Deep Learning in Biomedical Data Science

2018 ◽  
Vol 1 (1) ◽  
pp. 181-205 ◽  
Author(s):  
Pierre Baldi
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Spatial Scale ◽  
Biomedical Imaging ◽  
Data Science ◽  
Black Box ◽  
Biomedical Data ◽  
Learning Methods

Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

The Black Box Myth

2019 ◽  
Vol 1 (1) ◽  
pp. 1-3
Author(s):  
Abraham Rudnick
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Deep Learning ◽  
Black Box ◽  
Dynamic Decision Making ◽  
Empirical Knowledge ◽  
Negative Consequences ◽  
Extreme Caution ◽  
Scientific Facts

Artificial intelligence (AI) and its correlates, such as machine and deep learning, are changing health care, where complex matters such as comoribidity call for dynamic decision-making. Yet, some people argue for extreme caution, referring to AI and its correlates as a black box. This brief article uses philosophy and science to address the black box argument about knowledge as a myth, concluding that this argument is misleading as it ignores a fundamental tenet of science, i.e., that no empirical knowledge is certain, and that scientific facts – as well as methods – often change. Instead, control of the technology of AI and its correlates has to be addressed to mitigate such unexpected negative consequences.

scholarly journals Recent advances in the application of data science and machine/deep learning methods to research in chemical and biological sciences : a review

2020 ◽  
Author(s):  
Ben Geoffrey
Keyword(s):  
Biological Sciences ◽  
Deep Learning ◽  
Computational Chemistry ◽  
Data Science ◽  
De Novo ◽  
Computational Cost ◽  
Data Driven ◽  
Learning Approaches ◽  
Learning Methods ◽  
Space Data

The rise in application of methods of data science and machine/deep learning in chemical and biological sciences must be discussed in the light of the fore-running disciplines of bio/chem-informatics and computational chemistry and biology which helped in the accumulation ofenormous research data because of which successful application of data-driven approaches have been made possible now. Many of the tasks and goals of Ab initio methods in computational chemistry such as determination of optimized structure and other molecular properties of atoms, molecules, and compounds are being carried out with much lesser computational cost with data-driven machine/deep learning-based predictions. One observes a similar trend in computational biology, wherein, data-driven machine/deep learning methods are being proposed to predict the structure and dynamical of interactions of biological macromolecules such as proteins and DNA over computational expensive molecular dynamics based methods. In the cheminformatics space,one sees the rise of deep neural network-based methods that have scaled traditional structure-property/structure-activity to handle big data to design new materials with desired property and drugs with required activity in deep learning-based de novo molecular design methods. In thebioinformatics space, data-driven machine/deep learning approaches to genomic and proteomic data have led to interesting applications in fields such as precision medicine, prognosis prediction, and more. Thus the success story of the application of data science, machine/deep learning, andartificial intelligence to the disciple of chem/bio-informatics, and computational chemistry and biology has been told in light of how these fore-running disciplines had created huge repositories of data for data-driven approaches to be successful in these disciplines.

scholarly journals Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models

2020 ◽  
Author(s):  
Evren Dağlarli
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Black Box ◽  
Learning Models ◽  
Learning Methods ◽  
Data Set ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Artificial Neural ◽  
Black Box Models

The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.

Network embedding in biomedical data science

2018 ◽  
Vol 21 (1) ◽  
pp. 182-197 ◽  
Author(s):  
Chang Su ◽  
Jie Tong ◽  
Yongjun Zhu ◽  
Peng Cui ◽  
Fei Wang
Keyword(s):  
Data Science ◽  
Dimensional Space ◽  
Rapid Development ◽  
Deep Understanding ◽  
Biomedical Science ◽  
Biomedical Data ◽  
Network Embedding ◽  
Learning Methods ◽  
Low Dimensional ◽  
Human Healthcare

AbstractOwning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. There remain challenges on handling high dimensionality and sparsity of the biomedical networks. The latest advances in network embedding technologies provide new effective paradigms to solve the network analysis problem. It converts network into a low-dimensional space while maximally preserves structural properties. In this way, downstream tasks such as link prediction and node classification can be done by traditional machine learning methods. In this survey, we conduct a comprehensive review of the literature on applying network embedding to advance the biomedical domain. We first briefly introduce the widely used network embedding models. After that, we carefully discuss how the network embedding approaches were performed on biomedical networks as well as how they accelerated the downstream tasks in biomedical science. Finally, we discuss challenges the existing network embedding applications in biomedical domains are faced with and suggest several promising future directions for a better improvement in human healthcare.

scholarly journals The Curious Case of Connectionism

2019 ◽  
Vol 2 (1) ◽  
pp. 190-205
Author(s):  
Istvan S. N. Berkeley
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Cognitive Functioning ◽  
Black Box ◽  
Second Phase ◽  
Learning Methods ◽  
Artificial Intelligence Research ◽  
Media Attention ◽  
Third Phase

AbstractConnectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it offered a way of counteracting the conceptual ties to the philosophical traditions of atomism, rationalism, logic, nativism, rule realism and a concern with the role symbols play in human cognitive functioning, which was prevalent as a consequence of artificial intelligence research. The surge in philosophical interest waned, possibly in part due to the efforts of some traditionalists and the so-called black box problem. Most recently, what may be thought of as a third phase of connectionist research, based on so-called deep learning methods, is beginning to show some signs of again exciting philosophical interest.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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