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PRINCETON, N.J.--(BUSINESS WIRE)-- ArrePath, an anti-infective drug discovery company addressing the global health challenge of drug resistant infections, announced today that it has raised $20 million in seed financing to advance its proprietary, machine learning (ML)-based platform for the discovery of new classes of anti-infectives to overcome antimicrobial resistance (AMR). The Boehringer Ingelheim Venture Fund, Insight Partners, and Innospark Ventures co-led the financing, which also included Viva BioInnovator, Arimed Capital, PTX Capital, and Noreaster Ventures.

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Dr. Lloyd Payne, ArrePath President and CEO (Photo: Business Wire)

ArrePath also announced that Dr. Lloyd Payne has been named President and CEO. Dr. Payne, who serves on the Novo REPAIR Impact Fund Scientific Selection Board and the AMR Action Fund Scientific Advisory Board, brings more than 25 years of scientific and business leadership in the discovery and development of anti-infectives. Prior to joining ArrePath, Dr. Payne served at Evotec, as Executive Vice President, Head of Anti-infectives, where he led the companys anti-infective discovery and translational microbiology businesses. Previously, he founded Euprotec, a life sciences company focused on anti-infective drug discovery and development and served as its CEO until its acquisition by Evotec.

ArrePath is an anti-infective drug discovery company addressing the global health challenge of drug resistant infections, which result in at least 1.27 million deaths, annually (according to recent analysis of data from 2019 published in The Lancet, The GRAM Report). New classes of antibiotics that more effectively treat drug resistant infections and overcome antimicrobial resistance are urgently needed. ArrePaths approach leverages a novel technology platform to enable the discovery and development of new and differentiated antibiotics by decoding the complex behavior of bacteria when exposed to new chemical entities (known as bacterial autopsies), leading to the efficient elucidation of biological mechanisms of action. Utilizing proprietary machine learning and imaging technologies, ArrePaths platform enables an unprecedented ability to identify, optimize, and rapidly develop new classes of anti-infectives with differentiated mechanisms of action compared to those exhibited by existing drugs.

The Boehringer Ingelheim Venture Fund is proud to cofound ArrePath with Dr. Zemer Gitai and co-lead the seed series investment to accelerate development of the companys potentially transformative Artificial Intelligence / Machine Learning-based drug discovery platform. Additionally, we are delighted that Dr. Payne, a highly experienced executive and leader in anti-infective drug discovery and development, has joined ArrePath as President and CEO, said Fei Shen, Ph.D., Managing Director, Boehringer Ingelheim Venture Fund USA. Antimicrobial resistance is an area with critical unmet medical need and is one of the Boehringer Ingelheim Venture Funds investment focuses globally. We are committed to playing a key role in the global ecosystem to solve the commercial challenges the area faces and supporting the next generation of anti-infective medicines, added Dr. Shen.

I am delighted to join ArrePath and work with such a talented team to address the central challenge of antimicrobial resistance and further develop the companys innovative platform technology, said Dr. Lloyd Payne, ArrePaths President and CEO. This financing is a strong vote of confidence, by a global investment syndicate, in our platform and its enormous potential in anti-infective drug discovery. The funding will enable the advancement of our initial leads and expansion of our discovery efforts, as well as the enhancement of our imaging platform and the application of machine learning in the discovery of new drugs to address critical global health challenges.

Dr. Gitai, Edwin Grant Conklin Professor of Biology at Princeton University and colleagues published proof-of-concept for the approach in the journal Cell in June 2020. The research describes the identification of a compound with a novel dual mechanism of action against both Gram-negative and Gram-positive bacteria and the platform has since identified additional compounds with novel mechanisms. A Princeton University spin-out, the company has an exclusive option from the university to license intellectual property related to the platform technology, and novel compounds generated through its application.

The worldwide clinical need for new antibiotics that overcome antimicrobial resistance is significant. Analysis by the US Centers for Disease Control and Prevention (CDC) has shown that, in the U.S. alone, drug resistant infections result in at least 35,000 deaths annually. It has been estimated that patients spend an aggregate of eight million additional days in the hospital due to drug resistant infections and cost the U.S. healthcare system between $21 billion and $34 billion. A report by the World Health Organization previously estimated that 750,000 people die each year from resistant infections worldwide but this number has recently been updated. The Lancet report (The GRAM Report), published online, in January 2022, analyzed the global burden of bacterial antimicrobial resistance in 2019 and found at least 1.27 million deaths per year are directly attributable to AMR. The UK-commissioned ONeill Review estimates that unless action is taken, the burden of deaths from AMR could balloon to 10 million lives each year by 2050, at a cumulative cost to global economic output of 100 trillion USD.

About Boehringer Ingelheim Venture Fund

Created in 2010, the Boehringer Ingelheim Venture Fund (BIVF) invests in ground-breaking therapeutics-focused biotechnology companies to drive innovation in biomedical research. BIVF is searching for significant enhancements in patient care through pioneering science and its clinical translation by building long-term relationships with scientists and entrepreneurs. BIVFs focus is to target unprecedented therapeutic concepts addressing high medical needs in immuno-oncology, regenerative medicine, infectious diseases, and digital health. These may include novel platform technologies to address so far undruggable targets, new generation vaccines and/or new biological entities. BIVF takes an active role with its portfolio companies delivering significant added value through its own extensive drug discovery, scientific and managerial expertise. BIVF has a fund volume of 300 million euros and currently supervises a portfolio of more than 40 companies. For more information, please visit http://www.boehringer-ingelheim-venture.com

About Insight Partners

Insight Partners is a leading global venture capital and private equity firm investing in high-growth technology and software ScaleUp companies that are driving transformative change in their industries. Founded in 1995, Insight Partners has invested in more than 400 companies worldwide and has raised through a series of funds more than $30 billion in capital commitments. Insight's mission is to find, fund, and work successfully with visionary executives, providing them with practical, hands-on software expertise to foster long-term success. Across its people and its portfolio, Insight encourages a culture around a belief that ScaleUp companies and growth create opportunity for all. For more information on Insight and all its investments, visit http://www.insightpartners.com or follow us on Twitter @insightpartners.

About Innospark Ventures

Innospark Ventures is a Boston-based early-stage venture fund investing in the AI-powered economy. We believe that computational intelligence (AI) will play an outsized role in the years to come, impacting businesses and industries alike. With decades of entrepreneurial experience across several AI startups, and deep investment expertise across stages, we are thoughtful in our approach to investing. We like foundational, deeply impactful ideas. We view our investments as the beginning of a partnership versus the culmination of a diligence process. Our unique no-LP model allows for patient, founder-friendly, capital. Since the fall of 2018, Innospark has made 20+ investments across the healthcare, life sciences, B2B enterprise, cybersecurity, and education sectors. We are excited to partner with the next generation of founders and can be reached at: info@innosparkventures.com

About ArrePath

ArrePath is an anti-infective drug discovery company addressing the global health challenge of drug resistant infections. The companys proprietary machine learning (ML)-based platform enables the rapid and efficient identification of anti-infective agents with new mechanisms of action at the outset of the discovery process, through a deep understanding and analysis of pathogen behavior. The platforms proof-of-concept has been demonstrated in studies published in Cell by ArrePaths scientific founder, Zemer Gitai, Ph.D., Edwin Grant Conklin Professor of Biology at Princeton University. The company is funded by the Boehringer Ingelheim Venture Fund, Insight Partners, Innospark Ventures, Viva BioInnovator, Arimed Capital, PTX Capital, and Noreaster Ventures. Learn more at http://www.arrepath.com and follow us on Twitter @ArrePath.

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ArrePath Announces $20 Million Seed Financing to Advance its Machine Learning-based Platform for Discovery of Novel Anti-infectives Addressing...

Wipros hiring hackathon sustainability machine learning challenge concluded on February 14, 2022. The hackathon had close to 1,880 participants and 550+ solutions posted on the leaderboard. The top three winners of the hackathon will receive cash prizes worth INR 3.5 lakh.

AIM spoke to the winners to understand their data science journey, winning approach, and their overall experience at MachineHack.

Ranjan is currently working at TheMathCompany as a data scientist. A former Infoscian, he has worked in the areas of data science and analytics in the IT and services/product industry. He is skilled in machine learning, modelling, and visualisation using Python. Ranjan is also proficient in strong onsite client interaction and analysing stakeholders needs.

I love interacting with data and creating models which best suit the business needs, along with participating in different ML hackathons to learn new technology and grow professionally, said Ranjan.

I have been part of MachineHack from its inception. Hackathons like these boost the confidence of any aspiring data scientist and help us to grow more technically proficient in the ML/DS field, as consistency is the key to learn and grow, said Ranjan.

Check out the code here.

Taps Das currently works as a data engineer in TheMathCompany. He got interested in machine learning and deep learning in 2018. I went through different MOOCs like the Andrew Ng ML course and Deep Learning Specialisation course on Coursera, said Das.

Further, he said he spent a significant amount of time learning Python programming basics. He then started picking diverse projects from online sources like Kaggle, HackerEarth, Driven Data, to get proficient.

He also participates in various hackathons to stay ahead of the curve. I was inspired and overwhelmed by the ability of ML algorithms to solve a variety of real-world problems, he added.

Das started with extensive EDA to explore the training dataset, which resulted in a few interesting insights, including

After this, he filtered out all records in both train/test datasets, which satisfied the above conditions. He then used feature engineering for the rest of the records, which generated the below feature types.

I changed the problem statement from time-series forecasting to purely regression problem and trained different tree-based models on the same. Finally, I used a weighted average ensemble of LightGBM, CatBoost and XGBoost models to generate the final predictions. Also, I used the Optuna library for hyperparameters search for the different models, said Das.

Competitive DS is a whole different ball game. The winning solutions of most of these challenges involve techniques that are seldom taught in academia, but are used in many production systems, said Das.

For a while, he has been participating in different hackathons on the MachineHack platform. He said he loves how the platform allows anyone, regardless of background or prior experience, to compete on a level playing field where the only thing that matters is optimising a metric.

Winning solutions from previous hackathons are an invaluable learning resource that I highly encourage aspiring participants to leverage. It is fun to compete with the greatest minds in the area of data science, added Das.

Check out the code here.

AppliedAI workshop at my college was my first step, after which I took some courses at Coursera. After taking Deeplearning.ai in my third year of B.Tech, I got a summer internship, then I got my first job offer, became a Kaggle notebook expert and eventually became a grandmaster. Then I got a full-time position, and now I am working at Karmalife.ai as a data scientist, said Durgaprasad.

In the data processing step, after some experiments, Durgaprasad figured out there was some correction over the years. So he took each year as one fold. hE then trained 10-folds each year as a fold and found that Public LB was calculated on 30 percent of data. This was the multi-label regression problem, said Durgaprasad. Hence, he trained the model on each fold, predicted and saved oofs and test prediction for each fold.

In the feature engineering step, he used sample code for creating rolling and shifting features. In the modelling part, he tried catboost multi-label regression and LSTM without any feature engineering, and achieved good results.

MachineHack hackathons are one of my favourite platforms for learning from others and collaborating with others, said Durgaprasad.

Check out the code here.

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Meet the winners of Wipro's Sustainability Machine Learning Challenge - Analytics India Magazine

SAN MATEO, Calif., March 1, 2022 /PRNewswire/ -- Apixio, Inc., the healthcare analytics company, today announced the launch of its new Apicare AuthAdvisor, which uses machine learning and predictive analytics to automate prior authorization decisions for payers. By leveraging historical decision data, AuthAdvisor automates approval for payers, medical benefits managers, and other vendors to deliver decisions within seconds rather than days, and reduces manual reviews by over 50%.

According to the Council for Affordable Quality Healthcare (CAQH), "the cost to complete a prior authorization remains the single highest cost for the healthcare industry at $13.40 per manual transaction and $7.19 per partially electronic web portal transaction." Not only is it costly, but it is also an administrative burden with manual reviews sometimes taking days or weeks, which delays patient treatments, creates obstacles to care, and potentially negatively impacts clinical outcomes.

Apixio's AuthAdvisor solves these problems by automatically approving diagnostics and procedures based on historical data and decisions made by the provider and payer.

"This is a new way to use data science to accelerate one of the most burdensome aspects of healthcare delivery," said Apixio CEO Sachin Patel. "AuthAdvisor relies on the accuracy of an organization's past decisions to process approvals, rather than relying on rules-based approaches that are tedious to maintain and often result in a high number of manual reviews. With AuthAdvisor, approvals are delivered at the speed and scale that today's high-performance healthcare environments demand."

With Apixio's Apicare AuthAdvisor solution, organizations can:

"The AuthAdvisor system is transparent and customizable, giving payers and benefits managers the visibility and flexibility they need to feel confident in the decisions being made," Patel said. "The latest addition to our AI platform, this technology has the potential to not only save tremendous time and money, but also greatly improve care delivery and member satisfaction for millions of Americans."

AuthAdvisor is already active in 16 states, automating authorization requests for over 4,000 different procedures. Apixio will be showcasing its value-based care platform, including Apicare AuthAdvisor, at both RISE National 2022 on March 7-9 in Nashville and HIMSS 2022 at booth #1579 on March 14-18 in Orlando.

To learn more about the Apicare AuthAdivsor solution, visit http://www.apixio.com/apicare-authadvisor/.

About ApixioApixio is advancing healthcare with data-driven intelligence and analytics. Our Artificial Intelligence platform gives organizations across the healthcare spectrum the power to mine clinical information at scale, creating novel insights that will change the way healthcare is measured, care is delivered, and discoveries are made. Learn more atwww.apixio.com.

MEDIA CONTACT:Kerri TarantoNext PR[emailprotected]

SOURCE Apixio Inc.

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Apixio's New Apicare AuthAdvisor Leverages Machine Learning, Predictive Decision-Making to Automate Approvals & Reduce Manual Workload by more...

The number of smartphone users across the world has skyrocketed over the last decade and promises to do so in the future too. Additionally, most business functions can now be executed on mobile devices. However, despite the mobile surge, telecom operators around the world are still not that profitable, with average net profit margins hovering around the 17% mark. The main reasons for the middling profit rates are the high number of market rivals vouching for the same customer base and the high overhead expenses associated with the sector. Communication Service Providers (CSPs) need to become more data-driven to reduce such costs and, automatically, improve their profit margins. Increasing the involvement of AI in telecom operations enables telecom companies to make this switch from rigid, infrastructure-driven operations to a data-driven approach seamlessly.

The inclusion of AI in telecom functional areas positively impacts the bottom line of CSPs in several ways. Businesses can use specific capabilities, avatars or applications of machine learning and AI for this purpose.

Mobile networks are one of the prime components of the ever-expanding internet community. As stated earlier, a large number of internet users and business operations have gone mobile in recent times. Additionally, the emergence of 5G and edge applications, and the impending arrival of the metaverse, will simply increase the need for high-performance telecom networks. It is very likely that the standard automation tech and personnel will be overwhelmed by the relentless pressure of high-speed network connectivity and mobile calls.

The use of AI in telecom operations can transform an underperforming mobile network into a self-optimizing network (SON). Telecom businesses can monitor network equipment and anticipate equipment failure with AI-powered predictive analysis. Additionally, AI-based tools allow CSPs to keep network quality consistently high by monitoring key performance indicators such as traffic on a zone-to-zone basis. Apart from monitoring the performance of equipment, machine learning algorithms can also continually run pattern recognition while scanning network data to detect anomalies. Then, AI-based systems can either perform remedial actions or notify the network administrator and engineers in the region where the anomaly was detected. This enables telecom companies to fix network issues at source before they adversely impact customers.

Network security is another area of focus for telecom operators. Of late, the rising security issues in telecom networks have been a point of concern for CSPs globally. AI-based data security tools allow telecom companies to constantly monitor the cyber health of their networks. Machine learning algorithms perform analysis of global data networks and past security incidents to make key predictions of existing network vulnerabilities. In other words, AI-based network security tools enable telecom businesses to pre-empt future security complications and proactively take preventive measures to deal with them.

Ultimately, AI improves telecom networks in multiple ways. By improving the performance, anomaly detection and security of CSP networks, machine learning algorithms can enhance the user experience for telecom company clients. This will result in a growth of such companies customer base in the long term, and, by extension, an increase in profits.

How Telecom Companies Can Leverage Machine Learning To Boost Their Profits

The Europol classifies the telecom sector to be particularly vulnerable to fraud. Telecom fraud involves the abuse of telecommunications systems such as mobile phones and tablets by criminals to siphon money off CSPs. As per a recent study, telecom fraud accounted for losses of US$40.1 billionapproximately 1.88% of the total revenue of telecom operators. One of the common types of telecom fraud is International Revenue Sharing Fraud (IRSF). IRSF involves criminals linking up with International Premium Rate Number (IPRN) providers to illegally acquire money from telecom companies by using bots to make an absurdly high number of international calls of long duration. Such calls are difficult to trace. Additionally, telecom companies cannot bill clients for such premium calls as the connections are fraudulent. So, telecom operators end up bearing the losses for such calls. The IPRNs and criminals share the spoils between themselves. Apart from IRSF, vishing (a portmanteau for voice calls and phishing attacks) is a way in which malicious entities dupe clients of telecom companies to extract money and data. The involvement of AI in telecom operations enables CSPs to detect and eliminate these kinds of fraud.

Machine learning algorithms assist telecom network engineers with detecting instances of illegal access, fake caller profiles and cloning. To achieve this, the algorithms perform behavioral monitoring of the global telecom networks of CSPs. Accordingly, the network traffic along such networks is closely monitored. The pattern recognition capabilities of AI algorithms come into play again as they enable network administrators to identify contentious scenarios such as several calls being made from a fraudulent number, or blank callsa general indicator of vishingbeing repeatedly made from questionable sources. One of the more prominent examples of telecom companies using data analytics for fraud detection and prevention is Vodafones partnership with Argyle Data. The data science-based firm analyzes the network traffic of the telecom giant for intelligent, data-driven fraud management.

Detecting and eliminating telecom fraud are major steps towards increasing the profit margins of CSPs. As you can see, the role of AI in telecom operations is significant for achieving this objective.

To reliably serve millions of clients, telecom companies need to have a massive workforce that can handle their backend operations on a daily basis efficiently. Dealing with such a large volume of customers creates several opportunities for human error.

Telecom companies can employ cognitive computinga robotics-based field that involves Natural Language Processing (NLP), Robotic Process Automation (RPA) and rule enginesto automate the rule-based processes such as sending marketing emails, autocompleting e-forms, recording data and carrying out certain tasks that can replicate human actions. The use of AI in telecom operations brings greater accuracy in back-office operations. As per a study conducted by Deloitte, several executives in the telecom, media and tech industry felt that the use of cognitive computing for backend operations brought substantial and transformative benefits to their respective businesses.

Customer sentiment analysis involves a set of data classification and analysis tasks carried out to understand the pulse of customers. This allows telecom companies to evaluate whether their clients like or dislike their services based on raw emotions. Marketers can use NLP and AI to sense the "mood" of their customers from their texts, emails or social media posts bearing a telecom companys name. Aspect-based sentiment analytics highlight the exact service areas in which customers have problems. For example, if a customer is upset about the number of calls getting dropped regularly and writes a long and incoherent email to a telcos customer service team about it, the machine learning algorithms employed for sentiment analysis can still autonomously ascertain their mood (angry) and the problem (the call drop rate).

Apart from sentiment analysis, telecom businesses can hugely benefit from the growing emergence of chatbots and virtual assistants. Service requests for network set-ups, installation, troubleshooting and maintenance-based issues can be resolved through such machine learning-based tools and applications. Virtual assistants enable CRM teams in telecom companies to manage a large number of customers with ease. In this way, CSPs can manage customer service and sentiment analysis successfully.

Across the board, users generally cite the quality of their telecom customer service to be below satisfactory. Telecom users are constantly infuriated by long waiting times to get to a service executive, unanswered complaint emails and poor grievance handling by CSPs. Poor CRM does not bode well for telecom companies as it maligns their reputation and diminishes shareholder confidence. By implementing machine learning for CRM, telecom companies can address such issues efficiently.

Like businesses in any other sector, telecom companies need to boost their profits for long-term survival and diversification. As stated at the beginning, there are multiple factors that thwart their chances of profit generation. Going down the data science route is one of the novel ways to overcome such challenges. By involving AI in telecom operations, CSPs can manage their data wisely and channelize their resources towards maximizing revenues.

Despite the positives associated with AI, only a limited percentage of telecom businesses have incorporated the technology for profit maximization. Gradually, one can expect that percentage to rise.

See the article here:
How Telecom Companies Can Leverage Machine Learning To Boost Their Profits - Forbes

Scientists at Rice University are using machine-learning techniques to fine-tune the process of synthesizing graphene from waste through flash Joule heating. The researchers describe in their new work how machine-learning models that adapt to variables and show them how to optimize procedures are helping them push the technique forward.

Machine learning is fine-tuning Rice Universitys flash Joule heating method for making graphene from a variety of carbon sources, including waste materials. Credit: Jacob Beckham, from: Phys.org

The process, discovered by the Rice lab of chemist James Tour, has expanded beyond making graphene from various carbon sources to extracting other materials like metals from urban waste, with the promise of more environmentally friendly recycling to come. The technique is the same: blasting a jolt of high energy through the source material to eliminate all but the desired product. However, the details for flashing each feedstock are different.

"Machine-learning algorithms will be critical to making the flash process rapid and scalable without negatively affecting the graphene product's properties," Prof. Tour said.

"In the coming years, the flash parameters can vary depending on the feedstock, whether it's petroleum-based, coal, plastic, household waste or anything else," he said. "Depending on the type of graphene we wantsmall flake, large flake, high turbostratic, level of puritythe machine can discern by itself what parameters to change."

Because flashing makes graphene in hundreds of milliseconds, it's difficult to follow the details of the chemical process. So Tour and company took a clue from materials scientists who have worked machine learning into their everyday process of discovery.

"It turned out that machine learning and flash Joule heating had really good synergy," said Rice graduate student and lead author Jacob Beckham. "Flash Joule heating is a really powerful technique, but it's difficult to control some of the variables involved, like the rate of current discharge during a reaction. And that's where machine learning can really shine. It's a great tool for finding relationships between multiple variables, even when it's impossible to do a complete search of the parameter space". "That synergy made it possible to synthesize graphene from scrap material based entirely on the models' understanding of the Joule heating process," he explained. "All we had to do was carry out the reactionwhich can eventually be automated."

The lab used its custom optimization model to improve graphene crystallization from four starting materialscarbon black, plastic pyrolysis ash, pyrolyzed rubber tires and cokeover 173 trials, using Raman spectroscopy to characterize the starting materials and graphene products.

The researchers then fed more than 20,000 spectroscopy results to the model and asked it to predict which starting materials would provide the best yield of graphene. The model also took the effects of charge density, sample mass and material type into account in their calculations.

Lat month, the Rice team developed an acoustic processing method to analyze LIG synthesis in real time.

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Machine learning helps improve the flash graphene process - Graphene-Info