Conditional Generative Adversarial Networks to Model iPSC-Derived Cancer Stem Cells

The realization of effective and low-cost drug discovery is imperative to enable people to easily purchase and use medicines when necessary. This paper reports a smart system for detecting iPSC-derived cancer stem cells by using conditional generative adversarial networks. This system with artificial intelligence (AI) accepts a normal image from a microscope and transforms it into a corresponding fluorescent-marked fake image. The AI system learns 10,221 sets of paired pictures as input. Consequently, the system’s performance shows that the correlation between true fluorescent-marked images and fake fluorescent-marked images is at most 0.80. This suggests the fundamental validity and feasibility of our proposed system. Moreover, this research opens a new way for AI-based drug discovery in the process of iPSC-derived cancer stem cell detection.

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The Frontiers of Deep Learning for Earth System Modelling

10.5194/egusphere-egu21-12541 ◽

2021 ◽

Author(s):

David Hall

Keyword(s):

Artificial Intelligence ◽

Bias Correction ◽

Neural Nets ◽

Generative Adversarial Networks ◽

System Modelling ◽

Earth System ◽

Adversarial Networks ◽

The Earth ◽

Medium Range ◽

Earth System Modelling

<p>This talk gives an overview of cutting-edge artificial intelligence applications and techniques for the earth-system sciences. We survey the most important recent contributions in areas including extreme weather, physics emulation, nowcasting, medium-range forecasting, uncertainty quantification, bias-correction, generative adversarial networks, data in-painting, network-HPC coupling, physics-informed neural nets, and geoengineering, amongst others. Then, we describe recent AI breakthroughs that have the potential to be of greatest benefit to the geosciences. We also discuss major open challenges in AI for science and their potential solutions. This talk is a living document, in that it is updated frequently, in order to accurately relect this rapidly changing field.</p>

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Erratum: Drug Discovery Approaches to Target Wnt Signaling in Cancer Stem Cells

Oncotarget ◽

10.18632/oncotarget.26220 ◽

2018 ◽

Vol 9 (78) ◽

pp. 34856-34856 ◽

Cited By ~ 1

Author(s):

Joshua C. Curtin ◽

Matthew V. Lorenzi

Keyword(s):

Stem Cells ◽

Drug Discovery ◽

Cancer Stem Cells ◽

Wnt Signaling

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Novel drug discovery system for cancer stem cells in human squamous cell carcinoma of the esophagus

Oncology Reports ◽

10.3892/or.2013.2952 ◽

2013 ◽

Vol 31 (3) ◽

pp. 1133-1138 ◽

Cited By ~ 7

Author(s):

YOSHIHIRO KANO ◽

MASAMITSU KONNO ◽

KOICHI KAWAMOTO ◽

KEISUKE TAMARI ◽

KAZUHIKO HAYASHI ◽

...

Keyword(s):

Stem Cells ◽

Squamous Cell Carcinoma ◽

Drug Discovery ◽

Cancer Stem Cells ◽

Cell Carcinoma ◽

Squamous Cell ◽

Carcinoma Of The Esophagus ◽

Human Squamous Cell Carcinoma ◽

Discovery System ◽

Novel Drug

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Serlio and Artificial Intelligence: Problematizing the Image-to-Object Workflow

10.1007/978-981-16-5983-6_1 ◽

2021 ◽

pp. 3-12

Author(s):

Jean Jaminet ◽

Gabriel Esquivel ◽

Shane Bugni

Keyword(s):

Artificial Intelligence ◽

Data Storage ◽

Intelligent Systems ◽

Communication Theory ◽

Human Perception ◽

Generative Adversarial Networks ◽

Virtual Design ◽

Storage And Retrieval ◽

Adversarial Networks ◽

Design Production

AbstractVirtual design production demands that information be increasingly encoded and decoded with image compression technologies. Since the Renaissance, the discourses of language and drawing and their actuation by the classical disciplinary treatise have been fundamental to the production of knowledge within the building arts. These early forms of data compression provoke reflection on theory and technology as critical counterparts to perception and imagination unique to the discipline of architecture. This research examines the illustrated expositions of Sebastiano Serlio through the lens of artificial intelligence (AI). The mimetic powers of technological data storage and retrieval and Serlio’s coded operations of orthographic projection drawing disclose other aesthetic and formal logics for architecture and its image that exist outside human perception. Examination of aesthetic communication theory provides a conceptual dimension of how architecture and artificial intelligent systems integrate both analog and digital modes of information processing. Tools and methods are reconsidered to propose alternative AI workflows that complicate normative and predictable linear design processes. The operative model presented demonstrates how augmenting and interpreting layered generative adversarial networks drive an integrated parametric process of three-dimensionalization. Concluding remarks contemplate the role of human design agency within these emerging modes of creative digital production.

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Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

Electronics ◽

10.3390/electronics11010118 ◽

2021 ◽

Vol 11 (1) ◽

pp. 118

Author(s):

Holly Burrows ◽

Javad Zarrin ◽

Lakshmi Babu-Saheer ◽

Mahdi Maktab-Dar-Oghaz

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

User Interface ◽

Convolutional Neural Networks ◽

Mental Health Problems ◽

Well Being ◽

Working Environment ◽

Generative Adversarial Networks ◽

Deep Convolutional Neural Networks ◽

Adversarial Networks

It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.

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Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques

10.20944/preprints202005.0408.v1 ◽

2020 ◽

Author(s):

Bryan Jordan

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Chemical Space ◽

Machine Learning Techniques ◽

Training Dataset ◽

Generative Adversarial Networks ◽

Chemical Safety ◽

Adversarial Networks ◽

Wide Range ◽

Traditional Drug

The vastness of chemical-space constrains traditional drug-discovery methods to the organic laws that are guiding the chemistry involved in filtering through candidates. Leveraging computing with machine-learning to intelligently generate compounds that meet a wide range of objectives can bring significant gains in time and effort needed to filter through a broad range of candidates. This paper details how the use of Generative-Adversarial-Networks, novel machine learning techniques to format the training dataset and the use of quantum computing offer new ways to expedite drug-discovery.

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Cockle shell-derived aragonite calcium carbonate nanoparticle for targeting cancer and breast cancer stem cells

Cancer Nanotechnology ◽

10.1186/s12645-020-00067-3 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Kehinde Muibat Ibiyeye ◽

Sherifat Banke Idris ◽

Abu Bakar Zakaria Zuki

Keyword(s):

Stem Cells ◽

Calcium Carbonate ◽

Cancer Stem Cells ◽

Cancer Cells ◽

Low Cost ◽

Drug Carrier ◽

Drug Carriers ◽

Current State ◽

Highly Porous ◽

Cockle Shell

Abstract Cockle shell-derived aragonite calcium carbonate nanoparticles (CACNP) have demonstrated prospect as nano-sized drug carriers for targeting cancer cells. CACNP is biocompatible, biodegradable and its biomaterial is readily available and is of low cost. In addition, CACNP is highly porous, has a large surface area which confer a high loading capacity. The pH-dependent release properties as well as its potential for surface functionalization with targeting agents make CACNP useful in passive and active targeting of cancer cells and cancer stem cells. In this article, we reviewed the current state of CACNP as nano-sized drug carrier for targeting cancer cells, cancer stem cells and its biocompatibility.

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