Finding a needle in a haystack: virtual screening, an artificial intelligence approach to drug discovery

Artificial intelligence (AI) is a technology well suited for dealing with large amounts of information and has long been considered a promising tool in drug discovery. It has made steady in roads in the field in the last decade by improving the probability of success and bringing greater precision, speed, and cost-efficiency to a process…

A machine learning (ML) approach for identifying genetic biomarkers and new molecular targets associated with impaired survival of breast cancer patients

BACKGROUND Machine learning (ML) tools are suitable to dive vast amounts of clinical and genetic information in order to identify genetic biomarkers of worse survival and potential new molecular targets. This study has investigated the ability of one such tool to identify genetic biomarkers associated with higher risk of mortality in breast cancer, biomarkers that…

Prospective evaluation of SkinGuard, a deep algorithmic framework for the classification of neoplastic skin lesions

Introduction. Diagnostic errors between nevic lesions and skin tumors are frequent for non-specialist physicians. Therefore, there is a need to create a simple and practical tool based on artificial intelligence to assists them in distinguishing potentially malignant lesions from benign ones, improving early detection of skin cancer. Objective. To evaluate the performance of SkinGuard, an…

Harnessing the power of AI and HTE, cloud lab and automation data to transform drug discovery

Drug discovery is a lengthy and costly process affected by a high attrition rate. The revolution in experimental sciences brought about by HTE, cloud labs and lab automation should represent an excellent opportunity to completely transform drug discovery and overcome the serious problems that hinder the process. But this transformation will not be possible without…

El arte de la medicina hipocrática y la IA: un binomio al servicio del paciente

En un mundo en constante cambio y evolución como el nuestro, pocos métodos o doctrinas pueden tener el honor de permanecer vigentes durante más de 2.000 años. Uno de estos excepcionales casos es el método hipocrático, gracias al cual la medicina abandonó la superstición y la magia ejercidas por sacerdotes-médicos en aquella época y se…

Globant and Topazium Announce Partnership to Deliver AI-Powered End-to-End Solutions to the Life Sciences Industry

Globant and Topazium join to create new paradigms in drug discovery, clinical research, diagnostics, and well-being MADRID – September 27, 2022 – Globant (NYSE: GLOB), a digitally native company focused on reinventing businesses through innovative technology solutions, and Topazium, the biotech startup specialized in integrating the latest advances in artificial intelligence (AI) with traditional medical…

Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19

Background: To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection. Methods: We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means…

Detecting panitumumab/FOLFOX responders in K-Ras wild-type metastatic colorectal cancer through an artificial intelligence-based analytical tool

Background: Patients with K-RAS wild-type (WT) metastatic colorectal cancer (mCRC) are currently considered the optimal candidates for upfront treatment with combinations of chemotherapy and EGFR inhibitors. These combinations significantly extend overall survival (OS) compared to chemotherapy alone. However, a proportion of patients would not achieve this goal. This study has investigated the ability of an…

Use of deep learning frameworks to detect super-responder and super-survivor stage IV squamous non-small-cell lung cancer (NSCLC) patients treated with a gemcitabine and cisplatin combination

Background: Synthetic fingerprints integrate clinical data within computational models allowing the identification of particular clinical subpopulations at a given moment. We here describe a deep learning strategy to detect super-responder and super-survivor patients with squamous NSCLC by setting up synthetic fingerprints and using unsupervised deep learning frameworks (UDLF). Methods: Through www.projectdatasphere.org, we accessed the control…