With the advent and popularity of big data mining and huge text analysis in modern times, automated text summarization became prominent for extracting and retrieving important information from documents. This research investigates aspects of automatic text summarization from the perspectives of single and multiple documents. Summarization is a task of condensing huge text articles into short, summarized versions. The text is reduced in size for summarization purpose but preserving key vital information and retaining the meaning of the original document. This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling from summarised medical science journal articles with topics related to genes and diseases. In this study, PyLDAvis web-based interactive visualization tool was used to visualise the selected topics. The visualisation provides an overarching view of the main topics while allowing and attributing deep meaning to the prevalence individual topic. This study presents a novel approach to summarization of single and multiple documents. The results suggest the terms ranked purely by considering their probability of the topic prevalence within the processed document using extractive summarization technique. PyLDAvis visualization describes the flexibility of exploring the terms of the topics' association to the fitted LDA model. The topic modelling result shows prevalence within topics 1 and 2. This association reveals that there is similarity between the terms in topic 1 and 2 in this study. The efficacy of the LDA and the extractive summarization methods were measured using Latent Semantic Analysis (LSA) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliability and validity of the model.

A team of LSU researchers has developed a way to determine which drug therapies work best against an individual's unique type of cancer, possibly providing a way to find cures more quickly and make treatment more affordable. The interdisciplinary team includes researchers from the School of Veterinary Medicine, College of Science, College of Engineering and the Center for Computation & Technology. It created CancerOmicsNet, a new drug discovery engine run by artificial intelligence. Using algorithms originally designed to map complex social networks, like those utilized by Facebook, researchers generated three-dimensional graphs of molecular datasets that include cancer cell lines, drug compounds and interactions among proteins inside the human body. The graphs are then analyzed and interconnected by AI, forming a much clearer picture of how a specific cancer would respond to a specific drug.

The U.S. startup company DeepCure, which works to develop medications with the help of artificial intelligence, said this week that it will be opening a lab and offices in Israel for the first time. DeepCure is part of an emerging wave of companies seeking to improve and accelerate the drug-development process with tools like machine learning and AI. It was formed in 2018 by CEO Kfir Schreiber, alongside Joseph Jacobson and Thrasyvoulos (Thras) Karydis, who are today chief science and chief technology officer, respectively. The three met as students at the Massachusetts Institute of Technology. The company is developing small molecules drugs– in other words, medicines generally sold in capsule form, as opposed to antibody-based biological therapies given as an infusion, for example. DeepCure currently has five development programs underway for therapies against cancer, inflammatory diseases and nervous system diseases.

Cancer is known to be the second leading cause of death globally. According to the World Health Organization (WHO), there were around 9.6 million deaths worldwide in the year 2018 due to it. And the number of deaths from cancer is staggering day-by-day. So, we need a powerful weapon to fight cancer. And in today's digital era, the best weapon to fight cancer is Artificial intelligence or AI. In modern times, the utilization of cutting-edge'AI as a service' solutions has tremendously increased in the healthcare industry, especially in the field of oncology.

Researchers from Queen Mary University of London have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth. The approach may have the potential to advance personalised therapies in the future by allowing oncologists to select the best drugs to treat individual cancer patients. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. These are exciting results because previous machine learning methods have failed to accurately predict drug responses in verification datasets, and they demonstrate the robustness and wide applicability of our method."

Tiny drug-carrying robots that can move against the direction of blood flow could one day be used to deliver chemotherapy drugs directly to cancer cells. Metin Sitti at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany and his colleagues have developed tiny robots called "microrollers" that can carry cancer drugs and selectively target human breast cancer cells. The team drew inspiration for design of the robots from white blood cells in the human body, which can move along the walls of blood vessels against the direction of blood flow. The microrollers are made from glass microparticles and are spherical in shape. One half of the robot was coated with a thin magnetic nanofilm made from nickel and gold.

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced “omics” coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent.

Growing miniature versions of tumours and human organs could pave the way to better cancer treatments, researchers say. By taking cells from a diseased patient and creating a replica of their flesh in a lab, scientists could test which cancer drugs are most likely to work for them. The procedure can be done in less than two weeks and test hundreds of drugs without giving any to the patient until doctors have decided which could work best. This could mean individuals get more specific and tailored treatment instead of drugs which doctors only hope will work. Researchers at the University of California, Los Angeles (UCLA) tested the process on patients with high-grade carcinomas and ovarian cancers.

You are free to share this article under the Attribution 4.0 International license. Researchers have harnessed a powerful artificial intelligence platform to successfully treat a patient with advanced cancer, completely halting disease progression. The development represents a big step forward in personalized medicine, they say. In this clinical study, researchers gave a patient with metastatic castration-resistant prostate cancer (MCRPC) a novel drug combination consisting of the investigational drug ZEN-3694 and enzalutamide, an approved prostate cancer drug. The research team successfully used the platform, called CURATE.AI, to continuously identify the optimal doses of each drug to result in a durable response, allowing the patient to resume a completely normal and active lifestyle.

"The bottom line is that we need new ways to analyze the massive amounts of cancer genome data, and this [method] is one that looks like it can lead to exciting and unexpected discoveries, and maybe treatments someday," said Dr. Bruce Clurman, one of the lead researchers on the work. The study was published Monday in the Proceedings of the National Academy of Sciences of the United States of America. Clurman, executive vice president and deputy director of Fred Hutchinson Cancer Research Center and holder of the Rosput Reynolds Endowed Chair at the Hutch, described the study and its implications in the following interview, which has been edited for brevity and clarity. This is a protein that degrades a lot of other proteins that drive cancer. It's called a tumor suppressor.