Collaborating Authors

Experimental Study

Challenges, Experiments, and Computational Solutions in Peer Review

Communications of the ACM

While researchers are trained to do research, there is little training for peer review. Several initiatives and experiments have looked to address this challenge. Recently, the ICML 2020 conference adopted a method to select and then mentor junior reviewers, who would not have been asked to review otherwise, with a motivation of expanding the reviewer pool to address the large volume of submissions.43 An analysis of their reviews revealed that the junior reviewers were more engaged through various stages of the process as compared to conventional reviewers. Moreover, the conference asked meta reviewers to rate all reviews, and 30% of reviews written by junior reviewers received the highest rating by meta reviewers, in contrast to 14% for the main pool. Training reviewers at the beginning of their careers is a good start but may not be enough. There is some evidence8 that quality of an individual's review falls over time, at a slow but steady rate, possibly because of increasing time constraints or in reaction to poor-quality reviews they themselves receive. While researchers are trained to do research, there is little training for peer review … Training reviewers at the beginning of their careers is a good start but may not be enough.

Identification of long COVID patients through machine learning


In a recent study posted to Preprints with The Lancet*, researchers developed a machine learning approach to identify patients with long coronavirus disease (COVID). The post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are called long COVID. In the present study, researchers aimed to generate a robust clinical definition for long COVID using data related to long COVID patients. The team utilized data obtained from electronic health records that were integrated and harmonized in the secure N3C Data Enclave. This allowed the team to identify unique patterns and clinical characteristics among COVID-19-infected patients.

Artificial intelligence makes a splash in small-molecule drug discovery


In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Figure 1a). And the number of financings and average amount raised soared in 2021. At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Figure 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat.

New imaging method makes tiny robots visible in the body


How can a blood clot be removed from the brain without any major surgical intervention? How can a drug be delivered precisely into a diseased organ that is difficult to reach? Those are just two examples of the countless innovations envisioned by the researchers in the field of medical microrobotics. Tiny robots promise to fundamentally change future medical treatments: one day, they could move through patient's vasculature to eliminate malignancies, fight infections or provide precise diagnostic information entirely noninvasively. In principle, so the researchers argue, the circulatory system might serve as an ideal delivery route for the microrobots, since it reaches all organs and tissues in the body.

Doctors Are Very Worried About Medical AI That Predicts Race


To conclude, our study showed that medical AI systems can easily learn to recognise self-reported racial identity from medical images, and that this capability is extremely difficult to isolate,

Cranberries could improve memory and ward off dementia

Daily Mail - Science & tech

Eating a small bowl of cranberries every day could help ward off dementia, research suggested today. Scientists tested giving healthy older adults the equivalent of 100g of the fruit each day. Volunteers who ate a powdered version of the fruit -- which has a notoriously bitter taste -- were found to have a better memory recall after 12 weeks. And MRI scans showed those eating cranberries had better blood flow to important parts of the brain. People given cranberries also had 9 per cent lower bad cholesterol levels, according to the University of East Anglia study.

Predict Ads Click - Practice Data Analysis and Logistic Regression Prediction - Projects Based Learning


In this project we will be working with a data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user. Welcome to this project on predict Ads Click in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing.

Covid lockdown made people more creative, study says

Daily Mail - Science & tech

Whether it was doing the kitting or learning to play a new instrument, the Covid lockdown made people more creative, a new study says. Researchers in Paris have surveyed hundreds of people about activities performed during the first lockdown at the start of the pandemic more than two years ago. Overall, based on almost 400 responses, the team found that people were forced to adapt to a new situation and'rethink our habits', which bred creativity. The researchers also acknowledged that pandemic and stay-at-home rules'restricted our liberties and triggered health or psychological difficulties', however. The new study has been led by researchers from the Frontlab at the Paris Brain Institute in France and published in Frontiers in Psychology.

Deep Learning: Types and Applications in Healthcare


Deep learning (DL), also known as deep structured learning or hierarchical learning, is a subset of machine learning. It is loosely based on the way neurons connect to each other to process information in animal brains. To mimic these connections, DL uses a layered algorithmic architecture known as artificial neural networks (ANNs) to analyze the data. By analyzing how data is filtered through the layers of the ANN and how the layers interact with each other, a DL algorithm can'learn' to make correlations and connections in the data. These capabilities make DL algorithms an innovative tool with the potential to transform healthcare.

La veille de la cybersécurité


Artificial intelligence (AI) is showing promising results in detecting breast cancer which may otherwise have been missed by radiologists, the largest study of its kind has found. Researchers in Germany discovered that AI can correctly detect interval breast cancers, which develop in between routine screening rounds (usually 24 months in many countries) and can be missed and diagnosed as a false negative result. In 2020, there were 2.3 million women diagnosed with breast cancer and 685 000 deaths globally, according to the World Health Organization (WHO). The peer-reviewed study showed approximately 16 per cent of interval cancers are probably visible during a previous screening while one in five may be too subtle to the human eye and can be missed by radiologists, which is known as'minimal signs'. The findings present an opportunity to detect more cancers at screening with AI, which may help detect breast cancer earlier.