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2. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T, Bankapur, Asma R, D'Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S, Kahanda, Indika, Verspoor, Karin M, Ben-Hur, Asa, Koo, Emily, Penfold-Brown, Duncan, Shasha, Dennis, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed ME, Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nives, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T, Chapman, Samuel, C., Dukka B K., Khan, Ishita K, Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amos, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E, Hieta, Reija, Legge, Duncan, Lovering, Ruth C, Magrane, Michele, Melidoni, Anna N, Mutowo-Meullenet, Prudence, Pichler, Klemens, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, ElShal, Sarah, Tranchevent, Léon-Charles, Das, Sayoni, Dawson, Natalie L, Lee, David, Lees, Jonathan G, Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamás, Romero, Alfonso E, Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E, Pavlidis, Paul, Feng, Shou, Cejuela, Juan M, Goldberg, Tatyana, Hamp, Tobias, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio, del Pozo, Angela, Fernández, José M, Maietta, Paolo, Valencia, Alfonso, Tress, Michael L, Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco, Savino, Alessandro, Rehman, Hafeez Ur, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W, van Dijk, Aalt DJ, Gemovic, Branislava, Glisic, Sanja, Perovic, Vladmir, Veljkovic, Veljko, Veljkovic, Nevena, Almeida-e-Silva, Danillo C, Vencio, Ricardo ZN, Sharan, Malvika, Vogel, Jörg, Kansakar, Lakesh, Zhang, Shanshan, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael JE, Wass, Mark N, Huntley, Rachael P, Martin, Maria J, O'Donovan, Claire, Robinson, Peter N, Moreau, Yves, Tramontano, Anna, Babbitt, Patricia C, Brenner, Steven E, Linial, Michal, Orengo, Christine A, Rost, Burkhard, Greene, Casey S, Mooney, Sean D, Friedberg, Iddo, and Radivojac, Predrag

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations in technology for generating data and the inherently stochastic nature of biomolecular events have led to the discrepancy between the volume of data and the amount of knowledge gleaned from it. A major bottleneck in our ability to understand the molecular underpinnings of life is the assignment of function to biological macromolecules, especially proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, accurately assessing methods for protein function prediction and tracking progress in the field remain challenging. Methodology: We have conducted the second Critical Assessment of Functional Annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. One hundred twenty-six methods from 56 research groups were evaluated for their ability to predict biological functions using the Gene Ontology and gene-disease associations using the Human Phenotype Ontology on a set of 3,681 proteins from 18 species. CAFA2 featured significantly expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2. Conclusions: The top performing methods in CAFA2 outperformed the best methods from CAFA1, demonstrating that computational function prediction is improving. This increased accuracy can be attributed to the combined effect of the growing number of experimental annotations and improved methods for function prediction., Comment: Submitted to Genome Biology

Published
2016
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4. A large-scale evaluation of computational protein function prediction

Author

Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, Braak, Cajo JF ter, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, and Rost, Burkhard

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Algorithms, Animals, Computational Biology, Databases, Protein, Exoribonucleases, Forecasting, Humans, Molecular Biology, Molecular Sequence Annotation, Proteins, Species Specificity, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Published
2013

6. Additional file 4 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

Author

Trinidad, Marena, Hong, Xinying, Froelich, Steven, Daiker, Jessica, Sacco, James, Nguyen, Hong Phuc, Campagna, Madelynn, Suhr, Dean, Suhr, Teryn, LeBowitz, Jonathan H., Gelb, Michael H., and Clark, Wyatt T.

Abstract

Additional file 4: Fig S1. Disruption of ARSA by CRISPR/Cas9. ICE data showing a four-base-pair deletion in exon 2 of ARSA produced by HEK293T cells. ARSA, arylsulfatase A; CRISPR, clustered regularly interspaced short palindromic repeats; ICE, Inference of CRISPR edits. Fig S2. Correlation of enzyme activity severities and severities predicted by in silico methods. SIFT, PolyPhenand REVELscores for ARSA variants plotted as a function of the percentage of wild-type enzyme activity of ARSA variants expressed in HEK293T cells. WT, wild-type. Fig S3. Results of numeric simulation and analytically derived confidence intervals for the overall incidence of MLD using “All” allele frequencies in gnomAD. Blue bars represent a histogram of observed incidence rates from numeric simulation. Dashed red lines represent the upper and lower empirical 95% confidence intervals for the distribution generated by numeric simulation. The black dashed line represents the mean of the distribution generated by numeric simulation. The grey curve represents the beta approximation of the binomial distribution calculated using the equations for variance described in Methods. Red solid lines represent the analytically defined 95% confidence intervals. The black solid line represents the expected incidence rate”. Fig S4. cDNA construct map of plasmid pUC57-KAN.blaM.EIF.CMV.ARSA.bGH used for site-directed mutagenesis of ARSA. Expression of ARSA by the pUC57 plasmid is driven by the CMV promoter with expression of reverse-oriented beta-lactamase driven by an EF1-alpha promoter. ARSA, arylsulfatase A; bGH, bovine growth hormone; bp, base pair; CMV cytomegalovirus; EF1, elongation factor 1; EIF, eukaryotic initiation factor; SV40 simian virus 40.

Published
2023
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9. Identifying therapeutic drug targets for rare and common forms of short stature

Author

Estrada, Karol, primary, Froelich, Steven, additional, Wuster, Arthur, additional, Bauer, Christopher R., additional, Sterling, Teague, additional, Clark, Wyatt T., additional, Ru, Yuanbin, additional, Trinidad, Marena, additional, Nguyen, Hong Phuc, additional, Luu, Amanda R., additional, Wendt, Daniel J., additional, Yogalingam, Gouri, additional, Yu, Guoying Karen, additional, LeBowitz, Jonathan H., additional, and Cardon, Lon R., additional

Published
2020
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10. Assessment of predicted enzymatic activity of α‐ N ‐acetylglucosaminidase variants of unknown significance for CAGI 2016

Author

Clark, Wyatt T., primary, Kasak, Laura, additional, Bakolitsa, Constantina, additional, Hu, Zhiqiang, additional, Andreoletti, Gaia, additional, Babbi, Giulia, additional, Bromberg, Yana, additional, Casadio, Rita, additional, Dunbrack, Roland, additional, Folkman, Lukas, additional, Ford, Colby T., additional, Jones, David, additional, Katsonis, Panagiotis, additional, Kundu, Kunal, additional, Lichtarge, Olivier, additional, Martelli, Pier L., additional, Mooney, Sean D., additional, Nodzak, Conor, additional, Pal, Lipika R., additional, Radivojac, Predrag, additional, Savojardo, Castrense, additional, Shi, Xinghua, additional, Zhou, Yaoqi, additional, Uppal, Aneeta, additional, Xu, Qifang, additional, Yin, Yizhou, additional, Pejaver, Vikas, additional, Wang, Meng, additional, Wei, Liping, additional, Moult, John, additional, Yu, Guoying Karen, additional, Brenner, Steven E., additional, and LeBowitz, Jonathan H., additional

Published
2019
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12. Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Yuxiang Jiang, Oron, Tal Ronnen, Clark, Wyatt T., Bankapur, Asma R., D’Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Indika Kahanda, Verspoor, Karin M., Ben-Hur, Asa, Koo, Da Chen Emily, Penfold-Brown, Duncan, Shasha, Dennis, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Altenhoff, Adrian, Skunca, Nives, Dessimoz, Christophe, Tunca Dogan, Hakala, Kai, Suwisa Kaewphan, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Ching-Tai Chen, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T., Chapman, Samuel, Dukka BKC, Ishita K. Khan, Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amos, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E., Hieta, Reija, Legge, Duncan, Lovering, Ruth C., Magrane, Michele, Melidoni, Anna N., Mutowo-Meullenet, Prudence, Pichler, Klemens, Shypitsyna, Aleksandra, Li, Biao, Pooya Zakeri, ElShal, Sarah, Léon-Charles Tranchevent, Sayoni Das, Dawson, Natalie L., Lee, David, Lees, Jonathan G., Sillitoe, Ian, Prajwal Bhat, Nepusz, Tamás, Romero, Alfonso E., Sasidharan, Rajkumar, Haixuan Yang, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E., Pavlidis, Paul, Feng, Shou, Cejuela, Juan M., Goldberg, Tatyana, Hamp, Tobias, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Qingtian Gong, Ning, Wei, Yuanpeng Zhou, Weidong Tian, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio C.E., Pozo, Angela Del, Fernández, José M., Maietta, Paolo, Valencia, Alfonso, Tress, Michael L., Benso, Alfredo, Carlo, Stefano Di, Politano, Gianfranco, Savino, Alessandro, Hafeez Ur Rehman, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W., Dijk, Aalt D. J. Van, Gemovic, Branislava, Glisic, Sanja, Vladmir Perovic, Veljkovic, Veljko, Veljkovic, Nevena, Danillo C. Almeida-E-Silva, Vencio, Ricardo Z. N., Malvika Sharan, Vogel, Jörg, Lakesh Kansakar, Shanshan Zhang, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael J. E., Wass, Mark N., Huntley, Rachael P., Martin, Maria J., O’Donovan, Claire, Robinson, Peter N., Moreau, Yves, Tramontano, Anna, Babbitt, Patricia C., Brenner, Steven E., Linial, Michal, Orengo, Christine A., Rost, Burkhard, Greene, Casey S., Mooney, Sean D., Friedberg, Iddo, and Radivojac, Predrag

Abstract

A document containing a subset of CAFA2 analyses that are equivalent to those provided about the CAFA1 experiment in the CAFA1 supplement. (PDF 11100 kb)

Published
2016
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13. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Yuxiang, primary, Oron, Tal Ronnen, additional, Clark, Wyatt T., additional, Bankapur, Asma R., additional, D’Andrea, Daniel, additional, Lepore, Rosalba, additional, Funk, Christopher S., additional, Kahanda, Indika, additional, Verspoor, Karin M., additional, Ben-Hur, Asa, additional, Koo, Da Chen Emily, additional, Penfold-Brown, Duncan, additional, Shasha, Dennis, additional, Youngs, Noah, additional, Bonneau, Richard, additional, Lin, Alexandra, additional, Sahraeian, Sayed M. E., additional, Martelli, Pier Luigi, additional, Profiti, Giuseppe, additional, Casadio, Rita, additional, Cao, Renzhi, additional, Zhong, Zhaolong, additional, Cheng, Jianlin, additional, Altenhoff, Adrian, additional, Skunca, Nives, additional, Dessimoz, Christophe, additional, Dogan, Tunca, additional, Hakala, Kai, additional, Kaewphan, Suwisa, additional, Mehryary, Farrokh, additional, Salakoski, Tapio, additional, Ginter, Filip, additional, Fang, Hai, additional, Smithers, Ben, additional, Oates, Matt, additional, Gough, Julian, additional, Törönen, Petri, additional, Koskinen, Patrik, additional, Holm, Liisa, additional, Chen, Ching-Tai, additional, Hsu, Wen-Lian, additional, Bryson, Kevin, additional, Cozzetto, Domenico, additional, Minneci, Federico, additional, Jones, David T., additional, Chapman, Samuel, additional, BKC, Dukka, additional, Khan, Ishita K., additional, Kihara, Daisuke, additional, Ofer, Dan, additional, Rappoport, Nadav, additional, Stern, Amos, additional, Cibrian-Uhalte, Elena, additional, Denny, Paul, additional, Foulger, Rebecca E., additional, Hieta, Reija, additional, Legge, Duncan, additional, Lovering, Ruth C., additional, Magrane, Michele, additional, Melidoni, Anna N., additional, Mutowo-Meullenet, Prudence, additional, Pichler, Klemens, additional, Shypitsyna, Aleksandra, additional, Li, Biao, additional, Zakeri, Pooya, additional, ElShal, Sarah, additional, Tranchevent, Léon-Charles, additional, Das, Sayoni, additional, Dawson, Natalie L., additional, Lee, David, additional, Lees, Jonathan G., additional, Sillitoe, Ian, additional, Bhat, Prajwal, additional, Nepusz, Tamás, additional, Romero, Alfonso E., additional, Sasidharan, Rajkumar, additional, Yang, Haixuan, additional, Paccanaro, Alberto, additional, Gillis, Jesse, additional, Sedeño-Cortés, Adriana E., additional, Pavlidis, Paul, additional, Feng, Shou, additional, Cejuela, Juan M., additional, Goldberg, Tatyana, additional, Hamp, Tobias, additional, Richter, Lothar, additional, Salamov, Asaf, additional, Gabaldon, Toni, additional, Marcet-Houben, Marina, additional, Supek, Fran, additional, Gong, Qingtian, additional, Ning, Wei, additional, Zhou, Yuanpeng, additional, Tian, Weidong, additional, Falda, Marco, additional, Fontana, Paolo, additional, Lavezzo, Enrico, additional, Toppo, Stefano, additional, Ferrari, Carlo, additional, Giollo, Manuel, additional, Piovesan, Damiano, additional, Tosatto, Silvio C.E., additional, del Pozo, Angela, additional, Fernández, José M., additional, Maietta, Paolo, additional, Valencia, Alfonso, additional, Tress, Michael L., additional, Benso, Alfredo, additional, Di Carlo, Stefano, additional, Politano, Gianfranco, additional, Savino, Alessandro, additional, Rehman, Hafeez Ur, additional, Re, Matteo, additional, Mesiti, Marco, additional, Valentini, Giorgio, additional, Bargsten, Joachim W., additional, van Dijk, Aalt D. J., additional, Gemovic, Branislava, additional, Glisic, Sanja, additional, Perovic, Vladmir, additional, Veljkovic, Veljko, additional, Veljkovic, Nevena, additional, Almeida-e-Silva, Danillo C., additional, Vencio, Ricardo Z. N., additional, Sharan, Malvika, additional, Vogel, Jörg, additional, Kansakar, Lakesh, additional, Zhang, Shanshan, additional, Vucetic, Slobodan, additional, Wang, Zheng, additional, Sternberg, Michael J. E., additional, Wass, Mark N., additional, Huntley, Rachael P., additional, Martin, Maria J., additional, O’Donovan, Claire, additional, Robinson, Peter N., additional, Moreau, Yves, additional, Tramontano, Anna, additional, Babbitt, Patricia C., additional, Brenner, Steven E., additional, Linial, Michal, additional, Orengo, Christine A., additional, Rost, Burkhard, additional, Greene, Casey S., additional, Mooney, Sean D., additional, Friedberg, Iddo, additional, and Radivojac, Predrag, additional

Published
2016
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15. AN INTEGRATED APPROACH TO INFERRING GENE-DISEASE ASSOCIATIONS IN HUMANS

Author

Radivojac, Predrag, Peng, Kang, Clark, Wyatt T., Peters, Brandon J., Mohan, Amrita, Boyle, Sean M., and Mooney, Sean D.

Subjects
Leukemia, Genes, ROC Curve, Protein Interaction Mapping, Humans, Disease, Article, Algorithms
Abstract

One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene-disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously.www.phenopred.org.

Published
2008

18. An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

Author

Yuxiang Jiang, Tal Ronnen Oron, Clark, Wyatt T., Bankapur, Asma R., D'Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Kahanda, Indika, Verspoor, Karin M., Asa Ben-Hur, Da Chen Emily Koo, Penfold-Brown, Duncan, Shasha, Dennis, Noah Youngs, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, and Casadio, Rita

Published
2016
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24. Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

Author

Jain S, Trinidad M, Nguyen TB, Jones K, Neto SD, Ge F, Glagovsky A, Jones C, Moran G, Wang B, Rahimi K, Çalıcı SZ, Cedillo LR, Berardelli S, Özden B, Chen K, Katsonis P, Williams A, Lichtarge O, Rana S, Pradhan S, Srinivasan R, Sajeed R, Joshi D, Faraggi E, Jernigan R, Kloczkowski A, Xu J, Song Z, Özkan S, Padilla N, de la Cruz X, Acuna-Hidalgo R, Grafmüller A, Jiménez Barrón LT, Manfredi M, Savojardo C, Babbi G, Martelli PL, Casadio R, Sun Y, Zhu S, Shen Y, Pucci F, Rooman M, Cia G, Raimondi D, Hermans P, Kwee S, Chen E, Astore C, Kamandula A, Pejaver V, Ramola R, Velyunskiy M, Zeiberg D, Mishra R, Sterling T, Goldstein JL, Lugo-Martinez J, Kazi S, Li S, Long K, Brenner SE, Bakolitsa C, Radivojac P, Suhr D, Suhr T, and Clark WT

Abstract

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A ( ARSA ) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research., Competing Interests: Declarations Conflict of interest/Competing interests Wyatt T. Clark, Marena Trinidad, Courtney Astore, Teague Sterling, and Sufyan Kazi are former employees and potential shareholders of BioMarin Pharmaceutical. Rocio Acuna-Hidalgo is a current employee and shareholder of Nostos Genomics GmbH. Andrea Grafmüller and Laura T. Jiménez Barrón are former employees of Nostos Genomics GmbH.

Published
2024
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25. Vector quantization kernels for the classification of protein sequences and structures.

Author

Clark WT and Radivojac P

Subjects
Algorithms, Amino Acid Sequence, Bacterial Proteins chemistry, Bacterial Proteins classification, Bacterial Proteins genetics, Computational Biology, DNA Helicases chemistry, DNA Helicases classification, DNA Helicases genetics, Data Mining statistics & numerical data, Fourier Analysis, Gene Ontology statistics & numerical data, Hydrophobic and Hydrophilic Interactions, Proteins classification, Structural Homology, Protein, Support Vector Machine, Thermus thermophilus enzymology, Thermus thermophilus genetics, Proteins chemistry, Proteins genetics
Abstract

We propose a new kernel-based method for the classification of protein sequences and structures. We first represent each protein as a set of time series data using several structural, physicochemical, and predicted properties such as a sequence of consecutive dihedral angles, hydrophobicity indices, or predictions of disordered regions. A kernel function is then computed for pairs of proteins, exploiting the principles of vector quantization and subsequently used with support vector machines for protein classification. Although our method requires a significant pre-processing step, it is fast in the training and prediction stages owing to the linear complexity of kernel computation with the length of protein sequences. We evaluate our approach on two protein classification tasks involving the prediction of SCOP structural classes and catalytic activity according to the Gene Ontology. We provide evidence that the method is competitive when compared to string kernels, and useful for a range of protein classification tasks. Furthermore, the applicability of our approach extends beyond computational biology to any classification of time series data.

Published
2014

26. From protein-disease associations to disease informatics.

Author

Dalkilic MM, Costello JC, Clark WT, and Radivojac P

Subjects
Algorithms, Animals, Base Sequence, Cell Line, Disease Models, Animal, Humans, Polymorphism, Single Nucleotide, RNA genetics, Terminology as Topic, Computational Biology trends, Disease classification, Genetic Diseases, Inborn classification, Proteins genetics
Abstract

Advancements in high-throughput technology and computational power have brought about significant progress in our understanding of cellular processes, including an increased appreciation of the intricacies of disease. The computational biology community has made strides in characterizing human disease and implementing algorithms that will be used in translational medicine. Despite this progress, most of the identified biomarkers and proposed methodologies have still not achieved the sensitivity and specificity to be effectively used, for example, in population screening against various diseases. Here we review the current progress in computational methodology developed to exploit major high-throughput experimental platforms towards improved understanding of disease, and argue that an integrated model for biomarker discovery, predictive medicine and treatment is likely to be data-driven and personalized. In such an approach, major data collection is yet to be done and comprehensive computational models are yet to be developed.

Published
2008
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