Background Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information and biological knowledge from pathology slides and genomic data in cancer. In addition, a number of recent studies have introduced multimodal DL models designed to simultaneously process both images from pathology slides and genomic data as inputs. By comparing patterns from one data modality with those in another, multimodal DL models are capable of achieving higher performance compared to their unimodal counterparts. However, the application of these methodologies across various tumor entities and clinical scenarios lacks consistency. Methods Here, we present a systematic survey of the academic literature from 2010 to November 2023, aiming to quantify the application of DL for pathology, genomics, and the combined use of both data types. After filte

Background Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information and biological knowledge from pathology slides and genomic data in cancer. In addition, a number of recent studies have introduced multimodal DL models designed to simultaneously process both images from pathology slides and genomic data as inputs. By comparing patterns from one data modality with those in another, multimodal DL models are capable of achieving higher performance compared to their unimodal counterparts. However, the application of these methodologies across various tumor entities and clinical scenarios lacks consistency. Methods Here, we present a systematic survey of the academic literature from 2010 to November 2023, aiming to quantify the application of DL for pathology, genomics, and the combined use of both data types. After filte

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder which is characterized by the loss of both upper and lower motor neurons in the central nervous system. In a significant fraction of ALS cases – irrespective of family history- a genetic background may be identified. The genetic background of ALS shows a high variability from one ethnicity to another. The most frequent genetic cause of ALS is the repeat expansion of the C9orf72 gene. With the emergence of next-generation sequencing techniques and copy number alteration calling tools the focus in ALS genetics has shifted from disease causing genes and mutations towards genetic susceptibility and risk factors.In this review we aimed to summarize the most widely recognized and studied ALS linked repeat expansions and copy number variations other than the hexanucleotide repeat expansion in the C9orf72 gene. We compare and contrast their involvement and phenotype modifying roles in ALS among different populations.