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1. Genome wide predictions of miRNA regulation by transcription factors

   https://doi.org/10.1093/bioinformatics/btw452  

   Ruffalo, Matthew ; Bar-Joseph, Ziv ( August 2016 , Bioinformatics)

   Reconstructing regulatory networks from expression and interaction data is a major goal of systems biology. While much work has focused on trying to experimentally and computationally determine the set of transcription-factors (TFs) and microRNAs (miRNAs) that regulate genes in these networks, relatively little work has focused on inferring the regulation of miRNAs by TFs. Such regulation can play an important role in several biological processes including development and disease. The main challenge for predicting such interactions is the very small positive training set currently available. Another challenge is the fact that a large fraction of miRNAs are encoded within genes making it hard to determine the specific way in which they are regulated.

   To enable genome wide predictions of TF–miRNA interactions, we extended semi-supervised machine-learning approaches to integrate a large set of different types of data including sequence, expression, ChIP-seq and epigenetic data. As we show, the methods we develop achieve good performance on both a labeled test set, and when analyzing general co-expression networks. We next analyze mRNA and miRNA cancer expression data, demonstrating the advantage of using the predicted set of interactions for identifying more coherent and relevant modules, genes, and miRNAs. The complete set of predictions is available on the supporting website and can be used by any method that combines miRNAs, genes, and TFs.

   Code and full set of predictions are available from the supporting website: http://cs.cmu.edu/~mruffalo/tf-mirna/.

   zivbj@cs.cmu.edu

   Supplementary data are available at Bioinformatics online.

    

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2. DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning

   https://doi.org/10.1093/bioinformatics/btz367  

   Chen, Hao ; Shaw, Dipan ; Zeng, Jianyang ; Bu, Dongbo ; Jiang, Tao ( July 2019 , Bioinformatics)

   Alternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gene functions have been well studied, little is known about the specific functions of isoforms, making accurate prediction of isoform functions highly desirable. However, the existing approaches to predicting isoform functions are far from satisfactory due to at least two reasons: (i) unlike genes, isoform-level functional annotations are scarce. (ii) The information of isoform functions is concealed in various types of data including isoform sequences, co-expression relationship among isoforms, etc.

   In this study, we present a novel approach, DIFFUSE (Deep learning-based prediction of IsoForm FUnctions from Sequences and Expression), to predict isoform functions. To integrate various types of data, our approach adopts a hybrid framework by first using a deep neural network (DNN) to predict the functions of isoforms from their genomic sequences and then refining the prediction using a conditional random field (CRF) based on co-expression relationship. To overcome the lack of isoform-level ground truth labels, we further propose an iterative semi-supervised learning algorithm to train both the DNN and CRF together. Our extensive computational experiments demonstrate that DIFFUSE could effectively predict the functions of isoforms and genes. It achieves an average area under the receiver operating characteristics curve of 0.840 and area under the precision–recall curve of 0.581 over 4184 GO functional categories, which are significantly higher than the state-of-the-art methods. We further validate the prediction results by analyzing the correlation between functional similarity, sequence similarity, expression similarity and structural similarity, as well as the consistency between the predicted functions and some well-studied functional features of isoform sequences.

   https://github.com/haochenucr/DIFFUSE.

   Supplementary data are available at Bioinformatics online.

    

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3. Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape

   https://doi.org/10.1093/bioinformatics/btx480  

   Dai, Hanjun ; Umarov, Ramzan ; Kuwahara, Hiroyuki ; Li, Yu ; Song, Le ; Gao, Xin ; Stegle, ed., Oliver ( July 2017 , Bioinformatics)

   An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem.

   Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods.

   Our program is freely available at https://github.com/ramzan1990/sequence2vec.

   Supplementary data are available at Bioinformatics online.

    

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4. Domain-adaptive neural networks improve cross-species prediction of transcription factor binding

   https://doi.org/10.1101/gr.275394.121  

   Cochran, Kelly ; Srivastava, Divyanshi ; Shrikumar, Avanti ; Balsubramani, Akshay ; Hardison, Ross C. ; Kundaje, Anshul ; Mahony, Shaun ( March 2022 , Genome Research)

   The intrinsic DNA sequence preferences and cell type–specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence models of cell type–specific genomic occupancy of a TF in one species should generalize to closely matched cell types in a related species. To assess the viability of cross-species TF binding prediction, we train neural networks to discriminate ChIP-seq peak locations from genomic background and evaluate their performance within and across species. Cross-species predictive performance is consistently worse than within-species performance, which we show is caused in part by species-specific repeats. To account for this domain shift, we use an augmented network architecture to automatically discourage learning of training species–specific sequence features. This domain adaptation approach corrects for prediction errors on species-specific repeats and improves overall cross-species model performance. Our results show that cross-species TF binding prediction is feasible when models account for domain shifts driven by species-specific repeats. 

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5. Towards region-specific propagation of protein functions

   https://doi.org/10.1093/bioinformatics/bty834  

   Koo, Da Chen Emily ; Bonneau, Richard ; Wren, ed., Jonathan ( October 2018 , Bioinformatics)

   Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.

   We apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction. We compare region-level predictive performance of our method against that of a whole-protein baseline method using proteins with structurally verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into region-specific and whole-protein terms and select prediction methods for different classes of GO terms.

   The code and features are freely available at: https://github.com/ek1203/rsfp.

   Supplementary data are available at Bioinformatics online.

    

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