Kumar, M. VIRsiRNAdb: A curated database of experimentally validated viral siRNA/shRNA. Nucleic Acids Res., 2012, 40(D1), D230-D236.
[http://dx.doi.org/10.1093/nar/gkr1147] [PMID: 22139916]
[94]
Boopathi, V.; Subramaniyam, S.; Malik, A.; Lee, G.; Manavalan, B.; Yang, D.C. mACPpred: A support vector machine-based meta-predictor for identification of anticancer peptides. Int. J. Mol. Sci., 2019, 20(8), 1964.
[http://dx.doi.org/10.3390/ijms20081964] [PMID: 31013619]
[95]
Frank, E.; Hall, M.; Trigg, L.; Holmes, G.; Witten, I.H. Data mining in bioinformatics using Weka. Bioinformatics, 2004, 20(15), 2479-2481.
[http://dx.doi.org/10.1093/bioinformatics/bth261] [PMID: 15073010]
[96]
Abouelenien, M.; Yuan, X.; Duraisamy, P.; Yuan, X. Improving classification performance for the minority class in highly imbalanced dataset using boosting. Third International Conference on Computing Communication & Networking Technologies., 2013.
[97]
D Richard, C. Random forests for classification in ecology. Ecology, 2007, 88(11), 2783-2792.
[98]
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, L.; Polosukhin, I.J.A. Attention is all you need. arXiv, 2017, 2017, 1706.03762.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy