Report Scope: This report highlights the current and future market potential for machine learning in life sciences and provides a detailed analysis of the competitive environment, regulatory scenario, drivers, restraints, opportunities and trends in the market.
New York, Sept. 20, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Markets for Machine Learning in the Life Sciences" - https://www.reportlinker.com/p06320049/?utm_source=GNW
The report also covers market projections from 2022 through 2027 and profiles key market players.
The analyst analyzes each technology in detail, determines major players and current market status, and presents forecasts of growth over the next five years.Scientific challenges and advances, including the latest trends, are highlighted.
Government regulations, major collaborations, recent patents and factors affecting the industry from a global perspective are examined.
Key machine learning in life sciences technologies and products are analyzed to determine present and future market status, and growth is forecast from 2022 to 2027. An in-depth discussion of strategic alliances, industry structures, competitive dynamics, patents and market driving forces is also provided.
- 32 data tables and 28 additional tables
- A comprehensive overview and up-to-date analysis of the global markets for machine learning in life sciences industry
- Analyses of the global market trends, with historic market revenue data for 2020 and 2021, estimates for 2022, and projections of compound annual growth rates (CAGRs) through 2027
- Highlights of the current and future market potential for ML in life sciences application, and areas of focus to forecast this market into various segments and sub-segments
- Estimation of the actual market size for machine learning in life sciences in USD million values, and corresponding market share analysis based on solutions offering, mode of deployment, application, and geographic region
- Updated information on key market drivers and opportunities, industry shifts and regulations, and other demographic factors that will influence this market demand in the coming years (2022-2027)
- Discussion of the viable technology drivers through a holistic review of various platform technologies for new and existing applications of machine learning in the life sciences areas
- Identification of the major stakeholders and analysis of the competitive landscape based on recent developments and segmental revenues
- Emphasis on the major growth strategies adopted by leading players of the global machine learning in life sciences market, their product launches, key acquisitions, and competitive benchmarking
- Profile descriptions of the leading market players, including Alteryx Inc., Canon Medical Systems Corp., Hewlett Packard Enterprise (HPE), KNIME AG, Microsoft Corp., and Phillips Healthcare
Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines (e.g., robots) as well as computer hardware and software aimed at reproducing wholly or in part the intelligent behavior of human beings. AI is considered a branch of cognitive computing, a term that refers to systems able to learn, reason and interact with humans. Cognitive computing is a combination of computer science and cognitive science.
ML algorithms are designed to perform tasks such data browsing, extracting information that is relevant to the scope of the task, discovering rules that govern the data, making decisions and predictions, and accomplishing specific instructions. As an example, ML is used in image recognition to identify the content of an image after the machine has been instructed to learn the differences among many different categories of images.
There are several types of ML algorithms, the most common of which are nearest neighbor, naïve Bayes, decision trees, a priori algorithms, linear regression, case-based reasoning, hidden Markov models, support vector machines (SVMs), clustering, and artificial neural networks.Artificial neural networks (ANN) have achieved great popularity in recent years for high-level computing.
They are modeled to act similarly to the human brain. The most basic type of ANN is the feedforward network, which is formed by an input layer, a hidden layer and an output layer, with data moving in one direction from the input layer to the output layer, while being transformed in the hidden layer.
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