Fast prediction of protein–protein interaction sites based on Extreme Learning Machines

Wang, Debby D.; Wang, Ran; Yan, Hong

doi:10.1016/j.neucom.2012.12.062

Cited by 71 publications

(27 citation statements)

References 39 publications

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“…In fact, it has been proved that by utilizing the ELM, learning becomes very fast and it produces good generalization performance [31]. Researchers have applied ELM for solving problems in many scientific areas [32][33][34][35][36][37]. ELM is a powerful algorithm with faster learning speed comparing with traditional algorithms like back-propagation (BP).…”

Section: Introductionmentioning

confidence: 99%

Extreme learning machine approach for sensorless wind speed estimation

et al. 2016

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Section: Introductionmentioning

confidence: 99%

Extreme learning machine approach for sensorless wind speed estimation

et al. 2016

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“…This motivates this paper to apply ELM to estimate the gravity disturbance on the trajectory [11]. The ELM-based gravity disturbance estimation algorithm is utilized in the training process to establish the prediction model, with the carrier position (longitude and latitude) that the INS provided as input and the gravity disturbance on the geoid as output, then processes the obtained gravity disturbance to the height (provided by the altimeter) where INS has an upward continuation [12,13]. Finally, the estimated gravity disturbance on the trajectory is compensated in the INS error equations incorporated with gravity disturbance to restrain the error propagation in INS.…”

Section: Introductionmentioning

confidence: 99%

A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”

Zhou

Yang

Cai

et al. 2016

Sensors

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In recent years, with the emergency of high precision inertial sensors (accelerometers and gyros), gravity compensation has become a major source influencing the navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper presents preliminary results concerning the effect of gravity disturbance on INS. Meanwhile, this paper proposes a novel gravity compensation method for high-precision INS, which estimates the gravity disturbance on the track using the extreme learning machine (ELM) method based on measured gravity data on the geoid and processes the gravity disturbance to the height where INS has an upward continuation, then compensates the obtained gravity disturbance into the error equations of INS to restrain the INS error propagation. The estimation accuracy of the gravity disturbance data is verified by numerical tests. The root mean square error (RMSE) of the ELM estimation method can be improved by 23% and 44% compared with the bilinear interpolation method in plain and mountain areas, respectively. To further validate the proposed gravity compensation method, field experiments with an experimental vehicle were carried out in two regions. Test 1 was carried out in a plain area and Test 2 in a mountain area. The field experiment results also prove that the proposed gravity compensation method can significantly improve the positioning accuracy. During the 2-h field experiments, the positioning accuracy can be improved by 13% and 29% respectively, in Tests 1 and 2, when the navigation scheme is compensated by the proposed gravity compensation method.

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“…A protein secondary prediction framework based on ELM was proposed in [33] to provide good performance at extremely high speed. The work in [34] implemented the protein-protein interaction prediction on multi-chain sets and on single-chain sets using ELM and SVM for a comparable study. In both cases, ELM tends to obtain higher recall values than SVM and shows a remarkable advantage in computational speed.…”

Section: A Brief Introduction To Elmmentioning

confidence: 99%

Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Yin

Zhao

et al. 2016

Applied Sciences

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Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks:(1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML)-extreme learning machine (ELM), to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.

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Fast prediction of protein–protein interaction sites based on Extreme Learning Machines

Cited by 71 publications

References 39 publications

Extreme learning machine approach for sensorless wind speed estimation

Extreme learning machine approach for sensorless wind speed estimation

A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”

Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

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