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A Straightforward HPV16 Lineage Classification Based on Machine Learning

#artificialintelligence

Human Papillomavirus (HPV) is the causal agent of 5% of cancers worldwide and the main cause of cervical cancer and it is also associated with a significant percentage of oropharyngeal and anogenital cancers. More than 60% of cervical cancers are caused by HPV16 genotype, which has been classified into lineages (A, B, C, and D). Lineages are related to the progression of cervical cancer and the current method to assess lineages is by building a Maximum Likelihood Tree (MLT); which is slow, it cannot assess poor sequenced samples, and annotation is done manually. In this study, we have developed a new model to assess HPV16 lineage using machine learning tools. A total of 645 HPV16 genomes were analyzed using Genome-Wide Association Study (GWAS), which identified 56 lineage-specific Single Nucleotide Polymorphisms (SNPs). From the SNPs found, training-test models were constructed using different algorithms such as Random Forest (RF), Support Vector Machine (SVM), and K-nearest neighbor (KNN). A distinct set of HPV16 sequences (n = 1,028), whose lineage was previously determined by MLT, was used for validation. The RF-based model allowed a precise assignment of HPV16 lineage, showing an accuracy of 99.5% in the known lineage samples. Moreover, the RF model could assess lineage to 273 samples that MLT could not determine. In terms of computer consuming time, the RF-based model was almost 40 times faster than MLT. Having a fast and efficient method for assigning HPV16 lineages,...


An enhanced Genetic Folding algorithm for prostate and breast cancer detection

#artificialintelligence

Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model’s area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed.


How AI and human intelligence will beat most cancers - Channel969

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We're excited to carry Rework 2022 again in-person July 19 and just about July 20 – 28. Be a part of AI and information leaders for insightful talks and thrilling networking alternatives. For context, Go is a board sport beforehand thought to require an excessive amount of human instinct for a pc to reach, and in consequence, it was a North Star for AI. For years, researchers tried and didn't create an AI system that would beat people within the sport. In 2016, AlphaGo, an AI system created by Google's DeepMind, not solely beat its champion human counterpart (Lee Sedol); it demonstrated that machines might discover enjoying methods that no human would give you.


Dementia breakthrough: Simple brain scan can detect early-stage Alzheimer's with 98% accuracy

Daily Mail - Science & tech

A simple brain scan can detect people with early-stage Alzheimer's disease, a study suggests. In what could be a breakthrough, researchers have developed an algorithm that can diagnose the condition with up to 98 per cent accuracy. The computer programme uses standard MRI technology found in most hospitals to produce a result in 12 hours. Currently it can take months to diagnose the disease on the NHS and requires a raft of memory and cognitive tests as well as scans. Researchers from Imperial College London who developed the algorithm, which was tested on more than 400 people, hope it will be rolled out on the NHS by 2025.


Scientists Used a Netflix-Style Algorithm to Create Blueprint of Cancer Genomes

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An international team of researchers led by Dr. Nischalan Pillay (UCL Cancer Institute) and Dr. Ludmil Alexandrov (University of California, San Diego) used AI to identify 21 frequent faults in the structure, order, and quantity of copies of DNA present when cancer begins and progresses. These widespread errors, known as copy number signatures, could help doctors find medicines that match the tumor's characteristics. As the Netflix algorithm suggests new videos on the basis of a person's like and dislikes, the researchers developed a similar algorithm that can filter through thousands of lines of genomic data to find common patterns in the way chromosomes organize and arrange themselves. The system may then classify the patterns that develop, assisting scientists in determining the types of cancer faults that can form. DNA alterations, such as gains and losses, frequently occur in cancer and result from a variety of interconnected events, including replication stress, mitotic mistakes, spindle multipolarity, and breakage–fusion–bridge cycles, which can cause chromosomal instability and aneuploidy.


How AI and human intelligence will beat cancer

#artificialintelligence

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. For context, Go is a board game previously thought to require too much human intuition for a computer to succeed in, and as a result, it was a North Star for AI. For years, researchers tried and failed to create an AI system that could beat humans in the game. In 2016, AlphaGo, an AI system created by Google's DeepMind, not only beat its champion human counterpart (Lee Sedol); it demonstrated that machines could find playing strategies that no human would come up with. AlphaGo shocked the world when it performed its unimaginable move #37.


AI algorithm finding similar…cancer recommendations?

#artificialintelligence

An international team of researchers have used AI to map signals across the entire genome that herald the beginnings of cancer. The researchers, who say that their algorithm is similar to that used by Netflix, have identified 21 common faults that occur in human DNA when cancer begins to grow. "Cancer is a complex disease, but we've demonstrated that there are remarkable similarities in the changes to chromosomes that happen when it starts and how it grows," says Dr Ludmil Alexandrov, an associate professor at the University of California, San Diego, US, and co-lead author on a paper describing the research, published in Nature. "Just as Netflix can predict which shows you'll choose to binge watch next, we believe that we will be able to predict how your cancer is likely to behave, based on the changes its genome has previously experienced." The researchers used their algorithm to examine genomic data from 9,873 patients, who had 33 different types of cancer.


Cancer therapies depend on dizzying amounts of data: Here's how it's getting sorted in the cloud

ZDNet

Cancer patients and their doctors have more information about the disease and its treatment than ever before, and the information available continues to grow at a dizzying rate. Think about a lung cancer patient, for instance, who might receive an early diagnosis through a screening program that produces a computed tomography (CT) image. As their diagnosis and treatment plan advances, their caretakers will bring in data sources like MR and molecular imaging, pathology data -- which is increasingly digitized -- and genomics information. "All of this, honestly, is a very difficult challenge for the care teams themselves as they're thinking about how to best care for and treat these patients," Louis Culot, GM of genomics and oncology informatics at Philips, said during an Amazon Web Services virtual event for the health industry. "In oncology now, or in any any medical discipline, this matters because the treatment matters, the intervention matters," Culot said.


Lunit Files Registration Statement for Initial Public Offering

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First Korean healthcare company to obtain "AA-AA" ratings in technology assessment for its FDA-cleared and CE-marked solutions Lunit intends to list its common stock on the KOSDAQ market under the ticker code "A32813". NH Investment & Securities will act as book-running manager, backing Lunit's debut. A total of 1,124,300 shares will be offered in the price range of KRW 44,000 to 49,000 ($34-38). The exact price will be determined after recording the demand of institutional investors on July 7-8, while retail buyers can take part in the public subscription during July 12-13. Based on the low end of the targeted range, Lunit expects to raise about KRW 54 billion ($42 million).


Men's Health Week: How AI is Tackling these 10 Healthcare Issues in Men

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International Men's Health Week is observed in several countries during the week leading up to and including Father's Day. The main goal of this health campaign's annual celebration is to increase awareness of preventable health issues (both physical and emotional) among men and boys, as well as to encourage early disease detection and treatment. This year's Men's Health Week will take place from June 10 to 16. This is a great time for all males to think about their health. Diabetes is a condition in which blood glucose levels in the body grow to dangerously high levels.