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.

Personalized medicine is expected to maximize the intended drug effects and minimize side effects by treating patients based on their genetic profiles. Thus, it is important to generate drugs based on the genetic profiles of diseases, especially in anticancer drug discovery. However, this is challenging because the vast chemical space and variations in cancer properties require a huge time resource to search for proper molecules. Therefore, an efficient and fast search method considering genetic profiles is required for de novo molecular design of anticancer drugs. Here, we propose a faster molecular generative model with genetic algorithm and tree search for cancer samples (FasterGTS). FasterGTS is constructed with a genetic algorithm and a Monte Carlo tree search with three deep neural networks: supervised learning, self-trained, and value networks, and it generates anticancer molecules based on the genetic profiles of a cancer sample. When compared to other methods, FasterGTS generated cancer sample-specific molecules with general chemical properties required for cancer drugs within the limited numbers of samplings. We expect that FasterGTS contributes to the anticancer drug generation.

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."

Cancer has long been a main cause of human death, and the discovery of new drugs and the customization of cancer therapy have puzzled people for a long time. In order to facilitate the discovery of new anti-cancer drugs and the customization of treatment strategy, we seek to predict the response of different anti-cancer drugs with variational autoencoders (VAE) and multi-layer perceptron (MLP).Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data, and encode these data with {\sc {GeneVae}} model, which is an ordinary VAE, and rectified junction tree variational autoencoder ({\sc JtVae}) (\cite{jin2018junction}) model, respectively. Encoded features are processes by a Multi-layer Perceptron (MLP) model to produce a final prediction. We reach an average coefficient of determination ($R^{2} = 0.83$) in predicting drug response on breast cancer cell lines and an average $R^{2} > 0.84$ on pan-cancer cell lines. Additionally, we show that our model can generate unseen effective drug compounds for specific cancer cell lines.

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.