Biomedical data mining: AI/ML tools and startups (2nd part)
AI transforming dissemination of biomedical science
This newsletter is an overview of AI Startups/Tools dedicated to biomedical data mining.
For the first part (🆓) of this newsletter: 👉 Biomedical data mining: AI/ML tools and startups (1st part).
AI-Based Literature Review Tools
Artificial Intelligence Review Assistant (AIRA) is a platform to support editors, reviewers and authors to evaluate the quality of manuscripts and to help meet global demand for high-quality, objective peer-review in publishing.
Penelope is an online tool that automatically checks whether scientific manuscripts meet journal requirements (such as references and the structure of a manuscript).
StatReviewer is an automated reviewer of statistical errors and reports integrity for scientific manuscripts.
SEMANTIC SCHOLAR is a free AI-powered research tool for scientific literature, for the topics of computer science, geoscience and neuroscience.
Elicit uses language models to help you automate research workflows, like summarizing papers, extracting data and synthesizing your findings.
Consensus is a search engine that uses AI to find insights in research papers, and now is using data annotations from Centaur Labs, a medical data labeling company for 📌Text (Unstructured clinical notes, Scientific text, Chatbots and more), 📌Audio (Heart auscultation, Lung auscultation, Artery auscultation and more), 📌Images (Ultrasound, External images, X-ray and more), 📌Video (Surgery, Clinical sessions and more) and 📌Wave (EEG, ECG and more). Centaur Labs has raised a total of $15.9M.
Scite is an award-winning platform for discovering and evaluating scientific articles via Smart Citations that allow users to see how a publication has been cited, by providing the context of the citation and a classification describing whether it provides supporting or contrasting evidence for the cited claim.
Scite allows researchers to assess the dependability in any particular context of references. It helps in evaluating the quality and impact of the research. It also provides better visualizations and metrics to understand the citation landscape of a particular paper or a topic, and
SciSpace is using AI to simplify the publication of research, for submitting, evaluating and publishing manuscripts.
For more:
5 Top AI Tools That Can Accelerate Literature Reviews for Research
Big Data & Analytics Tools
Off course, data mining ⛏️ and knowledge extraction from biomedical data
➡️ is not only about Peer-Reviewed Literature and the relevant:
💡Omics
proteomics, transcriptomics, genomics, metabolomics, lipidomics and epigenomics—which correspond to global analyses of proteins, RNA, genes, metabolites, lipids and methylated DNA or modified histone proteins in chromosomes, respectively—phenomics—an emerging transdiscipline, defined as the changes seen in an organism resulting in variations in the phenotype during the life span of the organism—and metagenomics—the study of the structure and function of entire nucleotide sequences isolated and analyzed from all the organisms (typically microbes) in a bulk sample.
For example, a branch of transcriptomics is concerned with the sequencing and analysis of the transcriptome (mRNA, rRNA, tRNA and other non-coding RNA). In 2021, LatchBio emerged from stealth mode providing almost code-free biocomputing solutions on the cloud that can be accessed from anywhere via a browser to simplify biological data analysis. Using their platform, researchers can upload files and access dozens of bioinformatics pipelines and data visualisation tools from analysing RNA sequencing data to designing CRISPR edits and even running the AlphaFold software just from their laptop. LatchBio has raised a total of $33.2M.
Another example, comes from a branch of genomics concerned with the sequencing and analysis of the genome of an individual. Genialis is developing next-generation patient classifiers using ML and high-throughput omics data and they offer: Genialis ResponderID, a biomarker discovery platform, and the Genialis Expressions software, that enables ML driven biomarker discovery by aggregating consistently analyzed and annotated data. The Genialis Expressions software is built on FAIR (findability, accessibility, interoperability and reusability) data management principles, in order to analyze sequencing data across numerous NGS platforms. Genialis has raised a total of. $15.5M.
Aimed Analytics in Germany provides big data, analytics and ML solutions to analyze medical data, by analyzing multiple data sources such as transcriptomics, epigenomics, proteomics and multi-omics data to provide research analytics for drug development. For example, during proteomics analysis focusing on flow-cytometry, mass cytometry, imaging mass cytometry, mass spectrometry and mass spectrometry, they offer the following modules: Dimensionality reduction, Clustering, Marker molecule identification, Cell-type annotation, Perturbation analysis, Differential expression analysis, Transcriptional regulator prediction, Integration of clinical data, Patient classification and many more.
Pharmacogenomics (sometimes called pharmacogenetics) is a field of research that studies how a person's genes affect how he or she responds to medications. Biolytica in Switzerland combines genomics, pharmacogenomics, epigenetics, biomarkers, wearable data, lifestyle data and nutritional data using the NEXUS platform, while Biolytica’s AIME platform analyzes the data to offer personalized longevity plans. For this, the startup analyzes patient health records and genetic signatures using big data analytics. Biolytica has raised a total funding of $5.87M over 1 round.
BioSymetrics is a phenomics-driven drug discovery company that integrates clinical and experimental data using ML creating a phenograph to navigate from phenotypes to genes and to druggable targets. on October 2022, BioSymetrics and Deerfield Management—a healthcare investment firm—announced a five-year joint venture to accelerate the advancement of new therapeutics, with an initial focus on cardiovascular and neurological diseases.
After omics another important dataset comes from imaging.