More Like this

1. DeepRMethylSite: A Deep Learning Based Approach for prediction of arginine methylation sites in Proteins

   Meenal Chaudhari; Niraj Thapa; Kaushik Roy; Robert H. Newman; Hiroto, Saigod; Dukka B. K. C. (June 2020, Molecular omics)

   Methylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine learning strategies, such as support vector machines and random forest, to predict sites of methylation based on a set of “hand-selected” features. As a consequence, the subsequent models may be biased toward one set of features. Moreover, due to the large number of features, model development can often be computationally expensive. In this paper, we propose an alternative approach based on deep learning to predict arginine methylation sites. Our model, which we termed DeepRMethylSite, is computationally less expensive than traditional feature-based methods while eliminating potential biases that can arise through features selection. Based on independent testing on our dataset, DeepRMethylSite achieved efficiency scores of 68%, 82% and 0.51 with respect to sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC), respectively. Importantly, in side-by-side comparisons with other state-of-the-art methylation site predictors, our method performs on par or better in all scoring metrics tested. 

   more » « less
2. A Deep Learning Based Approach for prediction of arginine methylation sites in Proteins

   Chaudhari, Meenal; North Carolina Agricultural and Technical State University, Computational Science; North Carolina Agricultural and Technical State University, Computational Science; North Carolina Agricultural and Technical State University, CS Newman; North Carolina Agricultural and Technical State University, Biology Saigo; Kyushu University, Informatics Dukka; Wichita State University, Electrical Engineering (May 2020, Molecular omics)

   Methylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine learning strategies, such as support vector machines and random forest, to predict sites of methylation based on a set of “hand-selected” features. As a consequence, the subsequent models may be biased toward one set of features. Moreover, due to the large number of features, model development can often be computationally expensive. In this paper, we propose an alternative approach based on deep learning to predict arginine methylation sites. Our model, which we termed DeepRMethylSite, is computationally less expensive than traditional feature-based methods while eliminating potential biases that can arise through features selection. Based on independent testing on our dataset, DeepRMethylSite achieved efficiency scores of 68%, 82% and 0.51 with respect to sensitivity (SN), specificity (SP) and Matthew’s correlation coefficient (MCC), respectively. Importantly, in side-by-side comparisons with other state-of-the-art methylation site predictors, our method performs on par or better in all scoring metrics tested. 

   more » « less
3. DeepPhoPred : Accurate Deep Learning Model to Predict Microbial Phosphorylation

   https://doi.org/10.1002/prot.26734  

   Ahmed, Faisal; Sharma, Alok; Shatabda, Swakkhar; Dehzangi, Iman (February 2025, Proteins: Structure, Function, and Bioinformatics)

   ABSTRACT Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host–pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low‐cost and high‐speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho‐serine (pS), phospho‐threonine (pT), and phospho‐tyrosine (pY) sites. DeepPhoPred incorporates a two‐headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep‐learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available athttps://github.com/faisalahm3d/DeepPhoPred. 

   more » « less
4. REDS: Random ensemble deep spatial prediction

   https://doi.org/10.1002/env.2780  

   Daw, Ranadeep; Wikle, Christopher_K (December 2022, Environmetrics)

   Abstract There has been a great deal of recent interest in the development of spatial prediction algorithms for very large datasets and/or prediction domains. These methods have primarily been developed in the spatial statistics community, but there has been growing interest in the machine learning community for such methods, primarily driven by the success of deep Gaussian process regression approaches and deep convolutional neural networks. These methods are often computationally expensive to train and implement and consequently, there has been a resurgence of interest in random projections and deep learning models based on random weights—so called reservoir computing methods. Here, we combine several of these ideas to develop the random ensemble deep spatial (REDS) approach to predict spatial data. The procedure uses random Fourier features as inputs to an extreme learning machine (a deep neural model with random weights), and with calibrated ensembles of outputs from this model based on different random weights, it provides a simple uncertainty quantification. The REDS method is demonstrated on simulated data and on a classic large satellite data set. 

   more » « less
5. Predicting Average Product Lifetime Using Physics-Based Gaussian Process Method in Early Design Stage

   https://doi.org/10.1115/DETC2020-22199  

   Wei, Xinpeng; Han, Daoru; Du, Xiaoping (August 2020, Proceedings of the ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   null (Ed.)

   Abstract Average lifetime, or mean time to failure (MTTF), of a product is an important metric to measure the product reliability. Current methods of evaluating MTTF are mainly statistics or data based. They need lifetime testing on a number of products to get the lifetime samples, which are then used to estimate MTTF. The lifetime testing, however, is expensive in terms of both time and cost. The efficiency is also low because it cannot be effectively incorporated in the early design stage where many physics-based models are available. We propose to predict MTTF in the design stage by means of physics-based models. The advantage is that the design can be continually improved by changing design variables until reliability measures, including MTTF, are satisfied. Since the physics-based models are usually computationally demanding, we face a problem with both big data (on the model input side) and small data (on the model output side). We develop an adaptive supervised training method based on Gaussian process regression, and the method can then quickly predict MTTF with minimized number of calling the physics-based models. The effectiveness of the method is demonstrated by two examples. 

   more » « less