LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions

Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe–disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe–disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.

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“…The disease symptom similarity matrix S s can be computed according to the method provided by Zhou et al (2020) .…”

Section: Methodsmentioning

confidence: 99%

Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

Chen

Sun

et al. 2021

Front. Microbiol.

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“…All parameters in BioProt and LPI-SKF are the corresponding values provided by refs. [36] and [21], respectively. The deep GBDT architecture we used is (input-16-16-output).…”

Section: Experimental Settingsmentioning

confidence: 99%

LPI-DeepGBDT: A Multiple-Layer Deep Framework based on Gradient Boosting Decision Trees for lncRNA-Protein Interaction Identification

ZhouZhou

Wang

Tian

et al. 2021

Preprint

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Background: Long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Discovery of lncRNA-protein interactions (LPIs) contributes to understand the biological functions and mechanisms of lncRNAs. Although wet experiments find a few interactions between lncRNAs and proteins, experimental techniques are costly and time-consuming. Therefore, computational methods are increasingly exploited to uncover the possible associations. However, existing computational methods have several limitations. First, majority of them were measured based on one simple dataset, which may result in the prediction bias. Second, few of them are applied to identify relevant data for new lncRNAs (or proteins). Finally, they failed to utilize diverse biological information of lncRNAs and proteins. Results: Under the feed-forward deep architecture based on Gradient Boosting Decision Trees (LPI-deepGBDT), this work focuses on classify unobserved LPIs. First, three human LPI datasets and two plant LPI datasets are arranged. Second, the biological features of lncRNAs and proteins are extracted by Pyfeat and BioProt, respectively. Thirdly, the features are dimensionally reduced and concatenated as a vector to represent an lncRNA-protein pair. Finally, a deep architecture composed of the forward mappings and inverse mappings is developed to predict underlying linkages between lncRNAs and proteins. LPI-deepGBDT is compared with four classical LPI prediction models (LPI-BLS, LPI-CatBoost, PLIPCOM, and LPI-SKF) under three cross validations on lncRNAs, proteins, lncRNA-protein pairs, respectively. It obtains the best average AUC and AUPR values on the five datasets under the three cross validations, significantly outperforming other four LPI identification methods. That is, AUCs computed by LPI-deepGBDT are 0.8321, 0.6815, and 0.9073, respectively and AUPRs are 0.8095, 0.6771, and 0.8849, respectively. The results demonstrate the powerful classification ability of LPI-deepGBDT. Case study analyses show that there may be interactions between GAS5 and Q15717, RAB30-AS1 and O00425, and LINC-01572 and P35637. Conclusions: Integrating ensemble learning and hierarchical distributed representations and building a multiple-layered deep architecture, this work improves LPI prediction performance as well as effectively probes interaction data for new lncRNAs/proteins.

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“…Other methods used to predict LPI that do not fall into a specific category include LPI-SKF (lncRNA-protein interaction similarity kernel fusion) [75], PMKDN (projectionbased neighborhood non-negative matrix decomposition model) [76] and LPI-MiRNA [77]. LPI-SKF uses an integrative approach where verified lncRNA-protein interactions are used to build a network, and similarity kernel fusion is used to integrate protein and lncRNA similarity scores before applying manifold learning.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

A Survey of Current Resources to Study lncRNA-protein Interactions.

Philip

Chen

Tyagi

2021

Preprint

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Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein-DNA interactions such as histone and transcription factor binding are well studied, along with RNA-RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely driven by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanism, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.

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LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions

Cited by 27 publications

References 48 publications

Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

LPI-DeepGBDT: A Multiple-Layer Deep Framework based on Gradient Boosting Decision Trees for lncRNA-Protein Interaction Identification

A Survey of Current Resources to Study lncRNA-protein Interactions.

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