scholarly journals Machine intelligence for precision oncology

2021 ◽  
Vol 9 (1) ◽  
pp. 1-10
Author(s):  
Nelson S Yee
Keyword(s):  
Machine Intelligence ◽  
Precision Oncology

Research round-up: Precision oncology

Nature ◽  
2020 ◽  
Vol 585 (7826) ◽  
pp. S2-S3
Author(s):  
Laura Vargas-Parada
Keyword(s):  
Precision Oncology

Avenues to Precision Oncology

2020 ◽  
pp. 36-45
Author(s):  
Alexander Meisel
Keyword(s):  
Precision Medicine ◽  
Trial Design ◽  
Ad Hoc ◽  
Molecular Targets ◽  
Clinical Trial Design ◽  
Predictive Biomarkers ◽  
Precision Oncology ◽  
Companion Diagnostics ◽  
Bona Fide ◽  
The One

Until recently, the clinical management of cancer heavily relied on anatomical and histopathological criteria, with ad hoc guidelines directing the therapeutic choices in specific indications. In the last years, the development and therapeutic implementation of novel anticancer therapies significantly improved the clinical outcome of cancer patients. Nonetheless, such cutting-edge approaches revealed the limitation of the one-size-fits-all paradigm. The newly discovered molecular targets can be exploited either as bona fide targets for subsequent drug development, or as tools to precision medicine, in the form of prognostic and/or predictive biomarkers. This article provides an overview of some of the most recent advances in precision medicine in oncology, with a focus on novel tissue-agnostic anticancer therapies. The definition and implementation of biomarkers and companion diagnostics in clinical trials and clinical practice are also discussed, as well as the changing landscape in clinical trial design.

Machine Intelligence. Part 2. Biological Foundations

1990 ◽  
Author(s):  
Raymond Scanlon ◽  
Mark Johnson
Keyword(s):  
Machine Intelligence

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

The Future of Human Workers

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Mental Capacity ◽  
Machine Intelligence ◽  
The Internet ◽  
Traditional Model ◽  
Global Workforce ◽  
Repetitive Tasks ◽  
The Internet Of Things

New technologies like artificial intelligence, robotics, machine intelligence, and the Internet of Things are seeing repetitive tasks move away from humans to machines. Humans cannot become machines, but machines can become more human-like. The traditional model of educating workers for the workforce is fast becoming irrelevant. There is a massive need for the retooling of human workers. Humans need to be trained to remain focused in a society which is constantly getting bombarded with information. The two basic elements of physical and mental capacity are slowly being taken over by machines and artificial intelligence. This changes the fundamental role of the global workforce.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 226-226
Keyword(s):  
Prostate Cancer ◽  
Knowledge Base ◽  
Prediction Models ◽  
Precision Oncology ◽  
Castration Resistant Prostate Cancer ◽  
Short Term ◽  
Castration Resistant

Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, Rudolph JE, Yaeger R, Soumerai T, Nissan MH, Chang MT, Chandarlapaty S, Traina TA, Paik PK, Ho AL, Hantash FM, Grupe A, Baxi SS, Callahan MK, Snyder A, Chi P, Danila D, Gounder M, Harding JJ, Hellmann MD, Iyer G, Janjigian Y, Kaley T, Levine DA, Lowery M, Omuro A, Postow MA, Rathkopf D, Shoushtari AN, Shukla N, Voss M, Paraiso E, Zehir A, Berger MF, Taylor BS, Saltz LB, Riely GJ, Ladanyi M, Hyman DM, Baselga J, Sabbatini P, Solit DB, Schultz N. OncoKB: a precision oncology knowledge base. JCO Precis Oncol 2017 Jul;2017 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28890946/ Newton Y, Novak AM, Swatloski T, McColl DC, Chopra S, Graim K, Weinstein AS, Baertsch R, Salama SR, Ellrott K, Chopra M, Goldstein TC, Haussler D, Morozova O, Stuart JM. TumorMap: exploring the molecular similarities of cancer samples in an interactive portal. Cancer Res 2017 Nov 1;77(21):e111-e114 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29092953/ Seyednasrollah F, Koestler DC, Wang T, Piccolo SR, Vega R, Greiner R, Fuchs C, Gofer E, Kumar L, Wolfinger RD, Winner KK, Bare C, Neto EC, Yu T, Shen L, Abdallah K, Norman T, Stolovitzky G, Soule HR, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Elo LL, Zhou FL, Guinney J, Costello JC, and Prostate Cancer DREAM Challenge Community. A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer. JCO Clin Cancer Inform 2017 Aug 4;(1):1-15 http://ascopubs.org/doi/abs/10.1200/CCI.17.00018

Sign in / Sign up

Export Citation Format

Share Document