isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection

Rahman, Mizanur; Rahman, Md. Khaledur; Kaykobad, M.; Rahman, M. Sohel

doi:10.1016/j.artmed.2017.11.003

Cited by 47 publications

(35 citation statements)

References 49 publications

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“…Hence, the exact classification of GA proteins may advance diagnosis and basic research, and contribute to targeted drug development in CDG. The identification of the sub-Golgi protein types (isGPT) model using RF and SVM has been developed to accurately identify sub-Golgi protein types, namely trans-and cis-Golgi proteins [89].…”

Section: Identification Of Golgi Proteinsmentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

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The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

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Section: Identification Of Golgi Proteinsmentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

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“…A characteristic feature of CRISPRpred(SEQ) is that, unlike the previous models, it only focuses on sequence based features. This is motivated by the empirical assertion of the natural belief (please see the recent Ph.D. thesis of Rahman [19] and the published results thereof in [20,21,22]) that the functional and structural information of a biological sequence are intrinsically encoded within its primary sequence. Indeed, our results in this research work further strengthen this assertion empirically as CRISPRpred(SEQ) has performed exceptionally well and has almost beaten the state-of-the-art deep neural networking pipeline (i.e., Deep-CRISPR) leveraging only traditional machine learning techniques and focusing only on primary sequence based features.…”

Section: Our Contributionsmentioning

confidence: 99%

“…Two types of features were already used in CRISPRpred [10]. We added a new type of feature called n-gapped dipeptide which was used in [34]. Nevertheless, the features are described below for the sake of completeness.…”

Section: Feature Extractionmentioning

confidence: 99%

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CRISPRpred(SEQ): a sequence based tool for sgRNA on target activity prediction [(almost) beating Deep Learning pipelines by traditional machine learning]

Rafid¹,

Toufikuzzaman²,

Rahman³

et al. 2019

Preprint

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An accurate and fast genome editing tool can be used to treat genetic diseases, modify crops genetically etc. However, a tool that has low accuracy can be risky to use, as incorrect genome editing may have severe consequences. Although many tools have been developed in the past, there are still room for further improvement. In this paper, we present CRISPRpred(SEQ), a sequence based tool for sgRNA on target activity prediction that leverages only traditional machine learning techniques. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. In spite of using only traditional machine learning methods, we are able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines), which is quite outstanding. *

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“…Various tools exist that extract features from DNA/protein sequences for prediction purposes, e.g., some tools extract features from genomic sequences to predict on-target activity in CRISPR/Cas9 technology [6,22] whereas other tools extract features from protein sequences to make efficient predictions [5,21,23,24]. But, almost all authors use a sequential approach [2,18,19] for feature extraction.…”

mentioning

confidence: 99%

FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N⁶-methyladenine sites

Rahman

2019

Preprint

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N 6 -methyladenine is widely found in both prokaryotes and eukaryotes. It is responsible for many biological processes including prokaryotic defense system and human diseases. So, it is important to know its correct location in genome which may play a significant role in different biological functions. Few computational tools exist to serve this purpose but they are computationally expensive and still there is scope to improve accuracy. An informative feature extraction pipeline from genome sequences is the heart of these tools as well as for many other bioinformatics tools. But it becomes reasonably expensive for sequential approaches when the size of data is large. Hence, a scalable parallel approach is highly desirable. In this paper, we have developed a new tool, called FastFeatGen, emphasizing both developing a parallel feature extraction technique and improving accuracy using machine learning methods. We have implemented our feature extraction approach using shared memory parallelism which achieves around 10× speed over the sequential one. Then we have employed an exploratory feature selection technique which helps to find more relevant features that can be fed to machine learning methods. We have employed Extra-Tree Classifier (ETC) in FastFeatGen and performed experiments on rice and mouse genomes. Our experimental results achieve accuracy of 85.57% and 96.64%, respectively, which are better or competitive to current stateof-the-art methods. Our shared memory based tool can also serve queries much faster than sequential technique. All source codes and datasets are available at https://github.com/khaled-rahman/FastFeatGen.

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isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection

Cited by 47 publications

References 49 publications

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

CRISPRpred(SEQ): a sequence based tool for sgRNA on target activity prediction [(almost) beating Deep Learning pipelines by traditional machine learning]

FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N⁶-methyladenine sites

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isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection

Cited by 47 publications

References 49 publications

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

CRISPRpred(SEQ): a sequence based tool for sgRNA on target activity prediction [(almost) beating Deep Learning pipelines by traditional machine learning]

FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N6-methyladenine sites

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FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N⁶-methyladenine sites