Bioinformatics in the context of "Cluster analysis"

Biomedical sciences are a set of sciences applying portions of natural science or formal science, or both, to develop knowledge, interventions, or technology that are of use in healthcare or public health. Such disciplines as medical microbiology, clinical virology, clinical epidemiology, genetic epidemiology, and biomedical engineering are medical sciences. In explaining physiological mechanisms operating in pathological processes, however, pathophysiology can be regarded as basic science.

Biomedical Sciences, as defined by the UK Quality Assurance Agency for Higher Education Benchmark Statement in 2015, includes those science disciplines whose primary focus is the biology of human health and disease and ranges from the generic study of biomedical sciences and human biology to more specialised subject areas such as pharmacology, human physiology and human nutrition. It is underpinned by relevant basic sciences including anatomy and physiology, cell biology, biochemistry, microbiology, genetics and molecular biology, pharmacology, immunology, mathematics and statistics, and bioinformatics. As such the biomedical sciences have a much wider range of academic and research activities and economic significance than that defined by hospital laboratory sciences. Biomedical Sciences are the major focus of bioscience research and funding in the 21st century.

Molecular medicine is a broad field, where physical, chemical, biological, bioinformatics and medical techniques are used to describe molecular structures and mechanisms, identify fundamental molecular and genetic errors of disease, and to develop molecular interventions to correct them. The molecular medicine perspective emphasizes cellular and molecular phenomena and interventions rather than the previous conceptual and observational focus on patients and their organs.

A phylogenetic tree or phylogeny is a graphical representation which shows the evolutionary history between a set of species or taxa during a specific time. In other words, it is a branching diagram or a tree showing the evolutionary relationships among various biological species or other entities based upon similarities and differences in their physical or genetic characteristics. In evolutionary biology, all life on Earth is theoretically part of a single phylogenetic tree, indicating common ancestry. Phylogenetics is the study of phylogenetic trees. The main challenge is to find a phylogenetic tree representing optimal evolutionary ancestry between a set of species or taxa. Computational phylogenetics (also phylogeny inference) focuses on the algorithms involved in finding optimal phylogenetic tree in the phylogenetic landscape.

Phylogenetic trees may be rooted or unrooted. In a rooted phylogenetic tree, each node with descendants represents the inferred most recent common ancestor of those descendants, and the edge lengths in some trees may be interpreted as time estimates. Each node is called a taxonomic unit. Internal nodes are generally called hypothetical taxonomic units, as they cannot be directly observed. Trees are useful in fields of biology such as bioinformatics, systematics, and phylogenetics. Unrooted trees illustrate only the relatedness of the leaf nodes and do not require the ancestral root to be known or inferred.

A non-coding RNA (ncRNA) is a functional RNA molecule that is not translated into a protein. The DNA sequence from which a functional non-coding RNA is transcribed is often called an RNA gene. Abundant and functionally important types of non-coding RNAs include transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs), as well as small RNAs such as microRNAs, siRNAs, piRNAs, snoRNAs, snRNAs, exRNAs, scaRNAs and the long ncRNAs such as Xist and HOTAIR.

The number of non-coding RNAs within the human genome is unknown; however, recent transcriptomic and bioinformatic studies suggest that there are thousands of non-coding transcripts. Many of the newly identified ncRNAs have unknown functions, if any. There is no consensus on how much of non-coding transcription is functional: some believe most ncRNAs to be non-functional "junk RNA", spurious transcriptions, while others expect that many non-coding transcripts have functions to be discovered.

The most recent understanding of the evolution of insects is based on studies of the following branches of science: molecular biology, insect morphology, paleontology, insect taxonomy, evolution, embryology, bioinformatics and scientific computing. The study of insect fossils is known as paleoentomology. It is estimated that the class of insects originated on Earth about 480 million years ago, in the Ordovician, at about the same time terrestrial plants appeared. Insects are thought to have evolved from a group of crustaceans. The first insects were landbound, but about 400 million years ago in the Devonian period one lineage of insects evolved flight, the first animals to do so. The oldest insect fossil has been proposed to be Rhyniognatha hirsti, estimated to be 400 million years old, but the insect identity of the fossil has been contested. Global climate conditions changed several times during the history of Earth, and along with it the diversity of insects. The Pterygotes (winged insects) underwent a major radiation in the Carboniferous (358 to 299 million years ago) while the Endopterygota (insects that go through different life stages with metamorphosis) underwent another major radiation in the Permian (299 to 252 million years ago).

Most extant orders of insects developed during the Permian period. Many of the early groups became extinct during the mass extinction at the Permo-Triassic boundary, the largest extinction event in the history of the Earth, around 252 million years ago. The survivors of this event evolved in the Triassic (252 to 201 million years ago) to what are essentially the modern insect orders that persist to this day. Most modern insect families appeared in the Jurassic (201 to 145 million years ago).

In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be supervised, semi-supervised or unsupervised.

Some common deep learning network architectures include fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance fields. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.