Attention-Based Deep Multiple-Instance Learning for Classifying Circular RNA and Other Long Non-Coding RNA

Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture fed with a raw sequence, to learn the sparse features of RNA sequences and to accomplish the circRNAs identification task. The model outperformed the state-of-art models. Moreover, following the validation of the attention mechanism effectiveness by the handwritten digit dataset, the key sequence loci underlying circRNA’s recognition were obtained based on the corresponding attention score. Then, motif enrichment analysis identified some of the key motifs for circRNA formation. In conclusion, we designed deep learning network architecture suitable for learning gene sequences with sparse features and implemented it for the circRNA identification task, and the model has strong representation capability in the indication of some key loci.

Application of random forest regressions on stellar parameters of A-type stars and feature extraction

Measuring the stellar parameters of A-type stars is more difficult than FGK stars because of the sparse features in their spectra and the degeneracy between effective temperature (Teff ) and gravity (logg). Modeling the relationship between fundamental stellar parameters and features through Machine Learning is possible because we can employ the advantage of big data rather than sparse known features. As soon as the model is successfully trained, it can be an efficient approach for predicting Teff and logg for A-type stars especially when there is large uncertainty in the continuum caused by flux calibration or extinction. In this paper, A- type stars are selected from LAMOST DR7 with signal-to-noise ratio greater than 50 and the Teff ranging within 7000K to 8500K. We perform the Random Forest (RF) algorithm, one of the most widely used Machine Learning algorithms to establish the regressio，relationship between the flux of all wavelengths and their corresponding stellar parameters((Teff ) and (logg) respectively). The trained RF model not only can regress the stellar parameters but also can obtain the rank of the wavelength based on their sensibility to parameters.According to the rankings, we define line indices by merging adjacent wavelengths. The objectively defined line indices in this work are amendments to Lick indices including some weak lines. We use the Support Vector Regression algorithm based on our new defined line indices to measure the temperature and gravity and use some common stars from Simbad to evaluate our result. In addition, the Gaia HR diagram is used for checking the accuracy of Teff and logg.

Sparse Neural Network for horse face detection in a Smart Riding Club Biometric System

Horses and breeders need to be safe on the farm and the riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and to recognize the identity of each horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse and the gait biometrics for a human due to their simplicity and intuitiveness in an uncontrolled environment. The present work focuses mainly on horse face detection and recognition. Animal face detection is still extremely difficult given the fact that face textures and shapes are grossly diverse. In addition, recent detectors require a huge dataset for training and represent a huge number of parameters and layers, leading to so much training time. For resolving these problems and also for a useful detection system, this paper proposes a Sparse Neural Network (SNN) based on sparse features for horse face detection.<br>Different global and local features were performed to identify horses and humans for the recognition process. Due to the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. This system achieved an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.

Sparse Neural Network for horse face detection in a Smart Riding Club Biometric System

Horses and breeders need to be safe on the farm and the riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and to recognize the identity of each horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse and the gait biometrics for a human due to their simplicity and intuitiveness in an uncontrolled environment. The present work focuses mainly on horse face detection and recognition. Animal face detection is still extremely difficult given the fact that face textures and shapes are grossly diverse. In addition, recent detectors require a huge dataset for training and represent a huge number of parameters and layers, leading to so much training time. For resolving these problems and also for a useful detection system, this paper proposes a Sparse Neural Network (SNN) based on sparse features for horse face detection.<br>Different global and local features were performed to identify horses and humans for the recognition process. Due to the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. This system achieved an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.

Attention-Based Deep Multiple Instances Learning For Classifying Circular RNA and Other Long Non-Coding RNA

Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture, which can be fed with raw sequence, to learn the sparse features in sequences and accomplish the identification task for circRNAs. The model outperformed previously reported models. Following the effectiveness validation of the attention score by the handwritten digit dataset, the key sequence loci underlying circRNAs recognition were obtained based on the corresponding attention score. Moreover, the motif enrichment analysis of the extracted key sequences identified some of the key motifs for circRNA formation. In conclusion, we designed a deep learning network architecture suitable for gene sequence learning with sparse features and implemented to the circRNA identification, and the network has a strong representation capability with its indication of some key loci.

Attention-based deep multiple instances learning for classifying circular RNA and other long non-coding RNA

Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture, which can be fed with raw sequence, to learn the sparse features in sequences and accomplish the identification task for circRNAs. The model outperformed previously reported models. Following the effectiveness validation of the attention score by the handwritten digit dataset, the key sequence loci underlying circRNAs recognition were obtained based on the corresponding attention score. Moreover, the motif enrichment analysis of the extracted key sequences identified some of the key motifs for circRNA formation. In conclusion, we designed a deep learning network architecture suitable for gene sequence learning with sparse features and implemented to the circRNA identification, and the network has a strong representation capability with its indication of some key loci.

Multi-Stage Convolutional Broad Learning with Block Diagonal Constraint for Hyperspectral Image Classification

By combining the broad learning and a convolutional neural network (CNN), a block-diagonal constrained multi-stage convolutional broad learning (MSCBL-BD) method is proposed for hyperspectral image (HSI) classification. Firstly, as the linear sparse feature extracted by the conventional broad learning method cannot fully characterize the complex spatial-spectral features of HSIs, we replace the linear sparse features in the mapped feature (MF) with the features extracted by the CNN to achieve more complex nonlinear mapping. Then, in the multi-layer mapping process of the CNN, information loss occurs to a certain degree. To this end, the multi-stage convolutional features (MSCFs) extracted by the CNN are expanded to obtain the multi-stage broad features (MSBFs). MSCFs and MSBFs are further spliced to obtain multi-stage convolutional broad features (MSCBFs). Additionally, in order to enhance the mutual independence between MSCBFs, a block diagonal constraint is introduced, and MSCBFs are mapped by a block diagonal matrix, so that each feature is represented linearly only by features of the same stage. Finally, the output layer weights of MSCBL-BD and the desired block-diagonal matrix are solved by the alternating direction method of multipliers. Experimental results on three popular HSI datasets demonstrate the superiority of MSCBL-BD.