The Development of Gordon Life Science Institute: Its Driving Force and Accomplishments

Chou, Kuo‐Chen

doi:10.4236/ns.2020.124018

Cited by 16 publications

(3 citation statements)

References 119 publications

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“…Meanwhile, as a byproduct, the present paper has also stimulated some very interesting or challenging papers (see, e.g., [275][276][277][278][279][280]).…”

Section: Comparison With the State-of-the-art Predictormentioning

confidence: 73%

pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

Shao¹,

Liu²,

Lu³

et al. 2020

Self Cite

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Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called "pLoc_bal-mHum" was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a "deep learning", a very powerful technique developed recently. The present study was devoted to incorporate the "deep-learning" technique and develop a new predictor called "pLoc_Deep-mHum". The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

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“…Meanwhile, as a byproduct, the present paper has also stimulated some very interesting or challenging papers (see, e.g., [275][276][277][278][279][280]).…”

Section: Comparison With the State-of-the-art Predictormentioning

confidence: 73%

pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

Shao¹,

Liu²,

Lu³

et al. 2020

Self Cite

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“…This sequence-based statistical predictor operates based on the following five prime rules i.e. 5-step rule (Chou 2020a , b , c , d , e , f ; Fang et al 2020 ; Lin et al 2020 ; Liu and Chou 2020 ; Lu and Chou 2020 ; Shao and Chou 2020 ; Shao et al 2020 ; Xu et al 2020 ; Zhang et al 2020 ) which is (i) Construction or selection of an effective benchmark dataset for training and testing the sequence-based statistical predictor, (ii) Formulation of the effectual biological sequence tasters with an operative measured expression to accurately replicate the intrinsic relation of the biological sequence with the target to be prophesied, (iii) Development of a productive and efficacious algorithm for operating the prediction, (iv) Execution of persuasive cross-validation trials to factually assess the projected precision of the predictor, and (v) The inception of a comprehensible and foolproof web-server regarding the predictor and to ensure its receptiveness and accessibility to the public.…”

Section: Introductionmentioning

confidence: 99%

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

Amin

Awais

Sahai

et al. 2021

Int J Pept Res Ther

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DNA replication is one of the specific processes to be considered in all the living organisms, specifically eukaryotes. The prevalence of DNA replication is significant for an evolutionary transition at the beginning of life. DNA replication proteins are those proteins which support the process of replication and are also reported to be important in drug design and discovery. This information depicts that DNA replication proteins have a very important role in human bodies, however, to study their mechanism, their identification is necessary. Thus, it is a very important task but, in any case, an experimental identification is time-consuming, highly-costly and laborious. To cope with this issue, a computational methodology is required for prediction of these proteins, however, no prior method exists. This study comprehends the construction of novel prediction model to serve the proposed purpose. The prediction model is developed based on the artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. Highest overall accuracy has been achieved through tenfold cross-validation and Jackknife testing that was computed to be 96.22% and 98.56%, respectively. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a time and cost-effective stratagem for designing novel drugs to strike the contemporary bacterial infection.

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“…16,17 In 2015, very powerful webservers such as "Pse-in-One" 18 and its updated version "Pse-in-One2.0" 19 were established and used to generate any desired feature vectors for protein/peptide and DNA/RNA sequences. 19 We are inspired by the work done to extract relevant features, the use of webservers, and also by Chou's 5-step rule 20 as fully introduced in our method section (the rule has been widely employed in a variety of applications in driving proteome/genome analysis and drug development, as seen in recent papers [20][21][22] ). In our work, we will follow the 5-step rule to formulate an algorithm that requires first obtaining important features (eg, genomic, proteomic, transcriptomic and clinical outcome information) and then applying ML to extract pattern information between our chosen attributes and clinical outcome.…”

mentioning

confidence: 99%

Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials

Schperberg

Boichard

Tsigelny

et al. 2020

Intl Journal of Cancer

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Predicting oncologic outcome is challenging due to the diversity of cancer histologies and the complex network of underlying biological factors. In this study, we determine whether machine learning (ML) can extract meaningful associations between oncologic outcome and clinical trial, drug‐related biomarker and molecular profile information. We analyzed therapeutic clinical trials corresponding to 1102 oncologic outcomes from 104 758 cancer patients with advanced colorectal adenocarcinoma, pancreatic adenocarcinoma, melanoma and nonsmall‐cell lung cancer. For each intervention arm, a dataset with the following attributes was curated: line of treatment, the number of cytotoxic chemotherapies, small‐molecule inhibitors, or monoclonal antibody agents, drug class, molecular alteration status of the clinical arm's population, cancer type, probability of drug sensitivity (PDS) (integrating the status of genomic, transcriptomic and proteomic biomarkers in the population of interest) and outcome. A total of 467 progression‐free survival (PFS) and 369 overall survival (OS) data points were used as training sets to build our ML (random forest) model. Cross‐validation sets were used for PFS and OS, obtaining correlation coefficients (r) of 0.82 and 0.70, respectively (outcome vs model's parameters). A total of 156 PFS and 110 OS data points were used as test sets. The Spearman correlation (rs) between predicted and actual outcomes was statistically significant (PFS: rs = 0.879, OS: rs = 0.878, P < .0001). The better outcome arm was predicted in 81% (PFS: N = 59/73, z = 5.24, P < .0001) and 71% (OS: N = 37/52, z = 2.91, P = .004) of randomized trials. The success of our algorithm to predict clinical outcome may be exploitable as a model to optimize clinical trial design with pharmaceutical agents.

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The Development of Gordon Life Science Institute: Its Driving Force and Accomplishments

Cited by 16 publications

References 119 publications

pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials

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