Unveiling new disease, pathway, and gene associations via multi-scale neural network
Open accessAbstract
Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these profiles, improving our ability to diagnose and assess disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient's condition and co-morbidity risk. Here, we consider differential gene expressions obtained by microarray technology for patients diagnosed with various diseases. Based on these data and cellular multi-scale organization, we aim at uncovering disease-disease, disease-gene and disease-pathway associations. We propose a neural network with structure based on the multi-scale organization of proteins in a cell into biological pathways. We show that this model is able to correctly predict the diagnosis for the majority of patients. Through the analysis of the trained model, we predict disease-disease, disease-pathway, and disease-gene associations and validate the predictions by comparisons to known interactions and literature search, proposing putative explanations for the predictions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Gaudelet et al. modeled a neural network structure on the multiscale functional organization of a cell by integrating gene-pathway annotations in the Reactome database, and their model accurately determined patient diagnoses based on gene expression data.
“In a related study, Gaudelet et al. modeled a neural network structure on the multiscale functional organization of a cell by integrating gene-pathway annotations in the Reactome database; their model was able to accurately determine patient diagnoses based on gene expression data.”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Gaudelet et al., 2020
The trained Gaudelet neural network enabled extraction of biological knowledge by determining novel disease-pathway and disease-gene associations.
“Importantly, the trained neural network enabled extraction of biological knowledge by determining novel disease-pathway and disease-gene associations (Gaudelet et al., 2020).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Gaudelet et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
(2016) · Cited by 11,679 · see citing works
NCBI GEO: archive for functional genomics data sets—update
Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K. H., Sherman, P. M., Holko, M., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1193 · Cited by 10,418 · see citing works
limma: Linear Models for Microarray Data
Smyth, G. K. (2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0_23 · Cited by 5,796 · see citing works
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242 · see citing works
The Reactome Pathway Knowledgebase
Fabregat, A., Jupe, S., Matthews, L. R., Sidiropoulos, K., Gillespie, M. E., Garapati, P. V., Haw, R., Jassal, B., Korninger, F., May, B., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1132 · Cited by 2,798 · see citing works
Piñero, J., Bravo, À., Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., García-García, J., Sanz, F., & Furlong, L. I. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw943 · Cited by 2,680 · see citing works
The BioGRID interaction database: 2019 update
Oughtred, R., Stark, C., Breitkreutz, B., Rust, J., Boucher, L., Chang, C., Kolas, N. K., O’Donnell, L., Leung, G. C., McAdam, R. F., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1079 · Cited by 1,440 · see citing works
Genetics of gene expression and its effect on disease
Emilsson, V., Thorleifsson, G., Zhang, B., Leonardson, A. S., Zink, F., Zhu, J., Carlson, S., Helgason, A., Walters, G. B., Gunnarsdottir, S., et al. (2008) · Nature · doi:10.1038/nature06758 · Cited by 1,296 · see citing works
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 1,201 · see citing works
The Comparative Toxicogenomics Database: update 2019
Davis, A. P., Grondin, C., Johnson, R. J., Sciaky, D., McMorran, R., Wiegers, J., Wiegers, T. C., & Mattingly, C. J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky868 · Cited by 978 · see citing works
Cogswell, J. P., Ward, J., Taylor, I. A., Waters, M., Shi, Y., Cannon, B., Kelnar, K., Kemppainen, J., Brown, D., Chen, C., et al. (2008) · Journal of Alzheimer s Disease · doi:10.3233/jad-2008-14103 · Cited by 943 · see citing works
Disease Ontology: a backbone for disease semantic integration
Schriml, L. M., Arze, C., Nadendla, S., Chang, Y.-W. W., Mazaitis, M., Felix, V., Feng, G., & Kibbe, W. A. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr972 · Cited by 929 · see citing works
ArrayExpress update—simplifying data submissions
Kolesnikov, N. N., Hastings, E., Keays, M., Melnichuk, O., Tang, A., Williams, E. M., Dylag, M., Kurbatova, N., Brandizi, M., Burdett, T., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1057 · Cited by 700 · see citing works
Mendelian Inheritance in Man: A Catalog of Human Genes and Genetic Disorders
McKusick, V. A. (1998) · DigitalGeorgetown (Georgetown University Library) · Cited by 687 · see citing works
A Dynamic Network Approach for the Study of Human Phenotypes
Hidalgo, C. A., Blumm, N., Barabási, A.-Ĺ., & Christakis, N. A. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000353 · Cited by 670 · see citing works
Frozen robust multiarray analysis (fRMA)
McCall, M. N., Bolstad, B. M., & Irizarry, R. A. (2010) · Biostatistics · doi:10.1093/biostatistics/kxp059 · Cited by 582 · see citing works
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods
Abeel, T., Helleputte, T., de Peer, Y. V., Dupont, P., & Saeys, Y. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp630 · Cited by 574 · see citing works
Navigating gene expression using microarrays — a technology review
Schulze, A., & Downward, J. (2001) · Nature Cell Biology · doi:10.1038/35087138 · Cited by 535 · see citing works
The implications of human metabolic network topology for disease comorbidity
Lee, D., Park, J., Kay, K. A., Christakis, N. A., Oltvai, Z. N., & Barabási, A.-Ĺ. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0802208105 · Cited by 489 · see citing works
Using deep learning to model the hierarchical structure and function of a cell
Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T. (2018) · Nature Methods · doi:10.1038/nmeth.4627 · Cited by 435 · see citing works
Burger, P. C., Yu, I.-Y., Tihan, T., Friedman, H. S., Strother, D. R., Kepner, J. L., Duffner, P. K., Kun, L. E., & Perlman, E. J. (1998) · The American Journal of Surgical Pathology · doi:10.1097/00000478-199809000-00007 · Cited by 430 · see citing works
Jensen, A. B., Moseley, P. L., Oprea, T. I., Ellesøe, S. G., Eriksson, R., Schmock, H., Jensen, P. B., Jensen, L. J., & Brunak, S. (2014) · Nature Communications · doi:10.1038/ncomms5022 · Cited by 407 · see citing works
Selmer, C., Olesen, J. B., Hansen, M. L., Lindhardsen, J., Olsen, A.-M. S., Madsen, J. C., Faber, J., Hansen, P. R., Pedersen, O. D., Torp-Pedersen, C., et al. (2012) · BMJ · doi:10.1136/bmj.e7895 · Cited by 323 · see citing works
Stable feature selection for biomarker discovery
He, Z., & Yu, W. (2010) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2010.07.002 · Cited by 309 · see citing works
The Ubiquitin-Proteasome System and Its Role in Inflammatory and Autoimmune Diseases
Jingsong, Wang, Michael, & Maldonado (2006) · 中国免疫学杂志：英文版 · Cited by 269 · see citing works
Integrated interactions database: tissue-specific view of the human and model organism interactomes
Kotlyar, M., Pastrello, C., Sheahan, N., & Jurišica, I. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1115 · Cited by 254 · see citing works
The ubiquitin-proteasome system and its role in inflammatory and autoimmune diseases.
Wang, J., & Maldonado, M. A. (2006) · PubMed · Cited by 207 · see citing works
Sensitivity analysis for minimization of input data dimension for feedforward neural network
Żurada, J. M., Malinowski, A., & Cloete, I. (2002) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.1994.409622 · Cited by 201 · see citing works
Visible Machine Learning for Biomedicine
Yu, M. K., Ma, J., Fisher, J., Kreisberg, J. F., Raphael, B. J., & Ideker, T. (2018) · Cell · doi:10.1016/j.cell.2018.05.056 · Cited by 162 · see citing works
Comorbidities in vitiligo: comprehensive review
Dahir, A. M., & Thomsen, S. F. (2018) · International Journal of Dermatology · doi:10.1111/ijd.14055 · Cited by 148 · see citing works
Gene-Network Analysis Identifies Susceptibility Genes Related to Glycobiology in Autism
van der Zwaag, B., Franke, L., Poot, M., Hochstenbach, R., Spierenburg, H. A., Vorstman, J. A. S., van Daalen, E., de Jonge, M. V., Verbeek, N. E., Brilstra, E. H., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005324 · Cited by 145 · see citing works
Disease signatures are robust across tissues and experiments
Dudley, J. T., Tibshirani, R. J., Deshpande, T., & Butte, A. J. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.66 · Cited by 110 · see citing works
Zhao, J., Yang, T., Huang, Y., & Holme, P. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024306 · Cited by 89 · see citing works
Bellizzi, D., Dato, S., Cavalcante, P., Covello, G., Cianni, F. D., Passarino, G., Rose, G., & BENEDICTIS, G. D. (2006) · Genomics · doi:10.1016/j.ygeno.2006.09.004 · Cited by 87 · see citing works
Koltai, E., Bori, Z., Osvath, P., Ihász, F., Szablics, P., Tóth, G., Degens, H., Rittweger, J., Boldogh, I. R., & Radak, Z. O. (2018) · Redox Biology · doi:10.1016/j.redox.2018.07.022 · Cited by 61 · see citing works
Proietti, M., Raparelli, V., Laroche, C., Dan, G.-A., Janion, M., Popescu, R., Sinagra, G., Vijgen, J., Boriani, G., Maggioni, A. P., et al. (2017) · EP Europace · doi:10.1093/europace/euw169 · Cited by 58 · see citing works
Ciucci, S., Ge, Y., Durán, C., Palladini, A., Jiménez-Jiménez, V., Martínez-Sánchez, L. M., Wang, Y., Sales, S., Shevchenko, A., Poser, S., et al. (2017) · Scientific Reports · doi:10.1038/srep43946 · Cited by 52 · see citing works
miR-202 suppresses proliferation and induces apoptosis of osteosarcoma cells by downregulating Gli2
Sun, Z., Zhang, T., Hong, H., Liu, Q., & Zhang, H. (2014) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-014-2195-z · Cited by 46 · see citing works
Cancer stem cells generated by alcohol, diabetes, and hepatitis C virus
Machida, K., Chen, C., Liu, J., Kashiwabara, C., Feldman, D. E., French, S. W., Sher, L. S., Hyeongnam, J. J., & Tsukamoto, H. (2012) · Journal of Gastroenterology and Hepatology · doi:10.1111/j.1440-1746.2011.07010.x · Cited by 42 · see citing works
Non‐segmental vitiligo and psoriasis comorbidity – a case‐control study in Italian patients
Arunachalam, M., Dragoni, F., Colucci, R., Berti, S., Crocetti, E., Galeone, M., Conti, R., & Moretti, S. (2013) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.12117 · Cited by 32 · see citing works
PCID: A Novel Approach for Predicting Disease Comorbidity by Integrating Multi-Scale Data
He, F., Zhu, G., Wang, Y., Zhao, X., & Huang, D.-S. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2550443 · Cited by 31 · see citing works
Schizophrenia and Infections: The Eyes Have It
Torrey, E. F., & Yolken, R. H. (2016) · Schizophrenia Bulletin · doi:10.1093/schbul/sbw113 · Cited by 31 · see citing works
SLC2A1 gene analysis of Japanese patients with glucose transporter 1 deficiency syndrome
Hashimoto, N., Kagitani‐Shimono, K., Sakai, N., Otomo, T., Tominaga, K., Nabatame, S., Mogami, Y., Takahashi, Y., Imai, K., Yanagihara, K., et al. (2011) · Journal of Human Genetics · doi:10.1038/jhg.2011.115 · Cited by 22 · see citing works
Pseudoacromegaly: A Differential Diagnostic Problem for Acromegaly With a Genetic Solution
Dahlqvist, P. M., Spencer, R., Marques, P., Dang, M. N., Glad, C. A. M., Johannsson, G., & Korbonits, M. (2017) · Journal of the Endocrine Society · doi:10.1210/js.2017-00164 · Cited by 22 · see citing works
Kannan, V., Swartz, F., Kiani, N. A., Silberberg, G., Tsipras, G., Gómez-Cabrero, D., Alexanderson, K. A. E., & Tegner, J. N. (2016) · Scientific Reports · doi:10.1038/srep26170 · Cited by 19 · see citing works
Makis, A. C., Polychronopoulou, S., & Haidas, S. (2004) · Journal of Pediatric Hematology/Oncology · doi:10.1097/00043426-200407000-00009 · Cited by 16 · see citing works
Crasto, J. A., Fourman, M. S., Morales-Restrepo, A., Mahjoub, A., Mandell, J. B., Ramnath, K., Tebbets, J. C., Watters, R. J., & Weiss, K. R. (2018) · Oncotarget · doi:10.18632/oncotarget.25733 · Cited by 16 · see citing works
Primary right atrial cardiac osteosarcoma with congestive heart failure
Dohi, T., Ohmura, H., Daida, H., & Amano, A. (2009) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/j.ejcts.2008.12.009 · Cited by 14 · see citing works
Right ventricular failure due to primary right ventricle osteosarcoma
Sogabe, O., & Ohya, T. (2007) · The Japanese Journal of Thoracic and Cardiovascular Surgery · doi:10.1007/s11748-006-0053-y · Cited by 13 · see citing works
Progressive visceral leishmaniasis misdiagnosed as cirrhosis of the liver: a case report
Giannitrapani, L., Soresi, M., Spada, E. L., Tripodo, C., & Montalto, G. (2009) · Journal of Medical Case Reports · doi:10.4076/1752-1947-3-7265 · Cited by 7 · see citing works
Hong, G., Li, H., Zhang, J., Guan, Q., Chen, R., & Guo, Z. (2017) · Scientific Reports · doi:10.1038/s41598-017-01536-3 · Cited by 6 · see citing works
Unveiling the molecular basis of disease co-occurrence: towards personalized comorbidity profilesPreprint
Sánchez-Valle, J., Tejero, H., Fernández, J. M., Juan, D., Capella-Gutiérrez, S., Al‐Shahrour, F., Tabarés‐Seisdedos, R., Pancaldi, V., & Valencia, A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/431312 · Cited by 3 · see citing works
Flagged first, then by citations
2 of the 59 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Current and future directions in network biology
Žitnik, M., Li, M. M., Wells, A., Glass, K., Gysi, D. M., Krishnan, A., Murali, T. M., Predrag Radivojac, A., Roy, S., Baudot, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae099 · Cited by 118 · see citing works
Network analysis reveals rare disease signatures across multiple levels of biological organization
Buphamalai, P., Kokotovic, T., Nagy, V., & Menche, J. (2021) · Nature Communications · doi:10.1038/s41467-021-26674-1 · Cited by 95 · see citing works
Mapping the multiscale structure of biological systems
Schaffer, L. V., & Ideker, T. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.012 · Cited by 75 · see citing works
Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder
Dwivedi, S. K., Tjärnberg, A., Tegner, J. N., & Gustafsson, M. (2020) · Nature Communications · doi:10.1038/s41467-020-14666-6 · Cited by 50 · see citing works
Bourgeais, V., Zehraoui, F., Hamdoune, M. B., & Hanczar, B. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04370-7 · Cited by 44 · see citing works
A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer
Baptista, D., Ferreira, P. G., & Rocha, M. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010200 · Cited by 38 · see citing works
Molecular bases of comorbidities: present and future perspectives
Sánchez-Valle, J., & Valencia, A. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.06.003 · Cited by 24 · see citing works
Mumtaz, H., Saqib, M., Jabeen, S., Muneeb, M., Mughal, W., Sohail, H., Safdar, M., Mehmood, Q., Khan, M. A., & Ismail, S. M. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1227168 · Cited by 24 · see citing works
Khanyari, M., Robinson, S., Morgan, E. R., Brown, T., Singh, N. J., Salemgareyev, A., Zuther, S., Kock, R. A., & Milner‐Gulland, E. J. (2021) · Journal of Applied Ecology · doi:10.1111/1365-2664.13937 · Cited by 20 · see citing works
Disease-Pathway Association Prediction Based on Random Walks With Restart and PageRank
Ghulam, A., Lei, X., Guo, M., & Bian, C. (2020) · IEEE Access · doi:10.1109/access.2020.2987071 · Cited by 17 · see citing works
Exploring novel disease-disease associations based on multi-view fusion network
Yang, X., Xu, W., Leng, D., Wen, Y., Wu, L., Li, R., Huang, J., Bo, X., & He, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.02.038 · Cited by 11 · see citing works
Ghosal, S., Chen, Q., Pergola, G., Goldman, A. L., Ulrich, W. S., Weinberger, D. R., & Venkataraman, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.28.446066 · Cited by 9 · see citing works
Interpretable Drug Response Prediction using a Knowledge-based Neural Network
Snow, O., Sharifi-Noghabi, H., Lu, J., Zolotareva, O. I., Lee, M., & Ester, M. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467212 · Cited by 8 · see citing works
Park, S., Huang, E., & Ahn, T. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222111531 · Cited by 6 · see citing works
Zhou, H., Zhang, Y., He, L., Shen, L., & Chen, B. Y. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385469 · Cited by 4 · see citing works
Zhang, Q., Han, Y., Lam, J. C. K., Bai, R., Gozes, I., & Li, V. O. K. (2023) · medRxiv · doi:10.1101/2023.01.30.23285175 · Cited by 3 · see citing works
Stability of Feature Selection Algorithms
Theng, D. P., & Bhoyar, K. K. (2022) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-981-19-0151-5_26 · Cited by 3 · see citing works
Biswas, S., Ranjan, V., Mitra, P., & Rao, K. S. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3526805 · Cited by 2 · see citing works
Mass Enhanced Node Embeddings for Drug Repurposing
Chatzianastasis, M., Nikolentzos, G., & Vazirgiannis, M. (2022) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3549737.3549813 · Cited by 2 · see citing works
Liang, L., Zhu, K., Tao, J., & Lu, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008792 · Cited by 1 · see citing works
Bonham-Carter, O., & Thu, Y. M. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73103-8_5
Multimorbidity measurement strategies for predicting hospital visits
Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z
Neural Network Approach in Gene-Disease Association Mining
Roy, S., Sarani, S. R., Barua, H., Ahona, J. M., & Al-Mamun, S. M. A. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11545648
Sparsity is all you need: rethinking biologically informed neural networks
Caranzano, I., Pancotti, C., Rollo, C., Sartori, F., Lió, P., Fariselli, P., & Sanavia, T. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag425
- Authors
- Gaudelet, T., Malod-Dognin, N., Sánchez-Valle, J., Pancaldi, V., Valencia, A., & Pržulj, N.
- Affiliations
- University College London (GB)
- Barcelona Supercomputing Center (ES)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Pržulj, Nataša
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-04-06
- Volume
- 15
- Issue
- 4
- Pages
- e0231059
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7135208
- OpenAlex
- W2914372520
- Cited by
- 25
- References
- 59
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 6 · 2022: 3 · 2021: 9 · 2020: 2
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Les Laboratories Pierre Fabre
- Javna Agencija za Raziskovalno Dejavnost RS (J1-8155, project J1-8155)
- H2020 European Research Council (770827)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- gene-disease association, differential gene expression, microarray data, multi-scale neural network, biological pathways, diagnostic prediction, drug repurposing
- MeSH terms
- Disease, Humans, Models, Biological, Prognosis, ROC Curve, Signal Transduction, Comorbidity, Neural Networks, Computer, Computational Biology, Gene Regulatory Networks, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
