We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions. For fusing cues, we explore decision and feature-level fusion, and an additive attention-based fusion strategy which quantifies the relative importance of the three modalities for trait prediction. Examining various long-short term memory (LSTM) architectures for classification and regression on the MIT Interview and First Impressions Candidate Screening (FICS) datasets, we note that: (1) Multimodal approaches outperform unimodal counterparts; (2) Efficient trait predictions and plausible explanations are achieved with both unimodal and multimodal approaches, and (3) Following the thin-slice approach, effective trait prediction is achieved even from two-second behavioral snippets.

Deep learning is widely known for its flexibility and the capability to uncover complex patterns in large datasets; with these advantages, instances of deep learning in the genomics domain are emerging. One such application is genomic prediction, where the traits of individuals -- like susceptibility to disease or yield-related traits -- are predicted using their genomic information. Understanding the correlation of the genetic traits and variations in genomes could have many benefits such as advancing crop breeding processes, and hence improve food security. In this article, we explore how genetic relationships can be exploited alongside genomic information to predict genetic traits, with the aid of graph machine learning algorithms. In genomic prediction, traditional deep learning would use an individual's genomic information -- like a single nucleotide polymorphism (SNP) -- as input features to the neural network. A SNP is essentially a difference that occurs at a specific position in an individual's genome.

Researchers from Human Longevity, Inc. (HLI) have published a study in which individual faces and other physical traits were predicted using whole genome sequencing data and machine learning. This work, from lead author Christoph Lippert, Ph.D. and senior author J. Craig Venter, Ph.D., was published in the journal Proceedings of the National Academy of Sciences (PNAS). The authors believe that, while the study offers novel approaches for forensics, the work has serious implications for data privacy, deidentification and adequately informed consent. The team concludes that much more public deliberation is needed as more and more genomes are generated and placed in public databases. For the IRB approved study, 1,061 ethnically diverse people ranging in age from 18 to 82 participated by having their genomes sequenced to an average depth of at least 30x.