Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0387

Opportunities and obstacles for deep learning in biology and medicine.

Motivation Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly. Furthermore, features are often optimized for a specific gold standard corpus, which makes extrapolation of quality measures difficult. Results We show that a completely generic method based on deep learning and statistical word embeddings [called long short-term memory network-conditional random field (LSTM-CRF)] outperforms state-of-the-art entity-specific NER tools, and often by a large margin. To this end, we compared the performance of LSTM-CRF on 33 data sets covering five different entity classes with that of best-of-class NER tools and an entity-agnostic CRF implementation. On average, F1-score of LSTM-CRF is 5% above that of the baselines, mostly due to a sharp increase in recall. Availability and implementation The source code for LSTM-CRF is available at https://github.com/glample/tagger and the links to the corpora are available at https://corposaurus.github.io/corpora/ . Contact habibima@informatik.hu-berlin.de.

/pdf/deep-learning-with-word-embeddings-improves-biomedical-named-4ertt2v5or.pdf

Deep learning with word embeddings improves biomedical named entity recognition.

Recent genome-wide studies have reported novel associations between common polymorphisms and susceptibility to many major infectious diseases in humans In parallel, an increasing number of rare mutations underlying susceptibility to specific phenotypes of infectious disease have been described Together, these developments have highlighted a key role for host genetic variation in determining the susceptibility to infectious disease They have also provided insights into the genetic architecture of infectious disease susceptibility and identified immune molecules and pathways that are directly relevant to the human host defence

Human genetic susceptibility to infectious disease

Antibacterial approaches in tissue engineering using metal ions and nanoparticles: From mechanisms to applications

Toll-like receptors (TLRs) are an important family of receptors that constitute the first line of defense system against microbes. They can recognize both invading pathogens and endogenous danger molecules released from dying cells and damaged tissues and play a key role in linking innate and adaptive immunity. TLRs are widely distributed in both immune and other body cells. The expressions and locations of TLRs are regulated in response to specific molecules derived from pathogens or damaged host cells. The binding of ligands to TLR activates specific intracellular signaling cascades that initiate host defense reactions. Such binding is ligand-dependent and cell type-dependent and leads to production of pro-inflammatory cytokines and type 1 interferon. TLR-dependent signaling pathways are tightly increased during innate immune responses by a variety of negative regulators. Overactivation of TLRs can ultimately lead to disruption of immune homeostasis and thus increase the risk for inflammatory diseases and autoimmune disorders. Antagonists/inhibitors targeting the TLR signaling pathways have emerged as novel therapeutics to treat these diseases. The present review summarizes the structure, characterizations, and signaling of TLRs and their regulators, as well as describes the implication of TLRs in many diseases with a brief idea about the inhibitors that target TLR signaling pathways. We conclude that TLRs are the main elements of our immune system, and they should be maintained functioning to keep the integrity of innate immunity. Targeting of TLR signaling represents a new challenge for treatment of many diseases.

/pdf/toll-like-receptors-activation-signaling-and-targeting-an-4sv7d34hcw.pdf

Toll-like receptors activation, signaling, and targeting: an overview

Genetic factors, as well as environmental factors, play a role in development of nasopharyngeal carcinoma (NPC). A number of single nucleotide polymorphisms (SNPs) have been reported to be associated with NPC. To confirm these genetic associations with NPC, two independent case-control studies from Southern China comprising 1166 NPC cases and 2340 controls were conducted. Seven SNPs in ITGA9 at 3p21.3 and 9 SNPs within the 6p21.3 HLA region were genotyped. To explore the potential clinical application of these genetic markers in NPC, we further evaluate the predictive/diagnostic role of significant SNPs by calculating the area under the curve (AUC). Results. The reported associations between ITGA9 variants and NPC were not replicated. Multiple loci of GABBR1, HLA-F, HLA-A, and HCG9 were statistically significant in both cohorts (Pcombined range from 5.96 × 10−17 to 0.02). We show for the first time that these factors influence NPC development independent of environmental risk factors. This study also indicated that the SNP alone cannot serve as a predictive/diagnostic marker for NPC. Integrating the most significant SNP with IgA antibodies status to EBV, which is presently used as screening/diagnostic marker for NPC in Chinese populations, did not improve the AUC estimate for diagnosis of NPC.

/pdf/evaluation-and-integration-of-genetic-signature-for-5228w9el7n.pdf

Evaluation and Integration of Genetic Signature for Prediction Risk of Nasopharyngeal Carcinoma in Southern China

Background
Southern China is a major area for endemic nasopharyngeal carcinoma (NPC). Genetic factors as well as environmental factors play a role in development of NPC. To investigate the roles of previously described carcinogen metabolism gene variants for NPC susceptibility in a Han Chinese population, we conducted a case-control study in two independent study population groups afflicted with NPC in Guangdong and Guangxi Provinces of southern China.

/pdf/genetic-polymorphisms-of-cyp2e1-gstp1-nqo1-and-mpo-and-the-3dtn9c1qn1.pdf

Genetic Polymorphisms of CYP2E1, GSTP1, NQO1 and MPO and the Risk of Nasopharyngeal Carcinoma in a Han Chinese Population of Southern China

https://aasldpubs.onlinelibrary.wiley.com/doi/pdf/10.1002/hep.24048

Strong influence of human leukocyte antigen (HLA)‐DP gene variants on development of persistent chronic hepatitis B virus carriers in the Han Chinese population

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ji Li

Author Tools

Chat about Author

Papers

Evaluation and Integration of Genetic Signature for Prediction Risk of Nasopharyngeal Carcinoma in Southern China

Genetic Polymorphisms of CYP2E1, GSTP1, NQO1 and MPO and the Risk of Nasopharyngeal Carcinoma in a Han Chinese Population of Southern China

Strong influence of human leukocyte antigen (HLA)‐DP gene variants on development of persistent chronic hepatitis B virus carriers in the Han Chinese population