Machine Learning in Life Sciences: AI assists boosts Bioinformatics

Posted by Deeksha on September 30th, 2019

Machine Learning is applied in areas of life sciences that include prognosis & therapeutic decision making and diagnosis & prevention. Machine learning algorithms aid in discovering new diseases and analyzing relationship and structure from the clinical data. It contributes in enhancing management & information retrieval processes and modification of medical procedures in the quest to provide efficient and cost-efficient solution to patient.

Machine learning serves life sciences like biology and medicine by intelligently organizing and analyzing the available heterogeneous high quality data that includes logistics data, clinical data, molecular data and bibliographical data. Machine Learning has significance in Bioinformatics as it helps in developing methods to cope up with complexity of available data such as information of nucleotides, amino acids, RNA, DNA, and proteins.

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Artificial intelligence paramount importance

Machine learning has boosted Artificial intelligence, placing it at limelight as one of the major technological innovations replacing lots of complex and repetitive tasks in the field of life sciences. AI has prominent contributions in automated analysis of images in digital pathology domain. Deep learning, subset of machine learning is provided data by software CellProfiler to measure different features including organelles texture and analysis of empty spaces in a cell.

Pattern recognition and AI have magnified pathologists’ ability to oversee analysis in Histopathology image analysis. AI has significantly reduced product development timelines by reducing the time invested in developing and manufacturing new therapies. Integration of research data, clinical data and new information of digital age that includes social media and wearable, help scientists to draw a holistic picture of drug development. AI and machine learning accelerate the entire process of product development and minimize the labor involved.

Pharmaceutical labels such as patient information leaflet and package insert are important safety information that require updates and submissions to corresponding health authority. Feeding appropriate and enough data points to AI can fasten up the updating process and ensure timely submission.

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Machine learning in life sciences: Report Synopsis

Quantitative and qualitative assessment exhort intelligence and overarching report on the Machine learning in life sciences. Further, the report thoroughly delineates various aspects of the market that will potentially have considerable influence on the development of the Machine learning in life sciences. As such, those aspects incorporate drivers, trends, restraints, and opportunities. Furthermore, the report elucidates segregation of the market that provides an exhaustive analysis on machine learning in life sciences.

An insightful and deep-dive assessment of the competitive assessment of the Machine learning in life sciences pins hope on Porters’ Five Force Analysis. Accordingly, the Porters’ Five Force Analysis offers a pressing analysis on the potential strategies of the preeminent players in the Machine learning in life sciences. In addition, the business strategies counts on company overview, product portfolio, SWOT analysis, key differentiation and recent development.

Machine learning in life sciences: Research Methodology

Primary sources and secondary sources propel intelligence report on Machine learning in life sciences that provide deep dive analysis on the market. As such, the report provides reliable and unbiased projections, and assessments which have palpable impact on the market size and historical data. Besides, the report also counts on primary sources which hinges upon in-depth and intelligent analysis from well-grounded and reliable experts, telephonic interview, and a thorough assessment from surveys and seasoned analyst.

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