Global Markets for Machine Learning in the Life Sciences



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) — announces the release of the report, Global Markets for Machine Learning in the Life Sciences –
The report also covers market forecasts from 2022 to 2027 and introduces the major market players.

The analyst analyzes each technology in detail, determines the key players and current market status, and presents growth forecasts for the next five years. Scientific challenges and advances, including the latest trends, are highlighted.

Government regulations, key collaborations, current patents and factors affecting the industry from a global perspective are examined.

Key technologies and products of Machine Learning in Life Sciences are analyzed to determine the present and future market status and the growth is forecast from 2022 to 2027. An in-depth discussion of strategic alliances, industry structures, competitive dynamics, patents and market drivers is also provided.

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Report contains:
– 32 data tables and 28 additional tables
– A comprehensive overview and up-to-date analysis of the global markets for machine learning in the life sciences industry
– Analyzes of the global market trends with historical market sales data for 2020 and 2021, estimates for 2022 and projections of compound annual growth rates (CAGRs) up to 2027
– Highlights of the current and future market potential for ML in Life Sciences applications and focus areas to forecast this market into various segments and sub-segments
– Estimation of the actual Machine Learning in Life Sciences market size in millions of US dollars and corresponding market share analysis based on solution offering, deployment type, application and geographic region
– Updated information on key market drivers and opportunities, industry changes and regulations along with other demographics that will impact this market demand in the coming years (2022-2027).
– Discussion of viable technology drivers through a holistic review of different platform technologies for new and existing machine learning applications in life sciences
– Identification of key stakeholders and analysis of the competitive landscape based on recent developments and segment sales
– Emphasizing key growth strategies employed by leading players of the global Life Sciences Machine Learning Market, their new product launches, key acquisitions, and competitive benchmarks
– 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

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Artificial intelligence (AI) is a term used to denote a scientific field that includes the creation of machines (e.g. robots) and computer hardware and software that aim to completely or partially reproduce. AI is considered a branch of cognitive computing, a term that refers to systems that can 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 as sifting through data, extracting information relevant to the scope of the task, discovering rules that govern the data, making decisions and predictions, and executing specific instructions . For example, ML is used in image recognition to identify the content of an image after instructing the machine to learn the differences between many different image categories.

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There are different types of ML algorithms, the most common of which are Nearest Neighbor, Naive Bayes, Decision Trees, A Priori Algorithms, Linear Regression, Case-Based Reasoning, Hidden Markov Models, Support Vector Machines (SVMs), Clustering, and Artificial Algorithms are neural networks. Artificial neural networks (ANN) have gained great popularity for high-level computing in recent years.

They are modeled to act similar to the human brain. The most basic type of ANN is the feedforward network, which consists of 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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