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Today, most drug discovery programmes begin with the identification and validation of disease modifying biological targets. The primary way to uncover these targets is through searching and reviewing published scientific literature. Eric Gilbert explores how data science techniques like text mining are speeding up research into areas such as pancreatic cancer.

The increasing rate of scientific publishing only made more apparent in 2020 with the flood of important and necessary COVID-19 research has made the task of sifting through literature extremely difficult and time consuming.

Between just February and May 2020, submissions to Elseviers journals alone went up by 58%compared to the same period in 2019. As a result, many researchers and organisations are relying more on digital technologies and data science techniques to help break down and digest the information available. Within this area, text mining is of particular interest because it enables researchers to retrieve highly specified information from unstructured content which means they can more quickly find meaningful answers to complex research questions.

Text mining can aid the search for new and innovative therapies for many unmet needs where there are large volumes of published literature for example, oncology. Pancreatic cancer is the third leading cause of cancer death in the United States and the five-year survival rate differs dramatically depending on the stage of the cancer at diagnosis. As such, it is a disease where we urgently need new and innovative therapies to not only treat the cancer but to improve early diagnosis rates.

Text mining was also able to highlight new insights into the mechanisms of pancreatic cancers immune evasion, which refers to cancer cells ability to evade an immune response

To explore how data science techniques like text mining can accelerate research into an area like pancreatic cancer, Elsevier developed a new research report using text mining to identify the emerging trends in pancreatic cancer literature over the last two years. The analysis has uncovered a number of research trends and points towards potential new research areas for the development of therapeutics.

Understanding more about the disease and what determines poor patient outcomes

The results of the text mining report bring light to recently trending protein and genomic terms with a semantic relationship to pancreatic cancer, showing the emergence in research in both X-inactive specific transcript (XIST) and lincoo511 (fig.1.). These are both long non-coding RNAs, which have attracted attention in the past two years due to their involvement in several cancers including pancreatic. They have the potential to be not only diagnostic/prognostic biomarkers but also targets of pharmaceutical intervention.

Figure 1: Trending Protein and Genomic terms with a semantic relationship to pancreatic cancer

Text mining was also able to highlight new insights into the mechanisms of pancreatic cancers immune evasion, which refers to cancer cells ability to evade an immune response. Currently, while progress has been made with the discovery of checkpoint inhibitors, clinical results for immune monotherapies have been disappointing for pancreatic cancer. This is in part due to the immunosuppressive nature of the tumours, however, recent revelations in the mechanism of immune evasion could provide disease-modifying therapeutic targets.

Potential areas for further research

Within the analysis of trending terms, ferroptosis was also highlighted in relation to biological functions. The induction of ferroptosis, a type of regulated cell death, is being looked at as a new strategy for pancreatic cancer treatment. This was only discovered in 2012 and is a currently active area of research within pancreatic cancer.

Finally, there is increasing evidence that propofol has biological effects that may alter the progression of pancreatic cancer. Propofol has been used for more than 30 years as an anaesthetic during medical procedures; more recently it has been found to interact with non-coding RNAs and to modulate immune function and a number of signaling pathways. Furthermore, propofol has been shown to suppress autophagy and enhance the activation of T helper cells. While propofol is unlikely to be used therapeutically, it is helping our understanding of pancreatic cancer and cancer in general.

How data science and analysis of research can progress future innovations

We have seen data science techniques applied to many other industries to accelerate innovation and find faster answers. For example, in financial services to help sift through regulatory reports and company filings; in retail to identify trends and analyse consumer decision making; and in entertainment to analyse customer preferences and to make recommendations. Life sciences is considerably more complex, but it can follow suit to help researchers stay current with the ever-expanding scientific literature. It is essential to help equip the R&D sector with a greater depth of information to help battle diseases and tackle unmet needs.

With COVID-19 still impacting the capacity of laboratories, being able to maximise the knowledge in already existing research is more important than ever. It also prevents work being duplicated and empowers future R&D decisions. In terms of pancreatic cancer, current research trends show hope for the development of new treatments and diagnostics. Ferroptosis and immune evasion are highly relevant to pancreatic cancer and remain areas where further research is required. Data science techniques, including text mining, will have an important role to play in further breakthroughs.

About the author

Eric Gilbert is a life sciences consultant at Elsevier. He is an accomplished medicinal chemistry research scientist with over 15 years of experience in drug discovery at Pfizer, Schering-Plough, and Merck. He is author or coauthor of 16 publications and an inventor for 22 issued US patents. Eric possesses a unique combination of synthesis and drug discovery experience along with an extensive data science skill set.

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The role of data science and text mining in the search for new therapies - - pharmaphorum

A data scientist collects and cleans large amounts of data, maintains dashboards, interprets data to solve problems, run experiments, build algorithms and presents visualized data to stakeholders. If all that interests you, heres some news for you, you can become a data scientist without experience.

Although most of the job postings that you will come across will mention a masters degree or a Ph.D. in engineering, computer science, mathematics, or statistics, its possible to land a job without any of that. There are plenty of online courses and certification programs that can give you the knowledge.

If you have a quantitative background, the switch from your old job to data science should be easy. But before jumping on to high-tech tools, getting the basics right, like plotting data points on graphs and finding correlations, is important. As a checklist, these are the things you should build a solid base on:

To be a data scientist, its important to know and master the necessary skills rather than getting a shiny degree from a university. The interview process is skill-based and these are the languages you need to master:

Companies look for people with practical experience. Once you have the basic knowledge puting that to work in real-life and dealing with work problems will make your case stronger and impress recruiters with real-time skills. These internships are easy to find as the criteria for internships start with no-basic experience.

Firstly, a data scientist and a data analyst are two different professions. Data analysts manage data collection and identify data trends, while data scientists also interpret data along with using coding and mathematical modeling. Hence, a data analyst role is the best way to launch yourself in the field.

Data science is a booming field and many might be having the idea to switch due to lucrative job roles. However, you need to be able to explain your career transition. Mention your past roles in such a way that you highlight the common aspects of the field. If you are a pro at using Microsoft Excel or developed business, communication, and collaborative skills, mention those skills and explain how you have improved on them to apply in this job.

With all these in mind, you can become a data scientist without experience. Another important thing to keep in mind is to network with people who can influence your position in this field. The more you network, the more opportunities will knock your door.

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This Is the Only Way to Become A Data Scientist Without Any Experience - Analytics Insight

There has to be something that makes data science the most sought-after career opportunity all across the globe. Data scientists are entrusted with one of the most crucial roles to play within the organization. A data scientist is hired to aid in the decision-making process that further helps the business in reaching new horizons. The demand for data scientists all over the world is high and it is thus critical to stand out from the rest. How do you do that? You need to work a little harder to gain better knowledge about this field. When you take up initiatives that improve your business knowledge, you are not only in a better position when compared to others but you are also capable enough of putting the knowledge gained to the best use possible and see your organization grow.

Here are a few ways in which you can build your business knowledge to transform yourself into an excellent data scientist

You being a fresher need to have something to impress the recruiter. The best way to do that is to go for an internship. For a fresher, there cannot be a better way to get practical knowledge. Now, lets be honest. Getting hired as an intern isnt that easy as well. And when the subject of interest isdata science, the process gets all the more complicated and tough. However, theres always a way out. Probably the best way to get hired as an intern is to bring your connections into force. Do not rely on just your family and friends to get into the corporate world. Social media is one of the best platforms available. Make full use of this and try to establish as many connections as you can. Yet another aspect that works in your favor is that you can convert your internship offer into the final offer. Just put in all your efforts and seal the opportunity. All that you need to keep in mind is that getting selected as an intern is no less than a golden opportunity to carve a niche for yourself in the field of data science.

The aim of a data scientist is to solve business problems. On that note, if you work on projects that help in solving business problems, you already are a step ahead when compared to others. Yes, technical skills are important for data science but that is not all. Start by researching about the company, its strengths and weakness, challenges faced, articles and research papers published, etc. A deep study of all this makes it easier for you to identify the business problem followed by your approach to dealing with it. This will surely create an impression thatll go in your favor.

This option is for those who are studying, working full-time, or are not having time to take up a full-time opportunity but are willing to do something in the field of data science. Getting a freelancing opportunity is not that easy. As said, connections play a pivotal role. Also, there are a lot of companies that might be in search of freelancers with some relevant skills in place. If you have some technical skills that could help you secure a job as a data scientist then do not hesitate in choosing this option.

No matter which option you choose, ultimately what matters is how much effort you put in to become a data scientist. You should be able to convince the recruiter that there is no one better than you whod be the best fit for the role.

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What to do to Excel in Data Science as a Beginner - Analytics Insight

Even a glance at the corporate world makes it evident that Data Science & Analytics have become critical cornerstones of business success. In recent years, the unprecedented adoption of Data solutions has relied heavily on accurate Data analysis and insights by businesses worldwide. The global Data Science & Analytics industry is expected to reach a high-watermark valuation of approximately USD 141 Billion by 2024, growing at a remarkable Compound Annual Growth Rate (CAGR) of roughly 30%.

The primary factors propelling this rapid growth are the increasing cross-industry focus on using customer and operational data to boost business and maintain a competitive edge and the pressing need to extract actionable business insights from massive data sets to enhance efficiency brand value.

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Over the years, Imarticus has forged reliable partnerships with global leaders in Big Data Analytics and premium Data Science education providers, such as KPMG In India and UCLA Extension, to develop industry-approved learning material, deliver world-class experiential training, and offer internationally recognised industry-accredited professional certifications.

As a result of their overall impact and industry influence, Imarticus has received numerous awards and accolades for its unique, state-of-the-art tech-based training methods. The institute believes in experiential learning and goes to great lengths to ensure that our learners comprehensively master the skills employers are looking for in Data Science & Analytics professionals.

Their state-of-the-art Post-Graduate Program in Analytics & Artificial Intelligence (which includes Data Science Fundamentals delivered by UCLA Extension) and Post Graduate Program in Data Analytics not only offer profound industry-endorsed professional education, these programs also come with a job placement guarantee. Successfully complete either program and their in-house Placement Team will help you get an assured job placement in the Data Science & Analytics industry.

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Beyond formal education, this is where the billion-dollar Data Science & Analytics industry picks its tal - Times of India

The U.S. Department of Transportation is appointing a Chief Science Officer for the entire Department for the first time in over four decades and has taken several additional steps to act on the Biden-Harris Administrations commitment to address the climate emergency.

The Department also announced that it has begun work to reestablish its Climate Change Center and has moved to restore public access to climate-related reports, program information, and other scientific and technical information.

The Departments Chief Science Officer has been named as Dr. Robert C. Hampshire, PhD. In this role, he will serve as the principal advisor to Secretary Pete Buttigieg on science and technology issues. He is charged with ensuring that DOTs research, development and technology programs are scientifically and technologically well-founded and conducted with integrity. He was previously associate professor at the University of Michigans Gerald R. Ford School of Public Policy and at both the U-M Transportation Research Institutes (UMTRI) Human Factors group and Michigan Institute for Data Science (MIDAS), and holds his PhD from Princeton University.

Climate resilience and environmental justice are at the heart of this Administrations mission to build back betterand that effort must be grounded in scientific expertise, said Buttigieg. Were thrilled to officially name Dr. Hampshire as our Chief Science Officer, and look forward to his contributions to this historic effort.

The re-introduction of a Chief Science Officer underscores transportations key role in addressing the complexity and criticality of our dynamically changing climate. I look forward to working across all modes of transportation to address the immediate concerns, and to ensure our future transportation system is sustainable, said the Acting Assistant Secretary for Research and Technology Robert Hampshire. It is important that USDOT incorporate scientific research to advance climate change initiatives that are fair and equitable to all.

The Departments actions stem from the Presidents Executive Order on Protecting Public Health and the Environment and Restoring Science to Tackle the Climate Crisis and the Presidential Memorandum on Restoring Trust in Government Through Scientific Integrity and Evidence-Based Policymaking.

The Climate Change Center will help coordinate the Departments related research, policies, and actions and support the transportation sector in moving toward a net-zero carbon emissions. The DOT Center for Climate Change and Environmental Forecasting was established during the Clinton Administration to serve as the multi-modal focal point for information and technical expertise on transportation and climate change, coordinating climate-related research, policies, and actions. The Center has been dormant since early 2017.

The Department has assessed public websites and information repositories, including the National Transportation Library, and identified 24 websites and 33 reports and other publications which had been de-published after January 21, 2017. All of these materials have been restored to public access.

The Department will also re-designate a Scientific Integrity Officer, responsible for research policy implementation, who reports directly to the Chief Science Officer.

The transportation sector is the number one producer of greenhouse gases in the U.S., which underscores the ability of the transportation industry and the Department to quickly and meaningfully reduce greenhouse gases and address the climate crisis. These actions are the first steps in returning the Department to its position as a leader in addressing climate change and environmental justice.

Read the announcement at the Department of Transportation

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DOT Appoints Chief Science Officer for the First Time in Over 40 Years Homeland Security Today - HSToday