Deep reinforcement learningDeep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.
Neuroendocrine tumorNeuroendocrine tumors (NETs) are neoplasms that arise from cells of the endocrine (hormonal) and nervous systems. They most commonly occur in the intestine, where they are often called carcinoid tumors, but they are also found in the pancreas, lung, and the rest of the body. Although there are many kinds of NETs, they are treated as a group of tissue because the cells of these neoplasms share common features, including a similar histological appearance, having special secretory granules, and often producing biogenic amines and polypeptide hormones.
Prostate cancerProstate cancer is the uncontrolled growth of cells in the prostate, a gland in the male reproductive system just below the bladder. Early prostate cancer usually causes no symptoms. As the tumor grows, it can damage nearby organs causing erectile dysfunction, blood in the urine or semen, and trouble urinating. Some tumors eventually spread to other areas of the body, particularly the bones and lymph nodes. There, tumors cause severe bone pain, leg weakness or paralysis, and eventually death.
Attention (machine learning)Machine learning-based attention is a mechanism mimicking cognitive attention. It calculates "soft" weights for each word, more precisely for its embedding, in the context window. It can do it either in parallel (such as in transformers) or sequentially (such as recursive neural networks). "Soft" weights can change during each runtime, in contrast to "hard" weights, which are (pre-)trained and fine-tuned and remain frozen afterwards. Multiple attention heads are used in transformer-based large language models.
Reinforcement learningReinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.
Somatic mutationA somatic mutation is a change in the DNA sequence of a somatic cell of a multicellular organism with dedicated reproductive cells; that is, any mutation that occurs in a cell other than a gamete, germ cell, or gametocyte. Unlike germline mutations, which can be passed on to the descendants of an organism, somatic mutations are not usually transmitted to descendants. This distinction is blurred in plants, which lack a dedicated germline, and in those animals that can reproduce asexually through mechanisms such as budding, as in members of the cnidarian genus Hydra.
Point mutationA point mutation is a genetic mutation where a single nucleotide base is changed, inserted or deleted from a DNA or RNA sequence of an organism's genome. Point mutations have a variety of effects on the downstream protein product—consequences that are moderately predictable based upon the specifics of the mutation. These consequences can range from no effect (e.g. synonymous mutations) to deleterious effects (e.g. frameshift mutations), with regard to protein production, composition, and function.
Personalized medicinePersonalized medicine, also referred to as precision medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept though some authors and organisations use these expressions separately to indicate particular nuances.
Treatment of cancerCancer can be treated by surgery, chemotherapy, radiation therapy, hormonal therapy, targeted therapy (including immunotherapy such as monoclonal antibody therapy) and synthetic lethality, most commonly as a series of separate treatments (e.g. chemotherapy before surgery). The choice of therapy depends upon the location and grade of the tumor and the stage of the disease, as well as the general state of the patient (performance status). Cancer genome sequencing helps in determining which cancer the patient exactly has for determining the best therapy for the cancer.
Q-learningQ-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.
