#Big data analytics in research

[latest-info]

The Role of Technology in Unveiling Knowledge Horizons

Introduction In today’s rapidly evolving world, technology plays a pivotal role in reshaping the horizons of knowledge. The unprecedented pace at which technology advances enables us to access, analyze, and disseminate information like never before. This article delves into how technology is unveiling new knowledge horizons, transforming education, research, communication, and societal…

[dhirajmarketresearch]

[jcmarchi]

AI’s Life-Changing, Measurable Impact on Cancer

New Post has been published on https://thedigitalinsider.com/ais-life-changing-measurable-impact-on-cancer/

AI’s Life-Changing, Measurable Impact on Cancer

Leveraging Big Data to Enhance AI in Cancer Detection and Treatment

Integrating AI into the healthcare decision making process is helping to revolutionize the field and lead to more accurate and consistent treatment decisions due to its virtually limitless ability to identify patterns too complex for humans to see.

The field of oncology generates enormous data sets, from unstructured clinical histories to imaging and genomic sequencing data, at various stages of the patient journey. AI can “intelligently” analyze large-scale data batches at faster speeds than traditional methods, which is critical for training the machine learning algorithms that are foundational for advanced cancer testing and monitoring tools. AI also has tremendous inherent pattern recognition capabilities for efficiently modeling data set complexities. This is important because it enables deeper, multi-layered understandings of the impact of nuanced molecular signatures in cancer genomics and tumor microenvironments. Discovering a pattern between genes only found in a certain subset of cancer cases or cancer progression patterns can lead to a more tailored, patient-specific approach to treatment.

What is the ultimate goal?  AI-powered cancer tests that support clinical decision-making for doctors and their patients at every step of the cancer journey – from screening and detection, to identifying the right treatment, and for monitoring patients’ response to interventions and predicting recurrence.

Data Quality and Quantity: The Key to AI Success

Ultimately, an AI algorithm will only be as good as the quality of data that trains it. Poor, incomplete or improperly labeled data can hamstring AI’s ability to find the best patterns (garbage in, garbage out). This is especially true for cancer care, where predictive modeling relies on impeccable precision – one gene modification out of thousands, for example, could signal tumor development and inform early detection. Ensuring that high level of quality is time-consuming and costly but leads to better data, which results in optimal testing accuracy. However, developing a useful goldmine of data comes with significant challenges. For one, collecting large-scale genomic and molecular data, which can involve millions of data points, is a complex task. It begins with having the highest quality assays that measure these characteristics of cancer with impeccable precision and resolution.  The molecular data collected must also be as diverse in geography and patient representation as possible to expand the predictive capacity of the training models.  It also benefits from building long-term multi-disciplinary collaborations and partnerships that can help gather and process raw data for analysis. Finally, codifying strict ethics standards in data handling is of paramount importance when it comes to healthcare information and adhering to strict patient privacy regulations, which can sometimes present a challenge in data collection.

An abundance of accurate, detailed data will not only result in testing capabilities that can find patterns quickly and empower physicians with the best opportunity to address the unmet needs for their patients but will also improve and advance every aspect of clinical research, especially the urgent search for better medicines and biomarkers for cancer.

AI Is Already Showing Promise in Cancer Care and Treatment

More effective ways to train AI are already being implemented. My colleagues and I are training algorithms from a comprehensive array of data, including imaging results, biopsy tissue data, multiple forms of genomic sequencing, and protein biomarkers, among other analyses – all of which add up to massive quantities of training data. Our ability to generate data on the scale of quadrillions rather than billions has allowed us to build some of the first truly accurate predictive analytics in clinical use, such as tumor identification for advanced cancers of unknown primary origin or predictive chemotherapy treatment pathways involving subtle genetic variations.

At Caris Life Sciences, we’ve proven that extensive validation and testing of algorithms are necessary, with comparisons to real-world evidence playing a key role. For example, our algorithms trained to detect specific cancers benefit from validation against laboratory histology data, while AI predictions for treatment regimens can be cross compared with real-world clinical survival outcomes.

Given the rapid advancements in cancer research, experience suggests that continuous learning and algorithm refinement is an integral part of a successful AI strategy. As new treatments are developed and our understanding of the biological pathways driving cancer evolves, updating models with the most up-to-date information offers deeper insights and enhances detection sensitivity.

This ongoing learning process highlights the importance of broad collaboration between AI developers and the clinical and research communities. We’ve found that developing new tools to analyze data more rapidly and with greater sensitivity, coupled with feedback from oncologists, is essential. Bottom-line: the true measure of an AI algorithm’s success is how accurately it equips oncologists with reliable, predictive insights they need and how adaptable the AI strategy is to ever-changing treatment paradigms.

Real-World Applications of AI Are Already Increasing Survival Rates and Improving Cancer Management

Advances in data scale and quality have already had measurable impacts by expanding the physician decision-making toolkit, which has had real-world positive results on patient care and survival outcomes. The first clinically validated AI tool for navigating chemotherapy treatment choices for a difficult-to-treat metastatic cancer can potentially  extend patient survival by 17.5 months, compared to standard treatment decisions made without predictive algorithms1. A different AI tool can predict with over 94% accuracy the tumor of origin for dozens of metastatic cancers2 – which is critical to creating an effective treatment plan.  AI algorithms are also predicting how well a tumor will respond to immunotherapy based on each person’s unique tumor genetics. In each of these cases, AI toolkits empower clinical decision-making that improves patient outcomes compared with current standards of care.

Expect An AI Revolution in Cancer

AI is already changing how early we can detect cancer and how we treat it along the way. Cancer management will soon have physicians working side-by-side with integrated AI in real time to treat and monitor patients and stay one step ahead of cancer’s attempts to outwit medicines with mutations. In addition to ever-improving predictive models for detecting cancer earlier and providing more effective personalized treatment paradigms, physicians, researchers, and biotech companies are hard at work today to leverage data and AI analyses to drive new therapeutic discoveries and molecular biomarkers for tomorrow.

In the not-too-distant future, these once-impossible advances in AI will reach far beyond cancer care to all disease states, ending an era of uncertainty and making medicine more accurate, more personalized, and more effective.

[dhallblogs]

Kerala establishes seven advanced research centers of excellence.

Thiruvananthapuram: The Kerala government has approved the establishment of seven Centers of Excellence, which would operate as independent institutions and concentrate on various fields of advanced research and training. These will be manned by elite teachers, researchers, and students, and furnished with cutting-edge amenities.

ALSO READ MORE-https://apacnewsnetwork.com/2024/07/kerala-establishes-seven-advanced-research-centers-of-excellence/

[labtroncc]

Desktop Trace Drug Detector

Labtron Desktop Trace Drug Detector offers rapid, accurate detection of trace amounts of narcotics with a sensitivity limit of 100 ng for TNT and an 8 second analysis time. Features include an audio and visual alert system and advanced ion mobility spectrometry technology, providing real-time results, and ensuring reliable identification of a wide range of drugs. 

[techtoio]

How Big Data Analytics is Changing Scientific Discoveries

Introduction

In the contemporary world of the prevailing sciences and technologies, big data analytics becomes a powerful agent in such a way that scientific discoveries are being orchestrated. At Techtovio, we explore this renewed approach to reshaping research methodologies for better data interpretation and new insights into its hastening process. Read to continue

[ambeone-marketwaysarabia]

The vast amount of data available for retailers today is helping them drive a better, enhanced tailored segmentation for customers different needs and preferences. 

Contact Information:

Address: PO Box: 127239, Business Bay, Dubai, UAE

Ph: +971 (04)4431578

email: info@marketwaysarabia.com

Website: www.marketwaysarabia.com

[easternindiatechnosoft-blog]

Big Data Analysis Company in Kolkata

Introduction

In the dynamic landscape of technology, big data has emerged as a game-changer for businesses worldwide. As organizations in Kolkata increasingly recognize the importance of harnessing data for strategic decision-making, the role of big data analysis companies has become pivotal.

The Rise of Big Data in Kolkata

Kolkata, known for its rich cultural heritage, is also witnessing remarkable growth in the realm of big data. Over the years, the city has transitioned from traditional methods to advanced data analytics, keeping pace with global trends.

Key Players in Kolkata’s Big Data Scene

Prominent among the contributors to this transformation are the leading big data analysis companies in Kolkata. Companies like DataSolve and AnalytixPro have carved a niche for themselves, offering cutting-edge solutions to businesses across various sectors.

Services Offered by Big Data Companies

These companies provide a range of services, including data analytics solutions, machine learning applications, and customized big data solutions tailored to meet the unique needs of their clients.

Impact on Business Decision-Making

The impact of big data on business decision-making cannot be overstated. By analyzing vast datasets, companies can gain valuable insights that inform strategic decisions, leading to increased efficiency and competitiveness.

Challenges and Solutions

However, the journey toward effective big data implementation is not without challenges. Big data companies in Kolkata face issues like data security and integration complexities. Innovative solutions, such as advanced encryption algorithms and seamless integration platforms, are being developed to address these challenges.

Future Prospects

Looking ahead, the future of big data in Kolkata appears promising. The integration of artificial intelligence and the Internet of Things is expected to open new avenues for data analysis, presenting exciting possibilities for businesses in the city.

Case Study: Successful Big Data Implementation

A closer look at a successful big data implementation in Kolkata reveals how a major e-commerce player utilized data analytics to enhance customer experience and optimize supply chain management.

Training and Skill Development

To keep up with the evolving landscape, there is a growing emphasis on training and skill development in the big data industry. Institutes in Kolkata offer comprehensive programs to equip professionals with the necessary skills.

Big Data and Small Businesses

Contrary to popular belief, big data is not exclusive to large enterprises. Big data companies in Kolkata are tailoring their services to suit the needs of small businesses, making data analytics accessible and affordable.

Ethical Considerations in Big Data

As the volume of data being processed increases, ethical considerations become paramount. Big data companies in Kolkata are taking steps to ensure data privacy and uphold ethical standards in their practices.

Expert Insights

Leading experts in the big data industry in Kolkata share their insights on current trends and future developments. Their perspectives shed light on the evolving nature of the industry.

Success Stories

Success stories from businesses in Kolkata highlight the transformative power of big data. From healthcare to finance, these stories underscore the positive impact that data analysis can have on diverse sectors.

Tips for Choosing a Big Data Analysis Company

For businesses considering a partnership with a big data company, careful consideration of factors such as experience, scalability, and data security is crucial. Avoiding common pitfalls in the selection process is key to a successful partnership.

Conclusion

In conclusion, the journey of big data analysis company in Kolkata reflects a broader global trend. As businesses increasingly recognize the value of data, the role of big data analysis companies becomes indispensable. The future promises even greater advancements, making it an exciting time for both businesses and big data professionals in Kolkata.

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[marketxcel]

The Future of Market Research: Unveiling the Top 10 Emerging Trends

The landscape of market research is undergoing a transformative shift, driven by the convergence of technology, consumer behavior, and data-driven insights. Embracing these six emerging trends empowers businesses to connect with their target audiences on a deeper level, adapt to changing market dynamics, and make informed decisions that drive success

[dhirajmarketresearch]

[mostlysignssomeportents]

The surveillance advertising to financial fraud pipeline

Monday (October 2), I'll be in Boise to host an event with VE Schwab. On October 7–8, I'm in Milan to keynote Wired Nextfest.

Being watched sucks. Of all the parenting mistakes I've made, none haunt me more than the times my daughter caught me watching her while she was learning to do something, discovered she was being observed in a vulnerable moment, and abandoned her attempt:

https://www.theguardian.com/technology/blog/2014/may/09/cybersecurity-begins-with-integrity-not-surveillance

It's hard to be your authentic self while you're under surveillance. For that reason alone, the rise and rise of the surveillance industry – an unholy public-private partnership between cops, spooks, and ad-tech scum – is a plague on humanity and a scourge on the Earth:

https://pluralistic.net/2023/08/16/the-second-best-time-is-now/#the-point-of-a-system-is-what-it-does

But beyond the psychic damage surveillance metes out, there are immediate, concrete ways in which surveillance brings us to harm. Ad-tech follows us into abortion clinics and then sells the info to the cops back home in the forced birth states run by Handmaid's Tale LARPers:

https://pluralistic.net/2022/06/29/no-i-in-uter-us/#egged-on

And even if you have the good fortune to live in a state whose motto isn't "There's no 'I" in uter-US," ad-tech also lets anti-abortion propagandists trick you into visiting fake "clinics" who defraud you into giving birth by running out the clock on terminating your pregnancy:

https://pluralistic.net/2023/06/15/paid-medical-disinformation/#crisis-pregnancy-centers

The commercial surveillance industry fuels SWATting, where sociopaths who don't like your internet opinions or are steamed because you beat them at Call of Duty trick the cops into thinking that there's an "active shooter" at your house, provoking the kind of American policing autoimmune reaction that can get you killed:

https://www.cnn.com/2019/09/14/us/swatting-sentence-casey-viner/index.html

There's just a lot of ways that compiling deep, nonconsensual, population-scale surveillance dossiers can bring safety and financial harm to the unwilling subjects of our experiment in digital spying. The wave of "business email compromises" (the infosec term for impersonating your boss to you and tricking you into cleaning out the company bank accounts)? They start with spear phishing, a phishing attack that uses personal information – bought from commercial sources or ganked from leaks – to craft a virtual Big Store con:

https://www.fbi.gov/how-we-can-help-you/safety-resources/scams-and-safety/common-scams-and-crimes/business-email-compromise

It's not just spear-phishers. There are plenty of financial predators who run petty grifts – stock swindles, identity theft, and other petty cons. These scams depend on commercial surveillance, both to target victims (e.g. buying Facebook ads targeting people struggling with medical debt and worried about losing their homes) and to run the con itself (by getting the information needed to pull of a successful identity theft).

In "Consumer Surveillance and Financial Fraud," a new National Bureau of Academic Research paper, a trio of business-school profs – Bo Bian (UBC), Michaela Pagel (WUSTL) and Huan Tang (Wharton) quantify the commercial surveillance industry's relationship to finance crimes:

https://www.nber.org/papers/w31692

The authors take advantage of a time-series of ZIP-code-accurate fraud complaint data from the Consumer Finance Protection Board, supplemented by complaints from the FTC, along with Apple's rollout of App Tracking Transparency, a change to app-based tracking on Apple mobile devices that turned of third-party commercial surveillance unless users explicitly opted into being spied on. More than 96% of Apple users blocked spying:

https://arstechnica.com/gadgets/2021/05/96-of-us-users-opt-out-of-app-tracking-in-ios-14-5-analytics-find/

In other words, they were able to see, neighborhood by neighborhood, what happened to financial fraud when users were able to block commercial surveillance.

What happened is, fraud plunged. Deprived of the raw material for committing fraud, criminals were substantially hampered in their ability to steal from internet users.

While this is something that security professionals have understood for years, this study puts some empirical spine into the large corpus of qualitative accounts of the surveillance-to-fraud pipeline.

As the authors note in their conclusion, this analysis is timely. Google has just rolled out a new surveillance system, the deceptively named "Privacy Sandbox," that every Chrome user is being opted in to unless they find and untick three separate preference tickboxes. You should find and untick these boxes:

https://www.eff.org/deeplinks/2023/09/how-turn-googles-privacy-sandbox-ad-tracking-and-why-you-should

Google has spun, lied and bullied Privacy Sandbox into existence; whenever this program draws enough fire, they rename it (it used to be called FLoC). But as the Apple example showed, no one wants to be spied on – that's why Google makes you find and untick three boxes to opt out of this new form of surveillance.

There is no consensual basis for mass commercial surveillance. The story that "people don't mind ads so long as they're relevant" is a lie. But even if it was true, it wouldn't be enough, because beyond the harms to being our authentic selves that come from the knowledge that we're being observed, surveillance data is a crucial ingredient for all kinds of crime, harassment, and deception.

We can't rely on companies to spy on us responsibly. Apple may have blocked third-party app spying, but they effect nonconsensual, continuous surveillance of every Apple mobile device user, and lie about it:

https://pluralistic.net/2022/11/14/luxury-surveillance/#liar-liar

That's why we should ban commercial surveillance. We should outlaw surveillance advertising. Period:

https://www.eff.org/deeplinks/2022/03/ban-online-behavioral-advertising

Contrary to the claims of surveillance profiteers, this wouldn't reduce the income to ad-supported news and other media – it would increase their revenues, by letting them place ads without relying on the surveillance troves assembled by the Google/Meta ad-tech duopoly, who take the majority of ad-revenue:

https://www.eff.org/deeplinks/2023/05/save-news-we-must-ban-surveillance-advertising

We're 30 years into the commercial surveillance pandemic and Congress still hasn't passed a federal privacy law with a private right of action. But other agencies aren't waiting for Congress. The FTC and DoJ Antitrust Divsision have proposed new merger guidelines that allow regulators to consider privacy harms when companies merge:

https://www.regulations.gov/comment/FTC-2023-0043-1569

Think here of how Google devoured Fitbit and claimed massive troves of extremely personal data, much of which was collected because employers required workers to wear biometric trackers to get the best deal on health care:

https://www.eff.org/deeplinks/2020/04/google-fitbit-merger-would-cement-googles-data-empire

Companies can't be trusted to collect, retain or use our personal data wisely. The right "balance" here is to simply ban that collection, without an explicit opt-in. The way this should work is that companies can't collect private data unless users hunt down and untick three "don't spy on me" boxes. After all, that's the standard that Google has set.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/09/29/ban-surveillance-ads/#sucker-funnel

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[puckpocketed]

[voice of an anthropologist] after careful research and data gathering (5 mins of dicking around on the pages of a bunch of kings replyguys/beat reporters/pundits) eye believe i may have cracked the code : u can tell how frothing mad someone on kingstwt is by what naming convention they use to refer to a player.

Non-exhaustive List:

nicknames them (i.e. juice, Q/QB, kopi, arvie, real deal akil, big save dave/BSD): good bet they’re pretty happy with the player, usually followed up by a clip of said player popping off or some reportage of a stat that makes the player look good.

last name: they’re in Analysis mode and want to seem objective — they aren’t. they never will be. yeah twitter user clarke for norris, you definitely have no biases here babe!!! (they’re just like me fr CALL CLARKIE UP TO THE NHL RN IM SO SERIOUS JIM HILLER)

initials+player number: they’re a tumblr sleeper agent and this is their dogwhistle? (<- working theory)

SPECIAL subcategory!!! Pierre-Luc Dubois Derangement: they never call him dubie (that’s reserved for the actual la kings players and the apologists girlies [gn]) but they will call him PL, PLD, Dubois, 80 — and no matter what, without fail, they will find a way to point out his contract.

using NUMBER ONLY: they’re killing this player/players to death with rocks and want to seem objective but likeee… it comes off as MAJOR overcompensating 2 me <3

common/key phrases:

engaged: vibes-based barometer of how hard they think my disasterwife PLD is trying during the game, varies from person to person but generally stays within the same neighbourhood of agreeing with each other

intangibles: ok i wasn’t present for this one when it happened but jim hiller/kings management is obsessed with Andreas Englund “having intangibles” , which means Clarkie can’t come up from the AHL and everybody disliked that to the point “intangibles” is a meme.

sidebar — things i know about englund: he’s a swedish guy who looks like he churns butter in an apron while living in a cottage, but is actually the kings’ playoff goon (???) he’s STAPLED to jordan spence, who is a much better dman analytics wise and also eye test wise (funniest shit ever is how well spence does away from englund, even funnier is how often kingstwt brings it up)

the 1-3-1: the la kings’ hockey system. 1 guy out the front, 3 guys clogging up entry lanes through the neutral-zone/their own d-zone, 1 guy hanging back. no1 on kingstwt likes it and has wanted it gone for years — still, when the discourse comes around they immediately close ranks to become the biggest 1-3-1 proponent EVER. they will protect the sanctity of their hockey god-given right to play whatever the fuck system they want to!!! even if it’s incredibly annoying <3

[uboat53]

Well, we're about a month out from the presidential election, so it's time to look at the state of the race. And the state of the race is… yeah, there is no real state of the race.

Look, there's enough evidence out there to make a solid case that Trump has the best shot of winning and there's enough evidence to make a solid case that Harris has the best shot of winning. Given the quality (or lack thereof) of the data that we have, it's possible that it's a tie that will come down to a few dozen votes or that one candidate is already running away with it.

That said, it doesn't really matter much. The only thing you, as a voter, can do is vote, and hopefully you were already going to do that anyways. If not, just remember that only those who vote get the right to complain.

Make sure you're registered to vote and, if you feel like you want to do more, get in touch with other people and make sure they're registered as well. You can confirm your registration here if you need to.

After that, just make sure you get out and vote. Federal law permits you to take up to 2 hours paid time off from work to vote, so make sure to take advantage of that if you need to. Also, if your state allows absentee voting, you might take advantage of that as well.

As for the big picture, get used to that being fuzzy until all the votes are counted. The data we have on this election is uniquely poor quality because there are so many moving variables and demographics across the country are changing at light speed. To make it even worse, many states controlled by 2020 election deniers have put in place odd requirements such as hand-counting ballots that make it unlikely that we'll know the results in those states until at least several days after voting is over.

All you can do is what you were hopefully going to do anyways. The data analytics can make it sometimes seem as if the outcome is pre-determined and it doesn't really matter if you vote, but nothing is for certain until all the results are counted and certified. Think about how many elections in our lifetimes have come down to the wire, even 2016 which we were assured was a lock for Clinton.

At the end of the day, forget about all the analysis, all the gamesmanship, and all the data. Just do your part - register to vote, research the issues and candidates, and vote - and the rest will take care of itself. That's all any of us can really do.

[saetoru]

I just read your post about Ai and can I just share that I work in archeology and I'm absolutely terrified of the way AI is becoming normalized within research. The University I work for recently granted our department a large budget for this research project, and instead of hiring new people, or taking even taking in undergrad students as assistant researchers, my superiors have been using ChatGPT. It's absolutely insane to me that it is being recommended for us to use it to create surveys and organize our collected data. we are even being told to use it if we're having trouble writing the summary reports that help us continue getting funding.

I use it almost everyday, and it is not 100% accurate. Its actually wrong a lot of the time when you're using it to do more advanced math so it's very worrisome that it's being used for quantitative research. it is so easy for things to be wrong and no one to even notice.

YES ITS SO SCARY !!! im in healthcare management and the company i’m working with is a supplier for hospitals and the way they use chat gpt to compare pricing and break down data and even just write emails for summaries is so scary because the calculations aren’t always correct and they admit that !! and they still use it !! means for breaking down data aren’t always the most effective. and it’s especially scary bc my friend who is a pharm tech has been consistently complaining that more and more drugs have become hard to order in larger quantities and they are having loads of shortages. they’ve had to limit how much they can give at a time significantly in a few pharmacies in my area and it’s like. if you chat gpt things like calculating when it comes to supplying things for the healthcare industry, you heavily risk accidentally miscalculating how you split things up or wtv the case may be and already scarce items will be even more scarce. it’s just a very very nauseating thing to hear — and young ppl especially !! the company i’m working with has also cut back so many interning spots with data analytics bc they rely on ai to do it !! there are so many young individuals that are slowly being robbed of experience they desperately need to build their careers and the economy isn’t exactly helping with the case either, and :,) it’s just. a rly rly big area for concern bc there seems to be no regulation whatsoever and i can’t wrap my head around it.

and besides the professional aspect, there’s also the social aspect you have to consider. ai generated images are becoming more and more accurate and we’re living in a time where we can start to make anyone’s voice and faces be generated to say / look however we want. there are so many dangers to that i don’t even think i need to go into but from every standpoint all ai screams to me is a way to make life more and more miserable instead of efficient :,)