Feature Selection and Class-Weight Tuning Using Genetic Algorithm for Bio-molecular Event Extraction

Majumder, Amit; Ekbal, Asif; Naskar, Sudip Kumar

doi:10.1007/978-3-319-59569-6_3

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Feature selection. To determine the most relevant set of features to make the characterization more accurate and efficient, a novel feature selection algorithm was pursued utilizing a combination of the genetic algorithm and random forest method (GA–RF) . As a result, the GA–RF was used to select a set of salient features from all inputs …”

Section: Methodsmentioning

confidence: 99%

Dual Self-Adaptive Intelligent Optimization of Feature and Hyperparameter Determination in Constructing a DNN Based QSPR Property Prediction Model

Huang

Chen

et al. 2022

Ind. Eng. Chem. Res.

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The octanol–water partition coefficient (K ow) is an extremely important and widely used parameter for the study of the distribution and balance of organic pollutants in environmental media. Therefore, the theoretical determination and prediction of such a property are vital in environmental chemistry. The past research on the use of models based on the quantitative structure–property relationship (QSPR) for the estimation of K ow has significant problems such as data redundancy and computational complexity in the molecular description. In this work, the genetic algorithm is coupled with the random forest algorithm to select the most suitable molecular feature combination from a vast number of features. As a consequence, the number of descriptors for developing the model recommended in available models is significantly reduced. Moreover, a combination of the backpropagation neural network and Bayesian optimization allows the development of an intelligent procedure for tuning the relevant model parameters. On the basis of comparison of the obtained estimations to the results of the available QSPR models in the literature, the developed model in this work shows considerably higher accuracy and predictability.

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Section: Methodsmentioning

confidence: 99%

Dual Self-Adaptive Intelligent Optimization of Feature and Hyperparameter Determination in Constructing a DNN Based QSPR Property Prediction Model

Huang

Chen

et al. 2022

Ind. Eng. Chem. Res.

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“…Like trigger detection, there have been more efforts in using ML algorithms by treating the edge detection problem as a supervised multiclass classification problem. Also, many studies are based on hybrid approaches by using a combination of the above methods or using ensemble methodology [47,48] as shown in Table 5. Here, the thought process is not to choose the 'best' classifier always, as it may not be representative of all data.…”

Section: Edge Detectionmentioning

confidence: 99%

Biomolecular Event Extraction using Natural Language Processing

Bali

Anandaraj

2023

Int. j. electr. comput. eng. syst. (Online)

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Biomedical research and discoveries are communicated through scholarly publications and this literature is voluminous, rich in scientific text and growing exponentially by the day. Biomedical journals publish nearly three thousand research articles daily, making literature search a challenging proposition for researchers. Biomolecular events involve genes, proteins, metabolites, and enzymes that provide invaluable insights into biological processes and explain the physiological functional mechanisms. Text mining (TM) or extraction of such events automatically from big data is the only quick and viable solution to gather any useful information. Such events extracted from biological literature have a broad range of applications like database curation, ontology construction, semantic web search and interactive systems. However, automatic extraction has its challenges on account of ambiguity and the diverse nature of natural language and associated linguistic occurrences like speculations, negations etc., which commonly exist in biomedical texts and lead to erroneous elucidation. In the last decade, many strategies have been proposed in this field, using different paradigms like Biomedical natural language processing (BioNLP), machine learning and deep learning. Also, new parallel computing architectures like graphical processing units (GPU) have emerged as possible candidates to accelerate the event extraction pipeline. This paper reviews and provides a summarization of the key approaches in complex biomolecular big data event extraction tasks and recommends a balanced architecture in terms of accuracy, speed, computational cost, and memory usage towards developing a robust GPU-accelerated BioNLP system.

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Programs Features Clustering to Find Optimization Sequence Using Genetic Algorithm

Almohammed

Alwan

Fanfakh

2020

Learning and Analytics in Intelligent Systems

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Feature Selection and Class-Weight Tuning Using Genetic Algorithm for Bio-molecular Event Extraction

Cited by 5 publications

References 6 publications

Dual Self-Adaptive Intelligent Optimization of Feature and Hyperparameter Determination in Constructing a DNN Based QSPR Property Prediction Model

Dual Self-Adaptive Intelligent Optimization of Feature and Hyperparameter Determination in Constructing a DNN Based QSPR Property Prediction Model

Biomolecular Event Extraction using Natural Language Processing

Programs Features Clustering to Find Optimization Sequence Using Genetic Algorithm

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