A Neural Network based Cluster Ensemble Approach for Biological Data Clustering

AbstractThe increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system combines pre-processing of full-text articles in a machine-readable format with relevant sentence extraction for downstream NLP analysis. Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy and F1 scores of 95.1%, 82.8%, 89.3% and 88.4% respectively (n = 30). Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction model. We provide a framework for extracting protein functional features from unstructured text in the literature with high accuracy, improving data dissemination and unlocking the potential of big data text analytics for generating new hypotheses.

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Document triage for identifying protein–protein interactions affected by mutations: a neural network ensemble approach

Database ◽

10.1093/database/bay097 ◽

2018 ◽

Vol 2018 ◽

Cited By ~ 2

Author(s):

Ling Luo ◽

Zhihao Yang ◽

Hongfei Lin ◽

Jian Wang

Keyword(s):

Neural Network ◽

Protein Interactions ◽

Neural Network Ensemble ◽

Protein Protein Interactions ◽

Ensemble Approach ◽

Document Triage

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Study of neural network algorithm of the earth remote sensing data clustering

2014 24th International Crimean Conference Microwave & Telecommunication Technology ◽

10.1109/crmico.2014.6959836 ◽

2014 ◽

Cited By ~ 1

Author(s):

O. Yu. Ivanov ◽

A. S. Vorobyova

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Data Clustering ◽

Remote Sensing Data ◽

Earth Remote Sensing ◽

Sensing Data ◽

The Earth ◽

Network Algorithm ◽

Neural Network Algorithm

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Deep Belief Network for Predicting the Predisposition to Lung Cancer in TP53 Gene

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.1.19 ◽

2020 ◽

pp. 171-177 ◽

Cited By ~ 1

Author(s):

Zahraa Naser Shahweli

Keyword(s):

Neural Network ◽

Lung Cancer ◽

Smoking Habit ◽

Back Propagation ◽

Tp53 Gene ◽

Back Propagation Neural Network ◽

Biological Data ◽

Deep Belief Network ◽

Restricted Boltzmann Machines ◽

Belief Network

Lung cancer, similar to other cancer types, results from genetic changes. However, it is considered as more threatening due to the spread of the smoking habit, a major risk factor of the disease. Scientists have been collecting and analyzing the biological data for a long time, in attempts to find methods to predict cancer before it occurs. Analysis of these data requires the use of artificial intelligence algorithms and neural network approaches. In this paper, one of the deep neural networks was used, that is the enhancer Deep Belief Network (DBN), which is constructed from two Restricted Boltzmann Machines (RBM). The visible nodes for the first RBM are 13 nodes and 8 nodes in each hidden layer for the two RBMs. The enhancer DBN was trained by Back Propagation Neural Network (BPNN), where the data sets were divided into 6 folds, each is split into three partitions representing the training, validation, and testing. It is worthy to note that the proposed enhancer DBN predicted lung cancer in an acceptable manner, with an average F-measure value of 0. 96 and an average Matthews Correlation Coefficient (MCC) value of 0. 47 for 6 folds.

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Optimized artificial neural network for classification of biological data

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.11065 ◽

2018 ◽

Vol 7 (2) ◽

pp. 817

Author(s):

Senthilselvan Natarajan ◽

Rajarajan S ◽

Subramaniyaswamy V

Keyword(s):

Neural Network ◽

Mental Stress ◽

Causes Of Death ◽

Biological Data ◽

High Dimensionality ◽

Soft Computing Techniques ◽

Artificial Neural ◽

Multi Class Classification ◽

Neural Networks Optimization

Biological data suffers from the problem of high dimensionality which makes the process of multi-class classification difficult and also these data have elements that are incomplete and redundant. Breast Cancer is currently one of the most pre-dominant causes of death in women around the globe. The current methods for classifying a tumour as malignant or benign involve physical procedures. This often leads to mental stress. Research has now sought to implement soft computing techniques in order to classify tumours based on the data available. In this paper, a novel classifier model is implemented using Artificial Neural Networks. Optimization is done in this neural network by using a meta-heuristic algorithm called the Whale Swarm Algorithm in order to make the classifier model accurate. Experimental results show that new technique outperforms other existing models.

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