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1. Majority Vote Cascading: A Semi-Supervised Framework for Improving Protein Function Prediction

   https://doi.org/10.1145/3307339.3342135  

   Lazarsfeld, John; Rodríguez, Jonathan; Erden, Mert; Liu, Yuelin; Cowen, Lenore J. (September 2019, BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)

   A method to improve protein function prediction for sparsely annotated PPI networks is introduced. The method extends the DSD majority vote algorithm introduced by Cao et al. to give confidence scores on predicted labels and to use predictions of high confidence to predict the labels of other nodes in subsequent rounds. We call this a majority vote cascade. Several cascade variants are tested in a stringent cross-validation experiment on PPI networks from S. cerevisiae and D. melanogaster, and we show that for many different settings with several alternative confidence functions, cascading improves the accuracy of the predictions. A list of the most confident new label predictions in the two networks is also reported. Code, networks for the cross-validation experiments, and supplementary figures and tables appear at http://bcb.cs.tufts.edu/cascade. 

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2. Predictive Powered Inference for Healthcare; Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression

   Schultz, Jacob; Jerry, L Prince; Jedynak, Bruno Michel (November 2024, Proceedings of the 9th Machine Learning for Healthcare Conference, PMLR 252, 2024.)

   Predictive power inference (PPI and PPI++) is a recently developed statistical method for computing confidence intervals and tests. It combines observations with machine-learning predictions. We use this technique to measure the association between the thickness of retinal layers and the time from the onset of Multiple Sclerosis (MS) symptoms. Further, we correlate the former with the Expanded Disability Status Scale, a measure of the progression of MS. In both cases, the confidence intervals provided with PPI++ improve upon standard statistical methodology, showing the advantage of PPI++ for answering inference problems in healthcare. 

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3. GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

   https://doi.org/10.1093/bioinformatics/btaa459  

   Devkota, Kapil; Murphy, James M; Cowen, Lenore J (July 2020, Bioinformatics)

   Abstract Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity

   https://doi.org/10.1093/bioinformatics/btab098  

   Barot, Meet; Gligorijević, Vladimir; Cho, Kyunghyun; Bonneau, Richard (February 2021, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Abstract Motivation Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to protein functional annotation use sequence similarity to transfer knowledge between species. These approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular context for meaningful prediction. To supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, most of these methods are tied to a network for a single species, and many species lack biological networks. Results In this work, we integrate sequence and network information across multiple species by computing IsoRank similarity scores to create a meta-network profile of the proteins of multiple species. We use this integrated multispecies meta-network as input to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and consequently leads to significant improvements in function prediction performance compared to two network-based methods, a deep learning sequence-based method and the BLAST annotation method used in the Critial Assessment of Functional Annotation. We are able to demonstrate that our approach performs well even in cases where a species has no network information available: when an organism’s PPI network is left out we can use our multi-species method to make predictions for the left-out organism with good performance. Availability and implementation The code is freely available at https://github.com/nowittynamesleft/NetQuilt. The data, including sequences, PPI networks and GO annotations are available at https://string-db.org/. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. A Novel Algorithm for Aggregating Crowdsourced Data

   Prihar, E.; Heffernan, Neil T. (January 2021, International Conference on Educational Data Mining)

   null (Ed.)

   Similar content has tremendous utility in classroom and online learning environments. For example, similar content can be used to combat cheating, track students’ learning over time, and model students’ latent knowledge. These different use cases for similar content all rely on different notions of similarity, which make it difficult to determine contents’ similarities. Crowdsourcing is an effective way to identify similar content in a variety of situations by providing workers with guidelines on how to identify similar content for a particular use case. However, crowdsourced opinions are rarely homogeneous and therefore must be aggregated into what is most likely the truth. This work presents the Dynamically Weighted Majority Vote method. A novel algorithm that combines aggregating workers’ crowdsourced opinions with estimating the reliability of each worker. This method was compared to the traditional majority vote method in both a simulation study and an empirical study, in which opinions on seventh grade mathematics problems’ similarity were crowdsourced from middle school math teachers and college students. In both the simulation and the empirical study the Dynamically Weighted Majority Vote method outperformed the traditional majority vote method, suggesting that this method should be used instead of majority vote in future crowdsourcing endeavors. 

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