Special Issue "Big Data in Biology, Life Sciences and Healthcare"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 50268

Special Issue Editors

Dr. Peter He
E-Mail Website
Guest Editor
Chemical Engineering, Auburn University, Auburn, AL 36849, USA
Interests: smart manufacturing; big data; data analytics; cancer informatics; modeling and control
Dr. Jin Wang
E-Mail Website
Guest Editor
Department of Chemical Engineering, Auburn University, Auburn, AL 36849, USA
Interests: apply systems engineering approaches, control engineering principles and techniques in particular, to understand, predict, and control complex dynamic processes

Special Issue Information

Dear Colleagues,

Massive quantities of data are being generated in biology, the life sciences and healthcare industries and institutions, which hold the promise of advancing our understandings of various biological systems and diseases, developing new biocatalysts and drugs, as well as delivering more effective patient care and reducing costs, etc. In this Special Issue, we seek research and case studies that demonstrate the application of big data modeling and analysis to support scientific research, drug development, clinical decision making, personalized medicine, and other critical tasks. Example topics include (but are not limited to) the following topics relating big data to biology, the life sciences or healthcare:

  • Novel systems engineering approaches, including modeling and numerical analysis algorithms
  • New statistical tools and algorithms
  • Machine learning and artificial intelligence
  • Integration of systems engineering approaches with machine learning
  • Novel visualization approaches
  • Computer or model-aided diagnostics
  • Model-based drug development
  • Evidence-based medicine
  • Modeling and analysis of data from a multitude of sources
  • Application of wearable devices
  • Public health surveillance

Prof. Peter He
Prof. Jin Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • omics data
  • electronic health record
  • modeling
  • monitoring
  • optimization
  • visualization
  • statistical analysis
  • machine learning
  • artificial intelligence
  • systems biology
  • biocatalyst
  • healthcare
  • drug development

Published Papers (15 papers)

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Editorial

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Editorial
Special Issue on “Big Data in Biology, Life Sciences and Healthcare”
Processes 2022, 10(1), 41; https://doi.org/10.3390/pr10010041 - 27 Dec 2021
Viewed by 1394
Abstract
In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare [...] Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)

Research

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Article
MobileNetV2 Ensemble for Cervical Precancerous Lesions Classification
Processes 2020, 8(5), 595; https://doi.org/10.3390/pr8050595 - 16 May 2020
Cited by 24 | Viewed by 4905
Abstract
Women’s cancers remain a major challenge for many health systems. Between 1991 and 2017, the death rate for all major cancers fell continuously in the United States, excluding uterine cervix and uterine corpus cancers. Together with HPV (Human Papillomavirus) testing and cytology, colposcopy [...] Read more.
Women’s cancers remain a major challenge for many health systems. Between 1991 and 2017, the death rate for all major cancers fell continuously in the United States, excluding uterine cervix and uterine corpus cancers. Together with HPV (Human Papillomavirus) testing and cytology, colposcopy has played a central role in cervical cancer screening. This medical procedure allows physicians to view the cervix at a magnification of up to 10%. This paper presents an automated colposcopy image analysis framework for the classification of precancerous and cancerous lesions of the uterine cervix. This framework is based on an ensemble of MobileNetV2 networks. Our experimental results show that this method achieves accuracies of 83.33% and 91.66% on the four-class and binary classification tasks, respectively. These results are promising for the future use of automatic classification methods based on deep learning as tools to support medical doctors. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Article
A Proposal for Public Health Information System-Based Health Promotion Services
Processes 2020, 8(3), 338; https://doi.org/10.3390/pr8030338 - 15 Mar 2020
Cited by 3 | Viewed by 4102
Abstract
This study aims to examine the current status and utilization of 22 health promotion projects that use the health care information system. We investigate the health promotion examination results for a counseling project held at health centers, which use information connected with the [...] Read more.
This study aims to examine the current status and utilization of 22 health promotion projects that use the health care information system. We investigate the health promotion examination results for a counseling project held at health centers, which use information connected with the Health Insurance Corporation. First, we review the status of 22 health promotion projects, including 13 integrated health promotion projects and 9 other health promotion projects. Next, we examine the linkages between the 22 projects and other health promotion systems. Consequently, despite accumulating vast amounts of data, only 10 places could be linked to health promotion data in the health and medical information system; the Social Security Information Service was the only exception to this trend. The Public Health Information System (PHIS) had the lowest data utilization rate in the project. The study results show that it is necessary to utilize data from local health and medical institutions in order to provide information system-based health promotion services. In particular, it seems to be effective when health and medical institutions provided various counseling services and other linked services to local residents in connection with the Korea Health Insurance Corporation’s health examination results. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Article
Detection of Drivers’ Anxiety Invoked by Driving Situations Using Multimodal Biosignals
Processes 2020, 8(2), 155; https://doi.org/10.3390/pr8020155 - 25 Jan 2020
Cited by 14 | Viewed by 2914
Abstract
It has become increasingly important to monitor drivers’ negative emotions during driving to prevent accidents. Despite drivers’ anxiety being critical for safe driving, there is a lack of systematic approaches to detect anxiety in driving situations. This study employed multimodal biosignals, including electroencephalography [...] Read more.
It has become increasingly important to monitor drivers’ negative emotions during driving to prevent accidents. Despite drivers’ anxiety being critical for safe driving, there is a lack of systematic approaches to detect anxiety in driving situations. This study employed multimodal biosignals, including electroencephalography (EEG), photoplethysmography (PPG), electrodermal activity (EDA) and pupil size to estimate anxiety under various driving situations. Thirty-one drivers, with at least one year of driving experience, watched a set of thirty black box videos including anxiety-invoking events, and another set of thirty videos without them, while their biosignals were measured. Then, they self-reported anxiety-invoked time points in each video, from which features of each biosignal were extracted. The logistic regression (LR) method classified single biosignals to detect anxiety. Furthermore, in the order of PPG, EDA, pupil, and EEG (easiest to hardest accessibility), LR classified accumulated multimodal signals. Classification using EEG alone showed the highest accuracy of 77.01%, while other biosignals led to a classification with accuracy no higher than the chance level. This study exhibited the feasibility of utilizing biosignals to detect anxiety invoked by driving situations, demonstrating benefits of EEG over other biosignals. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Article
An Analysis of Antimicrobial Resistance of Clinical Pathogens from Historical Samples for Six Countries
Processes 2019, 7(12), 964; https://doi.org/10.3390/pr7120964 - 17 Dec 2019
Cited by 7 | Viewed by 2383
Abstract
The spread of antimicrobial resistance pathogens in humans has increasingly become an issue that threatens public health. While the NCBI Pathogen Detection Isolates Browser (NPDIB) database has been collecting clinical isolate samples over time for various countries, few studies have been done to [...] Read more.
The spread of antimicrobial resistance pathogens in humans has increasingly become an issue that threatens public health. While the NCBI Pathogen Detection Isolates Browser (NPDIB) database has been collecting clinical isolate samples over time for various countries, few studies have been done to identify genes and pathogens responsible for the antimicrobial resistance in clinical settings. This study conducted the first multivariate statistical analysis of the high-dimensional historical data from the NPDIB database for six different countries from majorly inhabited landmasses, including Australia, Brazil, China, South Africa, the UK, and the US. The similarities among different countries in terms of genes and pathogens were investigated to understand the potential avenues for antimicrobial-resistance gene spreading. The genes and pathogens that were closely involved in antimicrobial resistance were further studied temporally by plotting time profiles of their frequency to evaluate the trend of antimicrobial resistance. It was found that several of these significant genes (i.e., aph(3″)-Ib, aph(6)-Id, blaTEM-1, and qacEdelta1) are shared among all six countries studied. Based on the time profiles, a large number of genes and pathogens showed an increasing occurrence. The most shared pathogens responsible for carrying the most important genes in the six countries in the clinical setting were Acinetobacter baumannii, E. coli and Shigella, Klebsiella pneumoniae and Salmonella enterica. South Africa carried the least similar antimicrobial genes to the other countries in clinical isolates. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Article
Proposal of a Learning Health System to Transform the National Health System of Spain
Processes 2019, 7(9), 613; https://doi.org/10.3390/pr7090613 - 10 Sep 2019
Cited by 3 | Viewed by 2807
Abstract
This article identifies the main challenges of the National Health Service of Spain and proposes its transformation into a Learning Health System. For this purpose, the main indicators and reports published by the Spanish Ministries of Health and Finance, Organization for Economic Co-operation [...] Read more.
This article identifies the main challenges of the National Health Service of Spain and proposes its transformation into a Learning Health System. For this purpose, the main indicators and reports published by the Spanish Ministries of Health and Finance, Organization for Economic Co-operation and Development (OECD) and World Health Organization (WHO) were reviewed. The Learning Health System proposal is based on some sections of an unpublished report, written by two of the authors under request of the Ministry of Health of Spain on Big Data for the National Health System. The main challenges identified are the rising old age dependency ratio; health expenditure pressures and the likely increase of out-of-pocket expenditure; drug expenditures, both retail and consumed in hospitals; waiting lists for surgery; potentially preventable hospital admissions; and the use of electronic health record (EHR) data to fulfil national health information and research objectives. To improve its efficacy, efficiency, and quality, the National Health Service of Spain should be transformed into a Learning Health System. Information and communication technologies (IT) enablers are a fundamental tool to address the complexity and vastness of health data as well as the urgency that clinical and management decisions require. Big Data solutions are a perfect match for that problem in health systems. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Article
Key Points for an Ethical Evaluation of Healthcare Big Data
Processes 2019, 7(8), 493; https://doi.org/10.3390/pr7080493 - 01 Aug 2019
Cited by 7 | Viewed by 3970
Abstract
Background: The article studies specific ethical issues arising from the use of big data in Life Sciences and Healthcare. Methods: Main consensus documents, other studies, and particular cases are analyzed. Results: New concepts that emerged in five key areas for the bioethical debate [...] Read more.
Background: The article studies specific ethical issues arising from the use of big data in Life Sciences and Healthcare. Methods: Main consensus documents, other studies, and particular cases are analyzed. Results: New concepts that emerged in five key areas for the bioethical debate on big data and health are identified—the accuracy and validity of data and algorithms, questions related to transparency and confidentiality in the use of data; aspects that raise the coding or pseudonymization and the anonymization of data, and also problems derived from the possible individual or group identification; the new ways of obtaining consent for the transfer of personal data; the relationship between big data and the responsibility of professional decision; and the commitment of the Institutions and Public Administrations. Conclusions: Good practices in the management of big data related to Life Sciences and Healthcare depend on respect for the rights of individuals, the improvement that these practices can introduce in assistance to individual patients, the promotion of society’s health in general and the advancement of scientific knowledge. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
Article
CDL4CDRP: A Collaborative Deep Learning Approach for Clinical Decision and Risk Prediction
Processes 2019, 7(5), 265; https://doi.org/10.3390/pr7050265 - 07 May 2019
Cited by 11 | Viewed by 2346
Abstract
(1) Background: Recommendation algorithms have played a vital role in the prediction of personalized recommendation for clinical decision support systems (CDSSs). Machine learning methods are powerful tools for disease diagnosis. Unfortunately, they must deal with missing data, as this will result in data [...] Read more.
(1) Background: Recommendation algorithms have played a vital role in the prediction of personalized recommendation for clinical decision support systems (CDSSs). Machine learning methods are powerful tools for disease diagnosis. Unfortunately, they must deal with missing data, as this will result in data error and limit the potential patterns and features associated with obtaining a clinical decision; (2) Methods: Recent years, collaborative filtering (CF) have proven to be a valuable means of coping with missing data prediction. In order to address the challenge of missing data prediction and latent feature extraction, neighbor-based and latent features-based CF methods are presented for clinical disease diagnosis. The novel discriminative restricted Boltzmann machine (DRBM) model is proposed to extract the latent features, where the deep learning technique is adopted to analyze the clinical data; (3) Results: Proposed methods were compared to machine learning models, using two different publicly available clinical datasets, which has various types of inputs and different quantity of missing. We also evaluated the performance of our algorithm, using clinical datasets that were missing at random (MAR), which were missing at various degrees; and (4) Conclusions: The experimental results demonstrate that DRBM can effectively capture the latent features of real clinical data and exhibits excellent performance for predicting missing values and result classification. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Article
Filter Variable Selection Algorithm Using Risk Ratios for Dimensionality Reduction of Healthcare Data for Classification
Processes 2019, 7(4), 222; https://doi.org/10.3390/pr7040222 - 18 Apr 2019
Cited by 13 | Viewed by 2677
Abstract
This research developed and tested a filter algorithm that serves to reduce the feature space in healthcare datasets. The algorithm binarizes the dataset, and then separately evaluates the risk ratio of each predictor with the response, and outputs ratios that represent the association [...] Read more.
This research developed and tested a filter algorithm that serves to reduce the feature space in healthcare datasets. The algorithm binarizes the dataset, and then separately evaluates the risk ratio of each predictor with the response, and outputs ratios that represent the association between a predictor and the class attribute. The value of the association translates to the importance rank of the corresponding predictor in determining the outcome. Using Random Forest and Logistic regression classification, the performance of the developed algorithm was compared against the regsubsets and varImp functions, which are unsupervised methods of variable selection. Equally, the proposed algorithm was compared with the supervised Fisher score and Pearson’s correlation feature selection methods. Different datasets were used for the experiment, and, in the majority of the cases, the predictors selected by the new algorithm outperformed those selected by the existing algorithms. The proposed filter algorithm is therefore a reliable alternative for variable ranking in data mining classification tasks with a dichotomous response. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Article
Employment of Emergency Advanced Nurses of Turkey: A Discrete-Event Simulation Application
Processes 2019, 7(1), 48; https://doi.org/10.3390/pr7010048 - 18 Jan 2019
Cited by 6 | Viewed by 4482
Abstract
In the present study, problems in emergency services (ESs) were dealt with by analyzing the working system of ESs in Turkey. The purpose of this study was to reduce the waiting times spent in hospitals by employing advanced nurses (ANs) to treat patients [...] Read more.
In the present study, problems in emergency services (ESs) were dealt with by analyzing the working system of ESs in Turkey. The purpose of this study was to reduce the waiting times spent in hospitals by employing advanced nurses (ANs) to treat patients who are not urgent, or who may be treated as outpatients in ESs. By applying discrete-event simulation on a 1/24 (daily) and 7/24 (weekly) basis, and by employing ANs, it was determined that the number of patients that were treated increased by 26.71% on a 1/24 basis, and by 15.13% on a 7/24 basis. The waiting time that was spent from the admission to the ES until the treatment time decreased by 38.67% on a 1/24 basis and 53.66% on a 24/7 basis. Similarly, the length of stay was reduced from 82.46 min to 53.97 min in the ES. Among the findings, it was observed that the efficiency rate of the resources was balanced by the employment of ANs, although it was not possible to obtain sufficient efficiency from the resources used in the ESs prior to the present study. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Article
Catastrophic Health Expenditures and Its Inequality in Households with Cancer Patients: A Panel Study
Processes 2019, 7(1), 39; https://doi.org/10.3390/pr7010039 - 14 Jan 2019
Cited by 16 | Viewed by 3385
Abstract
This study aims to examine the determinants of catastrophic health expenditure in households with cancer patients by conducting a panel analysis of three-year data. Data are adopted from surveys administered by Korea Health Panel for 2012–2014. We conducted correspondence and conditional transition probability [...] Read more.
This study aims to examine the determinants of catastrophic health expenditure in households with cancer patients by conducting a panel analysis of three-year data. Data are adopted from surveys administered by Korea Health Panel for 2012–2014. We conducted correspondence and conditional transition probability analyses to examine households that incurred catastrophic health expenditure, followed by a panel logit analysis. The analyses reveal three notable results. First, the occurrence of catastrophic health expenditure differs by age group, that is, the probability of incurring catastrophic health expenditure increases with age. Second, this probability is higher in households with National Health Insurance than those receiving medical care benefits. Finally, households without private health insurance report a higher occurrence rate. The findings suggest that elderly people with cancer have greater medical coverage and healthcare needs. Private health insurance contributes toward protecting households from catastrophic health expenditure. Therefore, future research is needed on catastrophic health expenditure with focus on varying age groups, healthcare coverage type, and private health insurance. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
Article
An Efficient Solitary Senior Citizens Care Algorithm and Application: Considering Emotional Care for Big Data Collection
Processes 2018, 6(12), 244; https://doi.org/10.3390/pr6120244 - 27 Nov 2018
Cited by 9 | Viewed by 3786
Abstract
The issue of solitary senior citizens dying alone has become serious in advanced countries where the average lifespan of their citizens is continuously extending due to improved health care and diet. Such unattended deaths are considered to be one of the major issues [...] Read more.
The issue of solitary senior citizens dying alone has become serious in advanced countries where the average lifespan of their citizens is continuously extending due to improved health care and diet. Such unattended deaths are considered to be one of the major issues pertaining to the ever-growing number of senior citizens so that many research studies have been conducted to find a solution to mitigate the situation. The framework proposed in this study allows monitoring of electric power consumption patterns of solitary senior citizens. At the same time, a test bed was constructed to estimate the performance of the framework. The results from the test bed experiment revealed that the framework was effective, flexible, and expandable for actual implementation. This framework is the product of these research studies describing individual designs and the method of implementing them for actual application. This research has confirmed that the framework for an extendable solitary senior citizens care system can be designed and implemented at low cost and the operations between system components worked smoothly while interacting flexibly. In particular, the rate of these old people dying alone in poor areas was above normal so that the proposed system would be quite meaningful to society as it helps in monitoring their safety by locating the whereabouts of those people with dementia or checking their daily routines, for example. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Review

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Review
Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review
Processes 2020, 8(8), 951; https://doi.org/10.3390/pr8080951 - 07 Aug 2020
Cited by 7 | Viewed by 2824
Abstract
In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in [...] Read more.
In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Review
Reckoning the Dearth of Bioinformatics in the Arena of Diabetic Nephropathy (DN)—Need to Improvise
Processes 2020, 8(7), 808; https://doi.org/10.3390/pr8070808 - 09 Jul 2020
Cited by 1 | Viewed by 4366
Abstract
Diabetic nephropathy (DN) is a recent rising concern amongst diabetics and diabetologist. Characterized by abnormal renal function and ending in total loss of kidney function, this is becoming a lurking danger for the ever increasing population of diabetics. This review touches upon the [...] Read more.
Diabetic nephropathy (DN) is a recent rising concern amongst diabetics and diabetologist. Characterized by abnormal renal function and ending in total loss of kidney function, this is becoming a lurking danger for the ever increasing population of diabetics. This review touches upon the intensity of this complication and briefly reviews the role of bioinformatics in the area of diabetes. The advances made in the area of DN using proteomic approaches are presented. Compared to the enumerable inputs observed through the use of bioinformatics resources in the area of proteomics and even diabetes, the existing scenario of skeletal application of bioinformatics advances to DN is highlighted and the reasons behind this discussed. As this review highlights, almost none of the well-established tools that have brought breakthroughs in proteomic research have been applied into DN. Laborious, voluminous, cost expensive and time-consuming methodologies and advances in diagnostics and biomarker discovery promised through beckoning bioinformatics mechanistic approaches to improvise DN research and achieve breakthroughs. This review is expected to sensitize the researchers to fill in this gap, exploiting the available inputs from bioinformatics resources. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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Review
In Silico Tools and Phosphoproteomic Software Exclusives
Processes 2019, 7(12), 869; https://doi.org/10.3390/pr7120869 - 21 Nov 2019
Cited by 2 | Viewed by 3205
Abstract
Proteomics and phosphoproteomics have been emerging as new dimensions of omics. Phosphorylation has a profound impact on the biological functions and applications of proteins. It influences everything from intrinsic activity and extrinsic executions to cellular localization. This post-translational modification has been subjected to [...] Read more.
Proteomics and phosphoproteomics have been emerging as new dimensions of omics. Phosphorylation has a profound impact on the biological functions and applications of proteins. It influences everything from intrinsic activity and extrinsic executions to cellular localization. This post-translational modification has been subjected to detailed study and has been an object of analytical curiosity with the advent of faster instrumentation. The major strength of phosphoproteomic research lies in the fact that it gives an overall picture of the workforce of the cell. Phosphoproteomics gives deeper insights into understanding the mechanism behind development and progression of a disease. This review for the first time consolidates the list of existing bioinformatics tools developed for phosphoproteomics. The gap between development of bioinformatics tools and their implementation in clinical research is highlighted. The challenge facing progress is ideally believed to be the interdisciplinary arena this field of research is associated with. For meaningful solutions and deliverables, these tools need to be implemented in clinical studies for obtaining answers to pharmacodynamic questions, saving time, costs and energy. This review hopes to invoke some thought in this direction. Full article
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
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